顾客旅程视角下品牌线上营销优化研究

As firms gather increasing amounts of data, the question of how future marketers can use these data to make their marketing more relevant and to make a strategic difference remains. However, students may feel uncomfortable about information systems and database technology, which they may perceive as complex and dry. This case describes how the development of a course on data-driven marketing makes use of service design methods. The experiential learning innovation is based on the optimization of customer journey mapping, which encompasses theoretical marketing concepts, modern database architecture and practical digital marketing knowledge. As a visualization of individual interactions with a product, service, or brand, customer journey mapping helps explain the way an interaction occurs in one moment and how it influences all other moments. By taking the “data as a service” perspective on the customer journey, students benefit from a more innovative and creative approach to data-driven marketing, which helps improve their attention and motivation.

Traditional attribution models struggle to accurately represent the intricate, multi-touchpoint customer journeys typ- ical of contemporary influencer marketing campaigns, especially in the Fast-Moving Consumer Goods (FMCG) sector, where brief purchase cycles and impulsive buying behaviors present distinct measurement difficulties. Three major problems with current methods are: they can’t tell the difference between real campaign impact and random correlations, they don’t handle cross-platform customer journey fragmentation well, and they don’t find a balance between measuring short-term sales and building long-term brand equity. This research introduces a comprehensive five-layer artificial intelligence architecture that integrates Long Short-Term Memory (LSTM) neural networks with attention mechanisms for sequential customer journey modeling, causal inference engines for distinguishing genuine campaign effects from external factors, and multi-objective optimization algorithms that concurrently maximize return on investment while maintaining brand-building objectives. The suggested method combines real-time data from many sources, such as social media APIs, e-commerce transaction logs, brand perception surveys, and competitive intelligence systems, with advanced machine learning processing layers that use computer vision and natural language processing to analyze content per- formance, graph neural networks to group influencers, and real-time scoring engines and budget allocation logic to make decisions automatically. Validation via synthetic control methods and counterfactual analysis guarantees measurement precision while mitigating the endogeneity bias seen in conventional attri- bution methodologies. The architecture offers substantial benefits over traditional models by supplying detailed, touchpoint-level attribution insights with temporal dependency modeling, facilitat- ing automated campaign optimization through real-time budget reallocation based on performance thresholds, and merging quantitative conversion metrics with qualitative brand equity indicators. This all-encompassing method fills the important gap between academic attribution theory and the needs of real-world FMCG marketing, providing a scalable framework for optimizing influencer marketing based on evidence that balances short-term performance with long-term brand building goals.

The usual marketing funnel no longer safely reflects consumer behaviour in the modern era. Customers prefer to participate in the Consumer Decision Journey (CDJ), a more repetitive and straight- ahead decision- making method. This analysis examines how businesses can impact buyers from when a customer becomes aware of a product until the purchase. As a result of the expansion of digital marketing, strategies like social media marketing, personalized advertising, search engine optimization (SEO), influencer marketing, and user-generated content (UGC) have gained more and more attention. This research uses the CDJ of McKinsey to observe how businesses can improve digital marketing to improve user experience and build brand loyalty. Moreover, it examines the impact of word- of- teeth marketing and online reviews on developing consumer trust and offers suggestions for controlling myths. But this review advises businesses to improve their digital marketing strategies, improve customer relationships, and get a competitive advantage in an increasing market.

This paper aims to discuss the application of AI in digital marketing, its role in enhancing decision-making, personalization, and campaign performance along the customer's journey. This study employs a qualitative research approach to investigate the strategic application of AI in digital marketing, drawing on recent academic literature, industry reports, and case studies. Thematic coding was applied to identify key patterns and emerging themes such as predictive analytics, customer personalization, and performance optimization. The key applications are big data analytics, content personalization, omnichannel integration, automated content generation, and dynamic customer interaction. The evidence suggests that the strategic use of AI not only enhances customer satisfaction and conversion rates but also improves operating efficiency and fosters long-term brand loyalty. As digital ecosystems continue to evolve, AI emerges as a key driver of innovation and digital marketing competitiveness. The study leverages existing academic and practical knowledge to describe how AI equips organizations with the capacity to manage enormous datasets, automate marketing functions, and deliver hyper-personalized experiences.

This study examines consumer behavior patterns through comprehensive search pattern analysis across three major e-commerce platforms. The research analyzed 127,543 search sessions from 12,847 unique users over six months using Latent Dirichlet Allocation (LDA) and K-means clustering. Data collection involved clickstream analysis, query pattern extraction, and behavioral tracking across mobile, desktop, and tablet devices. Statistical methods included hierarchical linear modeling, ANOVA, and chi-square tests. The analysis identified five distinct consumer segments: Exploratory Browsers (32.4%), Systematic Researchers (23.8%), Direct Purchasers (18.7%), Deal Seekers (15.3%), and Uncertain Seekers (9.8%). Results reveal significant behavioral variations across customer journey stages, with query length increasing from 2.84 to 4.89 words and brand mentions rising from 15.2% to 71.3% from awareness to retention stages. Mobile devices dominated usage (63.4%), with distinct behavioral patterns across demographics and temporal factors. These findings enable businesses to develop targeted marketing strategies, optimize user experience design, and implement personalized recommendation systems. This research contributes original insights by integrating quantitative behavioral analytics with qualitative thematic analysis, providing a comprehensive framework for understanding digital consumer decision-making processes in contemporary e-commerce environments.

No abstract available

The development of digital platforms in the mental health sector has shown significant growth, along with increasing awareness among younger generations regarding the importance of self-reflection and psychological well-being. One type of service with the potential to address these needs is digital journaling platforms. However, at the pre-launch stage, the main challenge faced by digital platforms lies not only in feature design, but also in building brand awareness from the outset through digital marketing strategies that are relevant to the needs of potential users.This study aims to design a digital marketing strategy based on consumer insight, customer journey, and value proposition at the pre-launch stage of the PelanPelan digital journaling platform. The study adopts a descriptive qualitative approach involving potential users from Generation Z aged 20–28 years. Data were collected through semi-structured interviews and open-ended surveys. The data were analyzed using thematic analysis to identify consumer insights, construct a conceptual customer journey, and formulate a value proposition as the foundation of the digital marketing strategy.The findings indicate that potential users perceive journaling as a tool for emotional regulation and self-reflection, yet still experience adoption barriers at the awareness stage. Based on these findings, pre-launch digital marketing strategies should focus on empathetic, simple, and emotionally driven communication to build contextual brand awareness.

In today’s digital world, delivering personalized customer experiences is paramount for businesses that are aiming to foster engagement and drive conversions. However, many organizations grapple with challenges such as data inconsistencies and outdated technologies. A report by Contentful highlights that 57% of senior marketing executives struggle with data inconsistencies when personalizing customer experiences, and only 24% of firms effectively invest in omnichannel personalization due to departmental silos and outdated technology1. Reinforcement Learning (RL), a subset of machine learning, offers a promising solution to these challenges by enabling systems to learn optimal strategies through trial and error interactions with the environment. In the context of marketing, RL can dynamically adapt customer journeys in real-time, optimizing for long-term customer value rather than short-term metrics . Salesforce Marketing Cloud serves as a solid platform for implementing RL-driven strategies, offering tools like Journey Builder and Audience Studio that facilitate the orchestration of personalized customer experiences across multiple channels . This whitepaper aims to explore the integration of Reinforcement Learning into Salesforce Marketing Cloud for dynamic customer journey optimization. It will explore the challenges of current personalization methods, elucidate the principles of RL, and provide guidance on implementing RL strategies within the Salesforce ecosystem to enhance customer engagement and business outcomes.

Bobrovnyk V.M., Andrushkevych Z.M. MICRO-MOMENTS IN THE DIGITAL TRANSFORMATION OF MARKETING: IMPACT ON THE CUSTOMER JOURNEY IN DIFFERENT BUSINESS MODELS Purpose. The aim of the article is to explore the practical aspects of the impact of short-term but meaningful interactions between a consumer and a brand in the digital environment, assess their role in shaping the customer journey in different business models, and generalize tools for measuring the effectiveness of such interactions in the context of modern marketing. Methodology of research. The study is based on an interdisciplinary approach that combines theoretical and practical aspects of analysing consumer behaviour in the digital environment in the context of the categories of “need” and “value”, and adapting marketing tools to the conditions of digital transformation. General scientific and specialized methods were used in the course of the research, which made it possible to comprehensively investigate micro-moments as a key factor in shaping the customer journey. To gain an in-depth understanding of current trends in the digital transformation of marketing, a review of current research was conducted, which allowed us to identify the most common practices of using micro-moments in marketing, identify gaps in theoretical and practical approaches to adapting marketing tools in the surrounding of digital change, and identify key aspects of using micro-moments in different contexts, including B2C, B2B, D2C, and C2C business models. This approach has provided a comprehensive understanding of the role of micro-moments in the modern marketing process, and allowed us to develop practical recommendations for brands on how to integrate micro-moments into their strategies in the context of digital marketing transformation. Findings. It has been proven that micro-moments are an important factor in shaping new brand interaction strategies with customers, and are formed at different stages of the customer journey. The key types of micro-moments and their connection with different stages of consumer decision-making were identified, which allows to accurately target marketing efforts at each stage of interaction. It was found that for the successful implementation of micro-moments in marketing strategies, it is necessary to better understand the needs and values of consumers, as well as to form a system of rapid brand response to changes in customer behaviour. The key aspects of the use of micro-moments in marketing strategies were identified. The key areas of measuring the effectiveness of interactions in different business models (B2C, B2B, D2C, C2C) were formed. Originality. The use of micro-moments in the context of digital marketing transformation and assessment of their impact on the formation of new consumer behaviour patterns characterized by nonlinearity and multi-vector nature was further developed. The practices of adapting marketing tools to the digital environment have been improved, allowing identifying micro-moments and using them to attract and retain customers in various business models. The main directions of influence of micro-moments on the formation of a personalized customer experience were substantiated, which, unlike the existing ones, determined their relationship with different stages of the customer journey in the context of four types of micro-moments and the effectiveness of interactions in different business models. Practical value. The results of the study contribute to improving communication processes with customers, optimizing the use of marketing resources, as well as creating competitive advantages and unique value propositions in various business models. The study can become an important resource for marketers, analysts, and digital communications professionals seeking to better understand consumer behaviour in the digital environment and effectively adapt marketing strategies to the conditions of digital transformation. It will also be useful for business managers and business owners interested in implementing innovative approaches to attract and retain customers, in particular through personalization of customer experience. Key words: micro-moments, digital transformation, customer journey, digital marketing, customer experience, personalization, business models, marketing strategies.

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.

The article investigates the adoption of real-time customer journey orchestration using Microsoft Power Platform CRM with minimal coding. It looks at the enhancements to Dynamics 365 Marketing and Customer Insights made in 2025 and shows how these have empowered marketers to implement AI-driven personalization at scale. Key components of the research include a technical architecture that integrates Dynamics 365 Customer Insights, Power Automate Flows, AI Builder Models, Dataverse, and Power Apps Canvas Applications. A four-phase implementation methodology provides a structured approach to deploying customer journey orchestration through clearly demarcated focus areas in data foundation, journey design, trigger implementation, and enhancing with AI. Those discussed above are several of the key technological challenges: state management, performance optimization, and integration complexity; this paper outlines relevant solutions. Ethical considerations focus on privacy compliance, algorithmic transparency, and personalization boundaries. The article concludes by providing future directions; conversational journey orchestration, extended reality integration, and edge computing capabilities provide direction for organizations seeking sophisticated approaches toward engaging their customers in an increasingly competitive landscape.

Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture latent behavioral patterns and adapt to multi-channel dynamics. These models often struggle to integrate unstructured data sources, failing to provide adaptive, personalized insights. To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. This research highlights the practical value of integrating structured and unstructured data for improving customer analytics. Our framework leverages LDA to extract behavioral patterns from customer interactions, enriching predictive models and enhancing real-time decision-making in financial services. Robustness checks confirm the scalability and adaptability of this approach, offering a data-driven strategy for long-term value optimization in dynamic digital ecosystems.

The rise in private primary care necessitates clinic entrepreneurs to innovate and improve clinic performance through effective marketing, particularly through social media platforms like Instagram. The digital era presents challenges due to the simultaneous presence of five generations: baby boomers, generation X, generation Y, generation Z, and generation Alpha, each with unique attitudes, preferences, and behaviors. This research aims to explore the effectiveness of Instagram as a clinic marketing platform across generations in a private clinic. Method: A cross-sectional design was conducted in this research. Data was collected using a consecutive sampling method among the Instagram clinic’s followers (n = 377). The collected data is then analyzed using the percentage value of customer path 5A attributes, conversion rate, purchase action ratio (PAR), brand advocacy ratio (BAR), and one-way ANOVA. The percentage value of customer path 5A attributes showed a good result in four generations, including the conversion rate scores. PAR and BAR scores have optimal results in four generations. There was a significant difference in 5A attributes, PAR, and BAR (p<0.05). Instagram is an effective social media platform for clinical marketing for every generation. This study contributes to reassuring clinic entrepreneurs to utilize Instagram as their marketing platform.

This study investigates the effectiveness of digital marketing strategies employed by Nogi Livin, an emerging Indonesian furniture company, in enhancing brand awareness and influencing customer decision journeys. The global furniture market’s expansion is leveraged by digitalization and changing consumer preferences, presenting challenges and opportunities for companies like Nogi Livin. Using the McKinsey Consumer Decision Journey Model, this research delineates the impact of social media strategies on customer engagement and purchasing decisions. A mixed-method approach, including quantitative data from online surveys and qualitative insights, was employed. Structural Equation Modeling (SEM) was used to measure the relationship between online presence and brand awareness, and subsequently, the influence of brand awareness on customer engagement. Findings indicate a significant positive correlation between strategic online engagements and enhanced brand awareness, which strongly links to increased customer engagement and positively affects the consumer decision journey. This research contributes to theoretical and practical understandings of digital marketing’s impact in the furniture industry.

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates or optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their KPIs.

Purpose This study aims to investigate the impact of advertising, packaging and packaging information on brand performance throughout the customer journey of a new brand. Design/methodology/approach A real new brand was assessed through a controlled three-stage study involving 100 participants. Data included eye-tracking, behavioral observations and self-reported measures. Tasks involved watching an ad (prepurchase), shopping in simulated supermarkets (purchase) and subsequent evaluations of the product in home settings (postpurchase). The framework integrates schema theory and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), and the Touchpoints Context Qualities (TCQ) framework. Findings Advertising in the prepurchase stage drove visual attention to the packaging in the purchase stage, with 2.5 times more attention than chance in virtual reality and e-commerce shopping tasks. A notable proportion of consumers purchased the novel product, indicating successful engagement despite competition. Multiple touchpoints enhanced brand recall and recognition and increased brand trust across the customer journey. Consumers were curious about the new brand, but the QR code on the packaging was largely ignored. Originality/value The study uniquely combines three stages of the customer journey with multimodal data, highlighting the role of integrated marketing communication (IMC) strategies. It demonstrates how advertising primes attention and curiosity, influencing consumer behavior and trust in new brands. The findings underline the importance of aligning brand communication across touchpoints to drive positive brand outcomes.

This study aims to examine the impact of Search Engine Optimization (SEO) and content marketing on brand awareness, with customer engagement serving as a mediating variable, focusing on the IQOS brand in Indonesia. SEO and content marketing are two integral components of digital marketing, both designed to enhance brand visibility and foster consumer interaction. Data for this study were collected digitally through questionnaires distributed to active IQOS consumers who participate in the digital space and represent specific demographic segments. The relationships between variables were analyzed in two stages using Structural Equation Modeling with Partial Least Squares (SEM-PLS), both for path analysis and to measure the direct and indirect effects among variables. The findings indicate a positive and significant influence of both SEO and content marketing on customer engagement, which in turn significantly contributes to brand awareness. These results reinforce the importance of optimizing content through SEO to increase market visibility and brand recognition for IQOS. Practically, the findings suggest that SEO and content marketing should be strategically integrated into digital marketing efforts to maximize their effectiveness.   Keywords: Search Engine Optimization (SEO), Content Marketing, Brand Awareness

The customer journey towards purchase has received substantial attention from businesses and academics, especially in relationship marketing and customer engagement. This interest has intensified due to the rise of social media, enabling customer-to-customer interactions alongside conventional marketing strategies. Customers increasingly seek information independently in the life insurance sector, and comprehending the decision-making process is critical. In Indonesia, low insurance literacy and inclusion add to the challenges of the traditional, face-to-face sales process. This research empirically investigates the impact of social media and traditional marketing communications on customer-to-customer interactions and subsequent customer decision-making. Brand preference is examined as a mediating variable. Quantitative methods, including online questionnaires and expert interviews, were employed in Greater Jakarta, Indonesia, targeting non-life insurance customers. Structural equation modeling (SEM) via LISREL was utilized to analyze data from 310 respondents. Results indicate that while social media communications have an insignificant impact on customer-to-customer interactions (t-value 1.15), traditional marketing communications positively influence customer-to-customer interactions (t-value 11.87). Customer-to-customer interactions also found to positively influence brand preference (t-value 10.14) while brand preference positively influences customer decision-making (t-value 3.86) Additionally, customer-to-customer interactions positively affect customer decision-making (t-value 3.54), with brand preference mediating this relationship (t-value 3.85).

The rapid shift toward social commerce and omnichannel retailing has reshaped how consumers discover, evaluate, and purchase silver products, particularly 925 silver jewelry and gift-oriented accessories. Uncertainty related to authenticity, finishing quality, sizing, and maintenance increases perceived risk and makes trust a critical mechanism for conversion and loyalty. Objective: This study aims to formulate and empirically test an integrated marketing strategy for silver sales by (1) mapping the customer journey, (2) identifying key drivers of trust formation in social commerce, and (3) examining how trust translates into purchase and repurchase intentions under conditions of perceived price fairness. Methodology: A quantitative explanatory design was applied using an online survey of 200 valid respondents who had interacted with or purchased from a Jakarta-based silver brand (SilverLine Jewelry) through Instagram/TikTok, Shopee, and WhatsApp Business. Data were analyzed using SEM-PLS to evaluate measurement quality and test relationships among information quality, seller communication, eWOM/UGC exposure, live streaming experience, transaction security perception, trust, purchase intention, repurchase intention, and perceived price fairness. Findings: The silver customer journey is video-led at awareness (TikTok and Instagram dominate discovery), trust-led at consideration (reviews/UGC and direct chat are pivotal), and marketplace-led at conversion (marketplace checkout is most preferred), with after-sales requests forming a meaningful post-purchase touchpoint. Trust is strongly explained by controllable social commerce factors, with seller communication and information quality as the most influential drivers, followed by transaction security, while eWOM/UGC and live streaming provide significant incremental effects. Trust is the strongest predictor of purchase intention; purchase intention predicts repurchase intention; and trust also has a smaller direct effect on repurchase, indicating partial mediation. Perceived price fairness strengthens the effect of trust on purchase intention. Implications: The findings support a “trust-bundle” strategy combining transparent product specifications, responsive interaction, secure checkout options, structured social proof (UGC/reviews), live demonstrations, and standardized after-sales services (polishing/resize), reinforced by consistent fairness-oriented value communication. Originality: This research operationalizes the customer journey into a silver-specific configuration and integrates trust formation, conversion, loyalty, and price fairness within a single tested model, offering category-tailored guidance beyond generic social commerce studies.

Digitalization and rising demand for branded content have reshaped Indonesia’s creative industry over the past decade. The post-pandemic shift toward digital content, social media marketing, and integrated creative solutions has pushed agencies to innovate continuously. Competition has grown not only among established agencies but also with the rise of large-scale freelance collectives, making client loyalty a critical differentiator. Agencies now need to prioritize long-term, meaningful relationships rather than focusing solely on acquiring new clients. Distrik Berisik, a Jakarta-based creative agency founded in 2021, operates within this dynamic environment and serves Indonesia’s youth market with end-to-end creative solutions. Although customer acquisition is strong, the agency faces retention challenges, maintaining only a 30–40% retention rate. At the same time, many business leaders believe overly digital approaches often fail to align with real customer needs. This study addresses these issues by optimizing the customer journey through a Design Thinking approach. The research is guided by three questions: mapping the current customer journey from the first interaction to post-program stages; identifying key pain points and opportunities; and developing an improved journey model to strengthen customer experience and loyalty. A qualitative method is used, with Design Thinking’s five stages—Empathize, Define, Ideate, Prototype, and Test—serving as the main framework. Data were collected through questionnaires and analyzed using Thematic and Descriptive Analysis to reveal patterns, satisfaction levels, retention potential, and repurchase intentions. Combining the concepts of Customer Journey, Customer Loyalty, and Design Thinking, the study proposes a practical, human-centered customer journey model tailored to the creative industry.

This study aims to provide a comprehensive understanding of the needs, preferences, and behaviors of its target market in the Indonesian full-service restaurant (FSR) sector. Firstly, it seeks to map the customer journey of FSRs in the food supplies purchasing process, identifying key touchpoints, brand awareness levels, information sources, and factors influencing brand choice. Secondly, it evaluates the effectiveness of various digital marketing channels, such as social media advertising, search engine marketing, and email marketing, by analyzing cost-per-lead and conversion rates to identify the most efficient channels for acquiring new FSR customers for PT MBI. Finally, the research intends to develop a data-driven digital marketing strategy that optimizes customer interactions, enhance brand visibility, and improve lead generation at each decision-making stage to drive sales conversions and increase customer lifetime value. The results of this study show that the current focus of PTXYZin the FSR segment lies between two segments: Relationship-Driven Traditionalists and Hybrid Decision-Makers.

In the highly competitive automotive industry, understanding the factors infl uencing customer satisfaction is crucial for long-term success. This study addresses the gap in research by comprehensively examining the combined eff ects of Brand Experience, Brand Identifi cation, and Eff ective Customer Journey on customer satisfaction. Using a structural equation modeling method with a partial least squares approach, data were collected through surveys involving individuals with experiences using specifi c car brands. The analysis reveals that both Brand Experience and Brand Identifi cation positively infl uence customer satisfaction. Additionally, the Eff ective Customer Journey, which encompasses how customers interact with brands throughout the purchasing and usage process, signifi cantly impacts customer satisfaction. These fi ndings highlight the need for marketing strategies that holistically integrate Brand Experience, Brand Identifi cation, and Eff ective Customer Journey to enhance customer satisfaction in the automotive sector. The study provides valuable insights for brand managers and marketing professionals to better understand and manage the critical factors shaping customer satisfaction.

Current study aimed at examining the influence of conversion rate optimization (CRO) as a digital marketing techniques (A/B Testing, Landing Page Optimization, User Experience (UX) Optimization, Clear Call-to-Action (CTA), Social Proof, Simplified Checkout Process) In enhancing the brand image from perspective of customers in Jordan banks sector. Quantitative methodology was adopted, and a questionnaire was self-administered by (100) customers. Results of study indicated thatconversion rate optimization (CRO)are able to enhancing the brand image with special influence of the variable (Simplified Checkout Process) as it has the ability to influence customer intention to make a purchase decision from a specific website based on the level of purchase process easiness and smoothness. This current study sheds the light on the relationship between conversion rate optimization (CRO) as a digital marketing techniquesand the concept of brand image, in addition to identify the best and most suitable digital process to optimize conversion rate.a

Gamification has evolved as a potent method for engaging and motivating consumers in today's dynamic market scenario. This research report investigates the impact of gamification on customer purchasing intentions to determine its current relevance. This study investigates the subtle links between gamification aspects, user engagement, brand loyalty, and consumer purchasing intentions using Partial Least Squares Structural Equation Modelling (PLS-SEM). Gamification is an important technique in modern marketing approaches because of its capacity to captivate and incentivize people. The survey included 300+ individuals from Durg and Raipur, Chhattisgarh's two major districts, representing a broad demographic. The findings show that Gamification has a large indirect effect on Customer Engagement, which in turn affects Brand Loyalty and eventually shapes Customer Buying Intentions. This study emphasises the critical significance of gamification in altering customer behaviour and the relevance of promoting User Engagement and Brand Loyalty to drive purchasing decisions. Gamification strategy emerges as a powerful force in the contemporary marketing landscape, with the potential to affect the entire customer journey, as firms seek novel methods to connect with consumers.

The research evaluates how artificial intelligence impacts personalized marketing through its three major effects on customer encounter and predictive market segmentation alongside brand audience interaction. Managers use AI to monitor consumer information while automating communication and adding tailored suggestions for consumers which leads to higher marketing results. This research employed a quantitative strategy which obtained data from secondary records involving AI marketing strategy case studies in addition to academic literature and industry reports. The evaluation of how AI helps personalization, engages customers better and optimizes targeting effectiveness occurred through statistical analysis. The analytic power of AI in marketing allows organizations to improve personalization because it examines consumer activities which results in specialized advertisement delivery. The use of AI-powered chatbots with virtual assistants delivers better customer support efficiency thereby producing happier clients who remain active with the system. The major obstacles in AI application involve privacy issues with customer data together with biased algorithms running against human interaction standards. The implementation of AI technologies in marketing operations brought about two key benefits consisting of automated choices along with maximized consumer connections. The presence of ethical problems involving data security as well as transparency remains unresolved. Organizations who apply AI-driven strategies successfully will achieve competitive superiority in their fields. Upcoming studies need to focus on how generative AI produces content as well as ethical standards for AI systems and its organizational usages throughout different sectors. Sustainable AI-designed marketing initiatives require organizations to maintain equal proportions between machine automation and human employee involvement.

This study examines the possibilities of enhancing relationship between external factors and five main steps of the customer journey influence on brand trust. Our aim is to fill a gap of empirical studies on the online channel in Thailand. We identify four external factors that contribute to each step of customer journey base on customer journey map theory. Data collected from 400 respondents was tested against the research model using a partial least squares (PLS) approach. Our hypotheses testing the determinants set of the customer journey with a statistical inferential analysis that, show the results support 7 of the 9 hypotheses, with a significant relationship between analysed constructs (Social influencer, eWom, and Marketing campaign) which are the factors that might contribute to online customer journey at the present.

Recent technological advancements have significantly transformed human life, particularly with the advent of the Fourth Industrial Revolution, which has profoundly influenced the use of the internet for business and economic activities. E-commerce has emerged as a crucial medium for online buying and selling, propelled by these digital advancements. This growth is especially evident in Indonesia, which ranks among the countries with the highest number of internet users globally. This study aims to identify the dominant factors influencing user journey mapping and their impact on the user experience of Tokopedia mobile application users. The research sample comprises 125 users of the Tokopedia application, with data collected through questionnaires distributed via Google Forms. The analysis involves factor analysis and simple linear regression. The findings reveal that the dominant factors influencing user journey mapping are user persona and opportunity. Furthermore, the study demonstrates that user journey mapping positively impacts the user experience for Tokopedia application users. This research underscores the importance of understanding user journey mapping in enhancing the overall user experience, which is crucial for e-commerce platforms like Tokopedia. The insights gained from this study can assist developers and marketers in better tailoring their strategies to improve user engagement and satisfaction. This study provides valuable perspectives on how user journey mapping can be utilized as a strategic tool to optimize user interactions and ensure that each step in the user journey delivers maximum value. Thus, user journey mapping not only enhances individual experiences but also contributes to the overall success of e-commerce platforms in an increasingly competitive market.

No abstract available

PurposeThis research delineates the interdependencies between e-service quality (e-SQ), product quality (PQ) and food biosafety measures (FBM) in shaping consumer satisfaction and loyalty within the online food delivery services (OFDS) landscape. Anchored by the technology acceptance model (TAM) and the theory of planned behavior (TPB), the study integrates these frameworks to examine how perceived service efficiency, reliability, product appeal and biosafety protocols contribute to overall consumer trust and repurchase intentions.Design/methodology/approachSurveys were conducted on several 100 online food delivery app users, ages 20 to 64, in major cities in Colombia, which provided data for structural equation modeling analysis.FindingsThe analysis revealed that reliable, responsive service and appealing food presentation significantly influence consumer perceptions of behind-the-scenes safety protocols during delivery. Strict standards around mitigating contamination risks and verifiable handling at each point further engender trust in the platform and intentions to repurchase among users. The data cement proper food security as pivotal for customer retention.Practical implicationsQuantitatively confirming biosafety’s rising centrality provides an impetus for platforms to integrate and promote integrity, safety and traceability protection as a competitive differentiator.Originality/valueThe study’s originality lies in its comprehensive exploration of the OFDS quality attributes and their direct impact on consumer loyalty. Besides, it offers valuable insights for both academic and practical implications in enhancing service delivery and marketing strategies.

In the contemporary digital marketplace, decoding customer interactions is vital for business advancement. This research applies cutting-edge visualization methodologies to transform complex e-commerce datasets into comprehensible, actionable intelligence. Utilizing dynamic charts, heatmaps, and temporal trend evaluations, the study uncovers patterns that influence user activity and revenue outcomes. The methodology identifies key stages within the customer journey and delivers practical recommendations to enhance user experience and streamline marketing initiatives. Results illustrate that visual analytics represents a powerful instrument for interpreting multifaceted online retail behaviors and supporting data-driven decision-making.

No abstract available

Consumer journey analysis led to efficient marketing implementation. A journey represents a path of steps and interaction between consumer and service units at each touchpoint. Dissatisfaction in the touchpoint, causes a negative effect to retain a customer. Previous studies always constructed the journey maps relied on the narrative approach. According to use Google, consumers always face massive websites to access, which is a pain point in the journey. Improving consumer buying, led to the research aims: identifying consumer needs, and reducing SEO pain-point using content relevance indexing. The data (social media posts from the Thai beauty communities in the year 2020) is analyzed and has found that there are two need types: curative and preventive. The study can segment the 150 websites into four groups which reduce the search space. Moreover, the significant words from the wrapping technique can use to create keywords in the homepage introduction that are matching the products to consumer needs.

We investigate how the perceived quality influences touchpoint performance to provide a more nuanced understanding for analysing customer journeys. To answer the research questions, a survey in a real life online setting was carried out. The setting contained complex service solutions that were offered in a business-to-business context. The quantitative study shows that the perceived quality of a website has an influence on consumers’ buying intention. This correlation increases as the customer journey progresses. The perceived quality influences the website’s impact on visitors’ buying intention with a medium to strong effect size and the influence of a website’s quality on the impact on visitors’ buying intention varies significantly at different customer journey phases. While extant research focusses either on customer experience at touchpoints or touchpoints’ effects on buying behavior, we combine insights from both streams of research to highlight the role of website quality in determining touchpoint performance along the customer journey. Practitioners can use these insights to allocate resources in marketing and sales more efficiently.

The purpose of this article is to verify the impact of missing the earned media and category media in multichannel conversion attribution models on digital media budget allocation. The analysis is based on a very unique approach: 532 users who declared their will to purchase a selected product in the next 3–5 months agreed to install special addons on all their devices connected to the Internet. These devices will register all the users’ activities throughout three months. All user activities on the path to purchase were extracted by means of text mining (URL analysis) techniques. Finally, 5171 activities were found and assigned to particular media areas and media channels. The average user spends 20 per cent of his time in the paid media and owned media areas. However, from the point of view of the number of touchpoints, 29 per cent of the activities occur in these two areas. The obtained results clearly show how much of consumers’ activity in the decision-making process is beyond the control of marketers who, on the basis of this partial data, have to make daily decisions about allocating advertising budgets. The study compared the results of conversion attribution for the full funnel (paid media, owned media, earned media, category media) with the conversion attribution based only on paid media and owned media. The results indicate that not all attribution models lead to similar conclusions in both approaches.

The aim of the article. Competitive analysis in the e-commerce sector using web analytics and statistical methods is an extremely important tool for modern retail companies, as it allows not only to assess their own performance but also to compare key metrics with major market competitors. This approach enables the identification of user behavioral patterns, the development of comprehensive marketing strategies, the increase of conversion rates and customer loyalty, and the rapid response to changes in the digital environment. Analysis results. The main objective of this study is to apply statistical methods for competitive analysis of user behavior on retail websites, providing objective data on the effectiveness of digital platforms and their interaction with audiences. The scientific novelty of the study lies in combining web analytics methods with a statistical approach to quantitatively evaluate the competitiveness of online platforms. The study systematizes key behavioral metrics such as session counts, bounce rates, conversion rates, traffic sources, user return rates, average time on site, number of pages viewed, exit rates, and pages with the highest ratings and exits. Based on these metrics, a model for integrated assessment of web resource performance is proposed, allowing for comparison between platforms and identification of both strengths and weaknesses of each retailer. The practical significance of the results is determined by their applicability for e-commerce enterprises to enhance the effectiveness of online platforms. The study provides recommendations for optimizing website structure, improving user experience (UX/UI), increasing conversion rates, and fostering a loyal audience. This enables marketers and e-commerce managers to make data-driven decisions based on user behavioral patterns and the specifics of the competitive environment. Within the study, users of EVA, Watsons, and Prostor websites were segmented into three behavioral groups: fast buyers, researchers, and price comparers. The analysis focused on user behavior in online cosmetics and household chemistry stores, emphasizing typical user journeys and behavioral segmentation. Comparisons of user behavior across the three platforms revealed differences in engagement, session duration, number of pages viewed, and effectiveness of conversion actions. To assess the effectiveness of web resources, core web analytics metrics were identified: number of visits, number of unique users, average time on site, pages viewed, bounce rate, conversion rate, exit rate, top-rated pages, and pages with the highest exits. This allowed for a comprehensive evaluation of user interaction with the site and identification of critical points for improving platform performance. Additionally, a SWOT analysis of the websites of the studied networks was conducted, highlighting strategic advantages and areas for improvement. Furthermore, a survey was conducted to study users’ subjective perceptions of website functionality and usability. Combining survey data with behavioral analysis enabled the evaluation of real user actions, identification of key patterns, and understanding of interaction dynamics. Based on this, recommendations were formulated to optimize interface design, improve navigation, and increase conversion, contributing to user retention and engagement. Conclusions and perspectives for further research. Future research prospects include the use of artificial intelligence and machine learning to automatically detect user behavioral patterns, the development of predictive models for conversion and loyalty, evaluation of UX/UI factors’ impact on user behavior, integration of data from social networks and mobile applications, and the creation of systems for real-time monitoring of competitive positions. Another important direction involves studying the ethical aspects of data collection and processing, ensuring transparency in analytics, and adhering to privacy principles in digital marketing. Implementing these directions will contribute to the formation of a modern scientific basis for data-driven decision-making in e-commerce and strengthen the competitive advantages of Ukrainian retailers.

No abstract available

Abstract This article investigates how AI can improve the user experience of various e-commerce platforms through personalization and automation, with a focus on consumer perception, trust, data privacy concerns but also the final purchase intention. Although a new topic to be researched, the current literature mentions the clear potential of artificial intelligence in improving customer engagement and satisfaction, although ethical considerations around data handling and bias remain ongoing challenges. Studies show that well-implemented, AI-based recommendation systems and chatbots can make the shopping journey simpler and more enjoyable, yet the acceptance of these technologies varies across different consumer segments. Drawing on these insights, the present research employed a structured, closed-ended survey administered to 150 online shoppers from Romania. The methodology aimed to address three core questions: (1) Does AI-driven personalization positively impact user satisfaction and purchase intention? (2) Are chatbots perceived as effective tools for resolving routine inquiries? (3) Do privacy and data security concerns reduce consumer trust in AI-enhanced e-commerce platforms? Analysis of the collected data indicates that, while most respondents find AI features convenient and helpful, a considerable proportion remains skeptical due to fears of privacy infringement. As a consequence, user trust remains a critical factor in AI acceptance. By analyzing our findings and comparing them with established theoretical frameworks, our paper contributes to the existing body of knowledge on AI applications in e-commerce and marketing. It shows that balancing technological innovation with transparent and ethical data practices is necesary for maximizing AI’s benefits while keeping the consumer trust in online commerce.

Purpose This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase). Design/methodology/approach Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis. Findings The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively. Practical implications This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences. Originality/value This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

Aim/Purpose: This study aims to create customer segmentation who use Online Travel Agent (OTA) mobile applications in Indonesia throughout their hotel booking journey. Background: In the context of mobile hotel booking applications, research analyzing the customer experience at each customer journey stage is scarce. However, literature increasingly acknowledges the significance of this stage in comprehending customer behavior and revenue streams. Methodology: This study employs a mixed-method and exploratory approach by doing in-depth interviews with 20 participants and questionnaires from 207 participants. Interview data are analyzed using thematic analysis, while the questionnaires are analyzed using descriptive statistics. Contribution: This study enriches knowledge in understanding customer behavior that considers the usage of mobile apps as a segmentation criterion in the hotel booking journey. Findings: We developed four user personas (no sweat player, spotless seeker, social squad, and bargain hunter) that show customer segmentation based on the purpose, motivation, and actions in each journey stage (inspiration, consideration, reservation, and experience). Recommendations for Practitioners: The resulting customer segmentation enables hospitality firms to improve their current services by adapting to the needs of various segments and avoiding unanticipated customer pain points, such as incomplete information, price changes, no social proof, and limited payment options. Recommendation for Researchers: The quality and robustness of the customer segment produced in this study can be further tested based on the criteria of homogeneity, size, potential benefits, segment stability, segment accessibility, segment compatibility, and segment actionability. Impact on Society: This study has enriched the existing literature by establishing a correlation between user characteristics and how they use smartphones for tourism planning, focusing on hotel booking in mobile applications. Future Research: For future research, each customer segment’s demographic and behavioral factors can be explored further.

Ride-hailing services have afflictive «monopolized» regions of North Africa and changed the nature of interaction cities and various digital platforms. Yassir — which provides a mobile-based alternative to standard taxi services — has rapidly established itself among Algeria's dominant VTC service providers. But this increasing visibility brings up critical questions about the real quality of the user experience. The company touts the affordability, efficiency, and convenience of its service, but user reviews from the web paint a more complex and, at times, contradictory portrait. By investigating the digital footprints present in comments made publicly by users as well as reviews found on platforms, this study aims to shed light on the various user stages in order to better comprehend the journey customers undergo when engaging with Yassir. The aim really is to find those key friction and satisfaction moments and how that influences their engagement and loyalty. We employed a qualitative netnographic methodology where we compiled and thematically analyzed user generated content on social media and mobile app platforms. Data were coded according to the four key phases of the customer journey: awareness, consideration, decision and post-experience. The results suggest that Yassir customer experience is an assemblage that is constituent by a variety of cognitive, and emotional factors that changes over time. Users usually find out about the app in the initial awareness stage when it is shown in the form of a sponsored ad or via a friend or an influencer. This phase is critical because it sets first impressions. The right pricing models will attract some users, a cross-section will be drawn in by competitive prices and the promise of convenience, while others express initial reluctance based on unclear pricing models and negative word-of-mouth. Those early concerns suggest a perception gap between the promotional promise and the perceived reliability of the service. In the consideration stage, users are actively comparing Yassir against their competitor apps (TemTem and Heetch). Online ratings, reviews and anecdotal experiences all serve as social proof, which is a critical driver of perceptions. While promotional codes and referral incentives boost engagement, cyclical pain points — such as pricing, sudden cancellations, and lack of clear route tracking — are frustrating users. This again points to the need to ensure they can maintain transparent communication and information flow. During the decision-making stage, for example, users tend to prefer availability and price first, since time pressures can influence a booking. Hip at this stage everyone, the app interface, driver ratings, and estimated arrival times, play a huge role in your choice. Yet that experience is often muddied by unpredictable fare fluctuations, delays and unresponsive customer service, all of which erodes trust and pushes some users to competitors. Once they are done with an experience, this post-experience stage will provide important insights into their long-term involvement. They can also serve up satisfied users, who turn into active promoters sharing positive reviews and encouraging others to install the app. By contrast, users who experience recurring problems — like overcharging, impolite or unprofessional drivers, or problems with complaints going unanswered — are more likely to vent publicly about their frustrations. Such negative narratives have a multiplicative effect on brand goodwill and highlight systemic failures in service recovery processes. The analysis finds that even though Yassir manages to attract a wide range of users with high visibility and low-price posting offers, it fails to provide a consistently reliable and transparent service. To improve the end-user experience and gain user loyalty, the platform needs to take some tangible actions: clear pricing structures, increased driver accountability, faster response to user complaints and effective loyalty programs targeting users with consistent usage. They're not just important for addressing the gap between promise and performance, but also for turning transactional interactions into deep and durable customer relationships. By adapting, Yassir stands to further consolidate its competitive edge while building deeper levels of trust and satisfaction among its users.

The growing role of digital platforms in educational marketing has transformed how parents engage with schools before making enrollment decisions. Previous studies have examined social media as a tool for branding and communication, yet few have explored its function as structured touchpoints within the customer journey of parents. This study introduces the ACDAL framework (Awareness, Consideration, Decision, Action, and Loyalty) to map parental engagement across different stages of school choice. Using a qualitative case study approach, data were collected from seven parents and documentation of Facebook and Instagram content published by a private junior high school in Tasikmalaya, Indonesia. Data were analyzed through content analysis to identify patterns of interaction between parents and school-generated content. The findings suggest that awareness is primarily stimulated by academic achievements and visually appealing content; consideration emerges through testimonials and program highlights; decision-making is influenced by responsive communication and transparent information; action is reflected in online and offline enrollment; and loyalty is strengthened by continuous publication of student activities and achievements. These results indicate that social media content functions as critical digital touchpoints along the parental customer journey. The study contributes to the literature by extending the customer journey model to educational settings using ACDAL and offers practical insights for schools seeking to design effective social media strategies to attract and retain parental trust.

Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters that can be useful for targeted consumer insights at scale. We observe higher sensitivity to the design of online shopping portals for session-level purchasing prediction with accuracy/recall in range 91-98%/73-99%, respectively. The user-journey level analysis demonstrates five unique user clusters, wherein New Shoppers are most predictable and Impulsive Shoppers are most unique with low viewing and high carting behaviors for purchases. Further, cluster transformation metrics and partial label learning demonstrates the robustness of each user cluster to new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge models.

This research explores the evaluation of User Experience (UX) design at the Key Kaos online store using the Lean UX approach. Key Kaos online store required user-centered design to increase customer satisfaction and drive engagement. This study integrates Lean UX principles to simplify the design process, encourage collaboration, and rapid iteration.The research methodology includes a combination of user feedback, usability testing, and iterative prototyping, which aligns with the Lean UX framework. Through this iterative process, this research aims to identify key weak points and areas of improvementin the existing UX design of the Key Kaos online store. These findings are expected to provide valuable insights in increasing user satisfaction, improving usability, and optimizing the overall user journey in the market.Additionally, this research explores the impact of Lean UX on cross-functional collaboration, aiming to identify how Lean UX facilitates communication and collaboration between designers, developers and other stakeholders. By assessing the effectiveness ofLean UX in the context of Key Kaos, this research contributes to a broader understanding of its application in the market environment.The expected outcomes of this research include actionable recommendations to improve the UX design of Key Kaos online stores, ultimately contributing to market success by creating a more user-friendly and efficient platform. Additionally, research insightsregarding the application of Lean UX principles can inform future efforts in marketplace UX design, offering a valuable reference for designers and researchers looking to optimize user experiences in online marketplaces.

Customer journey analysis aims at understanding customer behavior both in the traditional offline setting and through the online website visits. Particularly for the latter, web analytics tools like Google Analytics and customer journey maps have shown their usefulness, by being widely used by web companies. Nevertheless, they provide an oversimplified version of the user behavior in addition to other limitations related to the narrow scope over the cases. This paper contributes a novel approach to overcome these limitations by applying process mining and recommender systems techniques to web log customer journey analysis. Through our novel approach we are able to (i) discover the process that better describes the user behavior, (ii) discover and compare the processes of different behavioral clusters of users, and then (iii) use this analysis to improve the journey by optimizing some KPIs (Key Performance Indicators) via personalized recommendations based on the user behavior. In particular, with process mining it is possible to identify specific customer journey paths that can be enforced to optimize some KPIs. Then, with our novel, sequence-aware recommender system, it is possible to recommend to users particular actions that will optimize the selected KPIs, using the customer journey as an implicit feedback. The proof of the correctness of the introduced concepts is demonstrated through a real-life case study of 10 million events representing the online journeys in 1 month of 2 million users. We show and evaluate the discovered process models from this real web log, then use the extracted information from the process models to select and optimize a KPI via personalized recommendations.

E-commerce has been overgrown due to the ongoing advancements in Internet Technology and the notable enhancement of network fundamentals. Nowadays, the most popular type of electronic commerce is online shopping. These days, user profile-based online recommender systems are popular in engineering and research fields. Getting users' profiles right is essential for making recommendations. Many studies on extracting user profiles, including content-based recommendations, have been initiated recently. We have introduced an intelligent recommendation system for e-commerce, aiming to streamline online shopping by addressing information overload. This system leverages customer behaviour to tailor personalized suggestions and enhance user experiences. It combines a genetic algorithm and a decision tree model for e-commerce platforms. The goal is to smooth the shopping journey, ensuring users find what they seek amidst the vast online options. This approach prioritizes user preferences, reducing the risk of overlooking crucial details when navigating online stores. Through experiments and analysis, we assess its effectiveness compared to conventional recommendation systems. We tested the model using publicly available datasets, comparing its performance against existing recommendation systems. The suggested approach is targeted to decrease recommendation sparsity and increase recommendation accuracy.

The force turning this country in the grip of major corporate narrative based marketing to user driven feedback loops is the digital transformation of retail, especially fashion ecommerce. With the Indian e-commerce market expected to reach Rs 297 billion by 2030, for platforms such as Myntra, this "product-performance risk" becomes a continuous issue when there are no physical touchpoints. The purpose of this study is to analyze the overall impact of Online Customer Reviews (OCR) and electronic Word-of-Mouth (e-WOM) on purchase intentions among 100 current retail investors and shoppers. Through a descriptive research design and analysis of the Likert scale data, this study assesses the extent to which review valence, volume and visual content function as online stand-ins for physical inspection. The results show 80% of shoppers who have completed the "add to cart" phase believe reviews are an essential part of the process. One key finding is the power of visual User-Generated Content (UGC); 85% were more likely to make a purchase after viewing reviews with photos or videos, which provide “diagnostic cues” for fit and material. Notably, the amount of reviews (Mean = 4.37) carries more psychological weight than the mean star rating value (Mean = 3.13), implying that Indian consumers give more weight to “crowd validation” as a peripheral heuristic. ” Moreover, while 84% of users use positive reviews to gain a little momentum, the negative feedback carries a disproportionate weight with 94% using it to locate their “deal-breakers. The study finds out that in the case of a high involvement category such as fashion, reviews act as “trust anchors” which help overcome trust element lacking with virtual browsing. In order to maintain this competitive edge, the paper suggests that platforms such as Myntra, with fintech integrated into them, would naturally have greater focus on Explainable AI and authenticated visual feedback designed to close what the researchers called "the trust gap" -- leading to a more transparent and inclusive digital investment/shopping eco-system.

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the user preferences act as the common cause for both ad generation and user conversion, involving the confounding bias and leading to an out-of-distribution (OOD) problem in the counterfactual prediction. In this paper, we define the causal MTA task and propose CausalMTA to solve this problem. It systemically eliminates the confounding bias from both static and dynamic perspectives and learn an unbiased conversion prediction model using historical data. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. Extensive experiments on both synthetic and real data in Alibaba advertising platform show that CausalMTA can not only achieve better prediction performance than the state-of-the-art method but also generate meaningful attribution credits across different advertising channels.

This study aims to investigate the impact of digital marketing on consumer experience and knowledge in our current landscape. As we increasingly rely on online platforms, digital marketing has become an essential tool for businesses aiming to connect with and inform their customers. This research studies how a number of digital strategies, like social media marketing, content marketing, email campaigns, and search engine optimisation, affect how consumers perceive brands and make decisions. To achieve this, a qualitative research approach was taken, using thematic analysis to review existing academic literature, industry case studies, and secondary sources. The analysis is organised using the AIDA model (Awareness, Interest, Desire, Action) to help interpret results at various stages of consumer journey. Findings show that digital marketing boosts consumer awareness by enhancing visibility on digital platforms, sparks interest and desire through targeted and personalised content, and encourages action with user-friendly interfaces and engagement tools. This study offers marketers valuable insights into how digital strategies influence consumer learning and improve the overall experience.

In the era of e-commerce, providing an exceptional customer experience is pivotal for online businesses. This paper introduces a comprehensive machine learning-based recommendation system meticulously crafted to enhance the customer experience on e-commerce platforms. Our system employs a multifaceted approach, incorporating product popularity analysis, model-based collaborative filtering, and textual clustering, to address a spectrum of user profiles and business contexts. It excels in delivering personalized product recommendations, effectively tackling the challenges associated with attracting and retaining new customers, as well as guiding businesses in their nascent stages of online presence. By harnessing diverse methodologies, this system not only optimizes the customer journey but also offers a versatile framework for future research endeavors aimed at continuously refining and adapting to the dynamic e-commerce landscape.

Purpose The aim of the paper is to address the persistent uncertainty surrounding the effectiveness of chatbot-led service failure recovery (SFR) in delivering a satisfactory online customer experience. Prior studies have not explored how chatbot-led SFR processes influence customers’ actual experiences. This gap in the literature may exist because current understanding of chatbot–customer interactions obscure how individuals’ adoption of chatbot-led SFR shape their experiences. Design/methodology/approach Drawing on frustration–aggression theory and signaling theory, and building on a social constructivist philosophical paradigm, this paper interprets participants’ narratives on chatbot-led interactions and online experiences. Empirical data was generated through 52 in-depth interviews conducted with participants from the USA, France, Italy, and the UK. Findings Through thematic analysis of interview data, the study presents two key contributions. First, this paper elucidates the dynamics unfolding between customers and chatbots in a service recovery journey, encompassing customers’ priorities and expectations. Second, this paper delineates three customer typologies based on their interactions with chatbots during chatbot-led SFR, including their emotional responses. These interactions could either positively or negatively signal future patronage of chatbots. The identified three customer types can assist managers to reshape their strategies to effectively turn negative customer experiences into opportunities for enriching online customer experiences. This could involve providing multiple touchpoints, including human-led and chatbot-led interactions in the SFR process. Originality/value This study proposes that chatbots are not just technological tools that support customers during service failures and facilitate connection with the brand, but also function as signals that trigger emotional and cognitive responses, thereby influencing the customer experience.

This article focuses on the study and development of a Customer Journey Map (CJM) using the example of the Kazakhstani company Advert Reprise Digital LLP (hereafter referred to as Advert Reprise Digital). The purpose of the study is to develop and analyze a Customer Journey Map for Advert Reprise Digital, with a focus on comparing customer experience characteristics in the digital, FMCG, and pharmaceutical sectors to develop recommendations for optimizing customer communication. The authors closely analyzed the company's structure, activities, and conducted a comprehensive review of the customer experience to identify key interaction points and areas for improving communication. The main outcome is a detailed customer journey map that visualizes the stages of the customer's journey, their needs, potential challenges. It offers recommendations to improve interaction efficiency and enhance the company's customer experience. The practical value of mapping is validated by successful implementations in international companies such as Starbucks, IKEA, and Amazon, demonstrating the method’s effectiveness in improving customer communication and increasing loyalty. The effectiveness of this approach is also supported by the authors' experience in creating customer journey maps for both Advert Reprise Digital itself and clients like Pladis, Stopdiar (Gedeon Richter), and Smecta (Mayoly). The developed Customer Journey Map helped identify vulnerabilities in the consumer journey and provided practical recommendations that were used to optimize communication with customers.

The research objectives is to identify critical touchpoint (s) of digital customer journey in PT BTC marketplace channel strategy, to discover the result of current PT BTC digital customer journey feedback, to give recommendation in digital marketing strategy to improve PT BTC digital customer journey, and to give supporting strategy to enhance PT. In this research, data collection methods will be using qualitative data from Primary Data and Secondary Data. This research found 5 critical touchpoints of digital customer journey in PT BTC marketplace channel strategy, like Awareness, Appeal, Ask, Act, Advocate. Current marketplace channel strategy is effective to catch new B2B customers but less effective to retain their consistency to buy via online transaction. From this finding research, the best suitable digital marketing strategy that fit to PT BTC customer journey is creating and establishing website and improving its system to become modify-website that enabled to e-commerce facilities. The supporting strategy to establishing website marketing strategy is purposing new company’s marketing division structure to delegate workload better, more effective and more efficient.

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The article considers a practical case, using the example of an SME company, which shows the effectiveness of using the customer journey map (CJM) tool. The purpose of this study is to obtain confirmation of the effectiveness of CJM as a tool for customer analytics in the context of the activities of a Kazakhstan company a representative of SMEs. A company operating in the market of Almaty was chosen as the object of study. The subject of the study is the process of applying the customer journey map. The authors of the article obtained data confirming the impact of CJM on the company's performance level, including indicators of marketing activities and the return on efforts to organize and execute marketing activities focused on the client and using client analytics tools, as well as financial indicators. The article's content focuses on the fact that customer analytics is of particular importance in digitalization including because digitalization provides new opportunities for small and medium-sized businesses to use such tools. When conducting the study, a case study methodology was applied, which, in combination with the method of indepth interviews, allows for obtaining qualitative information about the subject of the study. The study results allow us to conclude that it is expedient to implement the customer journey map in the company's processes the object of study. The positive results and practical experience of testing CJM in the conditions of a Kazakhstani SME company indicate the possibility of replicating both the CJM tool itself among Kazakhstani small and medium-sized businesses and the experience of the article's authors in this direction.

This study investigates the influence of Voice AI capabilities and conversational marketing features on customer experience (CX), trust, satisfaction, and loyalty. Although prior research has emphasized the role of CX in shaping consumer outcomes, limited attention has been given to the combined effects of Voice AI and conversational marketing within a trust-centered framework. This research addresses this gap by clarifying when and how trust matters most in AI-mediated interactions. A quantitative, cross-sectional survey design was employed, collecting data from consumers familiar with AI-driven services. Using PLS-SEM in SmartPLS 4.0, the study tested direct, mediating, and moderating effects across a sample representative of digitally engaged users. Results reveal that Voice AI (β = 0.426, p < 0.001) and conversational marketing (β = 0.395, p < 0.001) significantly improve CX. CX strongly predicts satisfaction (β = 0.699, p < 0.001) and moderately predicts loyalty (β = 0.216, p = 0.006). Trust directly influences loyalty (β = 0.625, p < 0.001) but does not significantly affect satisfaction (p = 0.250). Moderation tests show trust strengthens CX → satisfaction but not CX → loyalty. The study contributes by extending Customer Experience Theory, showing that CX drives satisfaction, while trust sustains loyalty. Practically, it highlights the need for empathetic, transparent AI design to balance short-term and long-term consumer outcomes.

Big data analytics is becoming more important in marketing today. This tool helps companies understand market trends so that they can develop targeted campaigns. In this assignment, we are required to examine how the application of big data analytics can help marketers understand the behavior of their customers and develop an effective marketing campaign. This research paper contains a detailed analysis of consumer interactions on digital platforms using advanced machine learning algorithms like Gradient Boosting Machines and Random Forests. The author employed these models to derive insights from large data sets. The goal is to understand how the use of large amounts of data in tracking customer behavior can improve marketing strategies. The research of this study included an in-depth examination of both the organized and disorganized consumer information data. By utilizing predictive models, the research can accurately forecast customer behavior. The GBM model showcased remarkable performance in foretelling customer’s purchase behavior, realizing a phenomenal accuracy rate of 94.39%. Furthermore, by using K-means clustering to group consumers based on their behavior, marketers could develop better marketing strategies. This article provides a lot of useful information about digital marketing and the use of vast amounts of data to analyze it. This statement means that companies give great importance to data analytics to make better marketing decisions. This research serves as a foundational element for subsequent investigations and provides an avenue for further scrutiny regarding the integration of additional data and the enhancement of machine learning algorithms.

Digital strategy represents an inevitable requirement for superiority in a digital world (Menz et al., 2021; Turuk, 2020). More precisely, this study aimed to determine the key success factors (KSFs) of the digital marketing strategy that have an influential effect on the marketing performance of small enterprises (SEs) in Jordan. A theoretical framework has been proposed based on an extensive review of the related literature and the assumptions of the technology acceptance model (TAM). The study followed the quantitative approach, where intended data were gathered from 370 valid e-questionnaires based on the convenience sampling technique. Then, the collected data was statistically analyzed using the AMOS V.23 program. The findings of the research pointed out e-marketing orientation as a prominent KSF for SEs and recommended that such orientation deserves to be positioned at the heart of the digital strategy to drive success. In addition, the findings confirmed the indirect effect of customer awareness and consideration in the relationship between digital marketing applications and performance. Customer awareness and consideration need to be given more attention while developing SEs’ digital marketing model, as it plays a pivotal role and constitutes a crucial mediating variable between each of the digital strategy variables and the targeted performance of small enterprises (PSEs).

This study aims to examine the influence of digital marketing and dynamic pricing on customer satisfaction and hotel performance, with customer satisfaction serving as a mediating variable and online booking as a moderating variable. The research focuses on three-star hotels in Bali Province to understand the effectiveness of digital marketing strategies and price flexibility in enhancing customer experience and hotel performance amid increasingly dynamic industry competition. A quantitative approach was employed through a survey of managers of three-star hotels registered with hotel associations in Bali. The data were analyzed using PLS-SEM to test the direct, mediating, and moderating effects within the research model. In addition, IPMA was utilized to identify managerial priorities in improving variables that contribute to hotel performance. The findings reveal that dynamic pricing significantly influences customer satisfaction and hotel performance, while digital marketing does not show a direct effect on satisfaction. Customer satisfaction is confirmed to be an important mediator in improving hotel performance. Furthermore, online booking strengthens the effect of dynamic pricing on satisfaction but does not moderate the impact of digital marketing. These results offer practical implications for hotel management to prioritize customer satisfaction, optimize data-driven dynamic pricing strategies, and ensure effective integration between digital strategies and online booking systems to enhance long-term competitiveness.

In Africa's digital real estate market, traditional marketing no longer suffices. This study uses the Technology Acceptance Model to explore how Nigeria's Realvest can enhance customer acquisition and retention through better digital marketing. Based on data from 280 Nigerian proptech professionals, it identifies gaps in Realvest's use of data mining, audience targeting, and customer relationship management. The research surveys digital marketing tactics like SEO, email automation, content marketing, and customer journey mapping, comparing Realvest to leading African firms such as Kenya's Optiven, South Africa's Pam Golding, and Nigeria's Adron Homes. Findings reveal how digital tools can improve customer experience and brand loyalty, guiding Realvest to align marketing with evolving consumer behavior and technology.

This study employs the VIKOR method to evaluate and rank digital marketing strategies based on their effectiveness across multiple criteria. The research analyzes five key strategies: Website Analytics, Customer Engagement, Social Media Marketing, Content Marketing, and Email Marketing, assessing their performance in Data Collection, Conversion Rates, Customer Journey Analysis, and Behavioral Segmentation. The VIKOR method, a multi-criteria decision-making tool, was applied to determine the best and worst values for each strategy across the specified criteria. The analysis revealed significant variations in performance among the strategies. Customer Engagement emerged as the most effective strategy, ranking first in the overall evaluation, highlighting the importance of fostering meaningful interactions with customers in the digital landscape. Content Marketing and Email Marketing followed, securing the second and third positions respectively, underscoring their crucial roles in modern digital marketing. Interestingly, Social Media Marketing ranked fifth, suggesting potential areas for improvement despite its popularity. The study also calculated Sj (performance score), Rj (regret measure), and Qj (comprehensive evaluation) values for each strategy, providing a nuanced understanding of their strengths and weaknesses. The findings emphasize the need for an integrated approach to digital marketing, where strategies are tailored to specific objectives and target audiences. The study underscores the importance of continual evaluation and adaptation in the rapidly evolving digital landscape. By focusing on customer engagement, delivering high-quality content, and effectively integrating various digital channels, marketers can enhance their overall performance and achieve better results. This research provides valuable insights for digital marketers seeking to optimize their strategies in the dynamic digital marketing environment, offering a methodological framework for assessing and improving marketing effectiveness.

In modern marketing, researchers are focused on the omnichannel approach due to the growing number of communication channels and sources of consumer information about brands. This paper examines the complexity and multifactorial nature of the omnichannel approach in the formation of customer experience and marketing communications in the digital environment. The article discusses the concept of a “Consumer Journey Map”, which reflects the stages of making a purchase decision, after-sales interaction and the customer experience formed within this path. This approach allows you to define and analyze the touchpoints with the consumer and information messages at each stage of the consumer journey in order to create a positive brand image. The consumer’s journey is formed by the sequential passage of a number of points of contact, which may belong to the brand, its partners or be independent. This affects the degree of brand control over the information at each of these points. Particular attention is paid to the complexity of tracking the entire variety of contact points through which the customer passes on the way to making a purchase. The management of this variety of points from the standpoint of a single communication environment of brand and consumer interaction is the subject of omnichannel marketing research. Within the framework of this study, the author considers the evaluation of the effectiveness of marketing channels at the stage of preparation for purchase. These channels may be underestimated, since in terms of direct return on marketing investments, they lose to the final pre-purchase channels, but they play a key role in shaping the customer experience leading to subsequent purchases. The article presents the results of a study of online communication based on a company providing educational services. This study demonstrates the relationship between the volume of investments in paid promotion channels and the total revenue from online channels. The results of the study emphasize the importance of the omnichannel approach in modern communication, where the buyer interacts with the brand in various channels and perceives communication as a single continuous process, even when switching from one channel to another.

The article examines a step-by-step algorithm for developing a digital marketing strategy as a structured system that combines classical marketing principles and digital tools to achieve business goals. The difference between digital marketing and internet marketing is analyzed. The strategic hierarchy in marketing planning is considered. The key stages of building a strategy are defined: audit of current assets, market and competitor analysis, analysis and segmentation of the target audience, goal setting according to SMART criteria, creation of a customer journey map (CJM), development of conversion funnels, selection of digital channels, work on a traffic strategy, development of a content strategy, determination of KPIs, and financial assessment of marketing effectiveness. Emphasis is placed on the importance of a flexible approach to forming a digital strategy, which will take into account the specifics of the business, its goals, assets, and behavioral habits of target consumers.

Machine learning applications have become increasingly integral to digital marketing performance measurement and customer engagement analytics due to the volume, velocity, and behavioral richness of digital interaction data. This study quantitatively examined the relationships between machine learning–derived engagement indicators, marketing exposure variables, and digital marketing performance outcomes using an observational dataset of 1,250 user-level records. Descriptive analysis revealed substantial behavioral variability, with interaction frequency averaging 14.6 interactions per user (SD = 6.3), engagement recency averaging 4.1 days (SD = 2.7), and session depth averaging 6.2 actions per session (SD = 2.1). Reliability assessment confirmed strong internal consistency across all multi-item constructs, with Cronbach’s alpha values ranging from 0.82 for conversion outcomes to 0.93 for the customer value index. Multivariate regression results indicated that engagement intensity was the strongest predictor across all performance outcomes, with standardized coefficients of 0.38 for conversion, 0.42 for retention, and 0.41 for customer value, all statistically significant at p < .001. Engagement frequency also demonstrated positive and significant effects, with coefficients of 0.31 for conversion and 0.29 for customer value. Engagement recency showed a negative association across models, with coefficients ranging from −0.17 to −0.23, indicating declining performance as interaction gaps increased. Exposure frequency exhibited smaller yet significant effects, with coefficients between 0.18 and 0.24. The regression models demonstrated satisfactory explanatory power, reporting adjusted R² values of 0.39 for conversion, 0.44 for retention, and 0.47 for customer value. Hypothesis testing results showed that 11 of the 12 proposed hypotheses were supported. Overall, the findings demonstrated that machine learning–enabled engagement analytics substantially enhanced digital marketing performance measurement by capturing behavioral mechanisms underlying conversion, retention, and value creation more effectively than exposure-based metrics alone.

This study aims to identify opportunities and barriers in developing and implementing Food Shopping Support Systems (FSSS) for healthier and more sustainable choices, given the growing consumer demand and persistent societal problems related to food. The study examined the social and technical value of FSSS in an early development stage through one-on-one expert interviews (n = 20) and consumer focus groups (4 groups, n = 19). Experts were employed in the fields of behavioral sciences, digital marketing, decision aids, software development, persuasive technologies, and public health and sustainability. Consumer participants were used to shopping online. Through a card sorting task followed by semi-structured interview questions, responses were elicited. Participants were presented with 17 cards in 5 rounds, each addressing a different topic related to decision support. Results show that support is perceived useful, particularly when suggestions are personalized, transparent, and justified (using labelling or informative text). Opportunities for uptake were presenting suggestions early in the shopping trip in a visible but non-disruptive manner, allowing autonomy to choose the type of guidance (e.g., show sustainable but not healthier suggestions) and to (not) provide personal data, and educating consumers. Negative attitudes were associated with support being disruptive or steering, being of low credibility, and unclarity about what is healthy or sustainable. Consumer participants expressed concerns about too generic suggestions in relation to health and lack of knowledge about labelling. They emphasized that excessive support and required effort, such as repeatedly providing data, can be burdensome. Experts also worried about limited consumer interest and not having the required data to provide support. Results from this study reveal the potential for successful digital interventions to encourage healthier and more sustainable choices and what this means for further development.

PurposeChatbots have been explored as a novel approach to enhancing consumer engagement by delivering more enjoyable, personalized services. This research aims to investigate the mechanism through which anthropomorphic elements of chatbots influence consumers' intentions to use the technology.Design/methodology/approachThis research introduces five key concepts framed through the “computers-are-social-actors” (CASA) paradigm: form realism (FR), behavioral realism (BR), cognitive trust (CT), entertainment (EM) and chatbot usage intention (CUI). An online questionnaire garnered 280 responses from China and 207 responses from Indonesia. Data collection employed a combination of purposive and snowball sampling techniques. This research utilized structural equation modeling through the analysis of moment structures (AMOS) 27 software to test the hypotheses.Findings(1) FR positively predicts CT and EM, (2) FR negatively predicts CUI, (3) BR positively predicts CT and EM, (4) BR positively predicts CUI and (5) Both CT and EM mediate the relationship between FR and CUI, as well as between BR and CUI.Originality/valueThis research enriches the current literature on interactive marketing by exploring how the anthropomorphic features of chatbots enhance consumers' intentions to use such technology. It pioneers the exploration of CT and EM as mediating factors in the relationship between chatbot anthropomorphism and consumer behavioral intention. Moreover, this research makes a methodological contribution by developing and validating new measurement scales for measuring chatbot anthropomorphic elements.

In today’s rapid development of information technology and big data technology, consumer behavior is undergoing a profound transformation. This study focuses on the decision-making stage of consumer journey, selects indicators based on webpage click stream data, improves the K-means algorithm, and realizes the identification of consumer journey nodes using the binary K-means algorithm. Based on the review recommendation scenario, from the perspective of consumer decision-making journey, we introduce the “attention-attitude-understanding-purchase intention” stage-based decision-making model, apply it to the model design of deep learning, and combine the attention mechanism and co-attention mechanism to propose a product recommendation method based on online reviews. The results show that consumers in clusters 1-4 are in the consumer journey nodes of attention, understanding, attitude, and purchase intention, respectively. The product recommendation model exhibits better recommendation accuracy and time efficiency, with accuracy improved by 18.72%~67.12% and time reduced by 8.39%~62.03% over the comparison method. This paper realizes the innovation of deep learning method with the support of consumer behavior theory, and improves the methodological technical support for accurate online marketing strategy.

—The popularity and easy access of Internet has driven the sprung-up of e-commerce platforms, some of which finally disappear, some like VIPSHOP withstand competitions. This research explores the impact of graphic information, live marketing and product placement adopted by e-commerce platform on consumer behavior at different AISAS (attention, interest, search, action and share) stage in the context of VIPSHOP, adopting online questionnaire to collect data. From a total of 137 valid questionnaires, the author finds that online graphic and text information has a greater impact on stage of SA than other three stages, product placement has a greater impact on the latter A stage than other four stages and live streaming ’s impact on five stages is positively the same. The author suggests that enterprises should make rational use of the influence of these three marketing means on the five stages of the AISAS, make the three marketing means complementary to each other, further optimize and improve their own marketing strategies.

No abstract available

Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

Today’s world thrives on artificial intelligence’s (AI) ability to unlock new and exciting ways to engage customers. By powering innovative offerings and personalized experiences, AI strengthens the bond between brands and their consumers, setting them apart from the competition. According to the Stimulus–Organism–Response (SOR) model, this study aims to analyze how stimulus factors in Artificial Intelligence (AI) marketing efforts impact consumer behavior in food delivery service applications. This research uses a quantitative, descriptive, and cross-sectional survey approach. Structural Equation Model (SEM) was used to analyze 412 responses from a questionnaire survey of Generation Y and Z subjects who used food delivery service applications from 2 leading platforms in the industry, such as Gojek (GoFood) and Grab (GrabFood). The results showed that all stimulus factors in AI marketing efforts affect brand experience, while only information and interaction affect brand equity. Both brand experience and equity significantly influence responses (brand preference and reuse intention). Implications of this study can activate academia and business practitioners to understand the influence of AI on user experiences and provide a guide for the development of marketing and branding strategies to strive for customer satisfaction by offering online service.

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy.

The rapid development of social media has significantly changed consumer behavior patterns, making online shopping an indispensable part of life. This paper delves into the impact of social media marketing on consumer purchasing decisions against this backdrop. Taking Douyin as an example, the study reveals that content quality, online reviews, influencer charisma, and promotional activities are key influencing factors. The constructed consumer purchasing decision model for social media platforms shows that consumers are influenced by content quality in the awareness stage, by online reviews and influencer charisma in the interest stage, and by promotional activities in the purchase intention stage. The conclusion provides relevant suggestions from the perspectives of consumers and social media platforms, emphasizing that consumers should make careful decisions while platforms should enhance management and optimize marketing to address the opportunities and challenges posed by social media marketing.

The accelerated advancement of digital technologies has led to the increasing adoption of digital vouchers as a strategic marketing tool in e-commerce. E-commerce platforms such as TikTok Shop and Qpon extensively implement price discounts and various sales promotion programs to shape consumer purchasing behavior in Indonesia. This study seeks to examine the impact of digital voucher marketing strategies, specifically price discounts and sales promotions, on impulsive buying behavior, with positive emotion serving as a mediating variable. The research is grounded in the S–O–R theory, in which digital voucher strategies function as stimuli, positive emotion represents the internal organismic state, and impulsive buying constitutes the behavioral response. A quantitative descriptive-verificative methodology was employed, with data gathered through an online survey distributed to consumers in the Jakarta region who have previously utilized digital vouchers on TikTok Shop or Qpon. The findings are expected to indicate that price discounts and sales promotions exert a positive influence. This study contributes empirical evidence regarding the significance of emotional mechanisms in the effectiveness of digital voucher-based marketing strategies.

This research investigates the influence of live streaming on consumer online behavior before and after the COVID-19 pandemic. The influencer economy s rise and live streaming advancements have diversified China s internet economy. COVID-19 significantly boosted e-commerce live streaming s popularity, making it a consumption hotspot. Utilizing Affective-Behavioral-Cognitive (ABC) attitude theory and Stimulus-Organism-Response (S-O-R) theory, the study designed scales and questionnaires to assess e-commerce live streaming s impact on consumer purchase intention across pandemic stages. Analyzing 710 valid samples revealed high interactivity, promotional policies, trust, content quality, and influencer professionalism positively affect purchase intention, whereas perceived risk has a negative impact. Consumer attitudes partially mediate this relationship, with significant differences observed across ages, spending levels, and platforms like Tmall Taobao and Douyin. These insights inform tailored marketing strategies and market segmentation. Keywords include COVID-19, e-commerce live streaming, and consumer purchase intention.

This study explores the impact of social media marketing and online consumer reviews on purchasing decisions, with brand image as a mediating variable on TikTok. The research, conducted with 100 respondents who were active students in Indonesia and had made purchases via TikTok, used a quantitative approach and purposive sampling. Data analysis applied Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) method. The findings indicate that social media marketing and online consumer reviews both positively and significantly affect purchasing decisions and brand image. Furthermore, brand image plays a key role by mediating the relationship between social media marketing, online consumer reviews, and purchasing decisions. This study enriches existing literature by highlighting social media marketing and consumer reviews as essential factors shaping brand perception and purchasing behavior on TikTok. It supports the Theory of Planned Behavior (TPB), which emphasizes that attitudes, subjective norms, and perceived behavioral control influence consumer actions. From a practical standpoint, businesses can enhance their brand image and drive consumer decisions on TikTok by creating interactive, engaging content.

This study explores the enhancement of customer journey mapping through the application of cloud-based AI solutions. With businesses increasingly relying on data-driven insights, AI-driven mapping systems help deliver a more comprehensive view of customer interactions and preferences. Cloud-based platforms, combined with AI, provide scalable, real-time analytics that adapt to individual customer needs. This paper discusses how AI technologies integrated with cloud systems improve personalization, optimize engagement touchpoints, and identify new marketing opportunities. Results show that businesses adopting these solutions report significant improvements in customer satisfaction and retention rates.

In the dynamic nature of business especially in e-commerce, the management of customers has greatly been made to be a cardinal focus of any organization. This paper aims to analyse and discuss applications of big data and customer big data experience to improve the overall customer journey. Application of modern technologies including machine learning, deep learning, and processing real-time data is in the scope of the proposed methodology, which contains a full set of stages including data collection, preprocessing and feature engineering, as well as recommendation generation at the personal level. Neural collaborative filtering (NCF) improved performance results, and the precision and recall rates are greater than 93% suggesting that these methods can better capture complex patterns of user behavior. Real-time personalization also brought sustainable benefits regarding the most important metrics: a 50% increase in average session duration, a 73.5% increase in conversion, and a 38.2% decrease in the cart abandonment rate. Strong customer privacy protection measures were in place to address legal requirements including GDPR, and algorithm ethicality, specifically in bias in personalization guarantee. The results highlight the innovativeness of big data approach in designing positive buying experiences bye-commerce firms. This research offers recommendations for organizations that want to use advanced analytics for digital business innovations based on customer value, which sets the basis for future developments in the realm of personalized electronic commerce.

In today’s customer-centric market, offering integrated and seamless customer experiences strengthens business performance. One of the essential components of this journey is to personalize these experiences using data-driven methodologies. The objective of our research focuses on leveraging AI-driven data engineering to enhance customer experience personalization. An illustrative approach focuses on integrating deep learning methodologies for more meaningful interactions in real time. Measurable results display the effectiveness of AI to learn, understand, and derive optimal experiences tailored to every customer’s needs based on event data recording human interactions. In this digital world, a personalized experience makes an emotional connection and a competitive difference. However, with the large and multi-modal data channels and volume of data, data creation consumes excessive manual efforts and is scarce for an enterprise. Only a small percentage of customers’ data is used for personalization. Our objective is to leverage AI-driven data engineering to advance the personalization of the customer experience. We exemplify this by integrating deep learning into a big data environment to enhance and elevate the personalization of customer interactions. Measurable results show that our AI-driven approach can personalize the response. By combining and leveraging big data engineering in the data frame for modeling and analysis, we showcase the power of AI in tailoring experiences that are fitted to every customer using subsequent procedures that take structured events to match a narrative experience determined by unsupervised learning of dynamic customer journeys and map these dynamic clusters to customer answer sentiments. We demonstrate the proposed illustrative proof of method omnichannel, AI-driven, and deep learning modular-driven framework to process structured logs and the propagation of AI-driven business value improvement in a global and growing company by using the deep learning modeling improvements in the architecture of the algorithm. Finally, we show the effectiveness of deep learning by monitoring and measuring the chatbot performance when the relevant audience size grows exponentially. Our PM sets out end-to-end data engineering bundles with deep learning-driven data prep tasks across the three multi-modal data channels. Data and tools can be tailored to a company’s application landscape and technologies.

This paper explores the development of a conceptual model for Data-Driven Customer Value Management (CVM) aimed at enhancing product lifecycle performance and market penetration. The proposed model integrates customer insights, data analytics, and product lifecycle management to create a comprehensive framework for maximizing customer value throughout the product journey. It emphasizes the importance of data collection, personalization, segmentation, and cross-functional collaboration in driving customer engagement, loyalty, and retention. By leveraging advanced technologies such as CRM systems, machine learning, and analytics platforms, businesses can gain actionable insights to optimize product offerings, marketing strategies, and customer interactions. The study highlights how businesses can align customer value strategies with each stage of the product lifecycle—from development to decline—ensuring sustained relevance and competitiveness in the market. Furthermore, the paper provides practical recommendations for businesses to enhance product lifecycle performance and improve market penetration by focusing on customer-centric strategies, data-driven decision-making, and the optimization of communication channels. The findings underscore the need for businesses to continuously monitor and refine their CVM strategies based on customer feedback and data insights. Future research could explore the integration of emerging technologies and industry-specific adaptations further to enhance the effectiveness of the proposed conceptual model.

There has been a recent increase in interest regarding the remarkable potential of artificial intelligence (AI) to profoundly transform online advertising. The purpose of this research is to critically assess how AI can enhance customer experience (CX) in various business applications. We aim to identify important concepts, evaluate the impact of AI-powered CX initiatives, and offer suggestions for future research. By conducting a thorough analysis of academic publications, industry reports, and case studies, this study extracts theoretical frameworks, empirical findings, and practical insights. The results highlight the significant changes that occur with the integration of AI into Customer Relationship Management (CRM). AI enables personalized interactions, strengthens customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four key themes emerge from research findings: personalized service, improved engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article makes a valuable contribution to the AI in CRM discourse by summarizing the current state, exploring key themes, and suggesting future research opportunities. It is strongly advocated for responsible AI implementation, emphasizing ethical considerations and providing guidance to organizations as they navigate the opportunities and challenges presented by AI.

In today's world for we use online medium for virtually every aspect of our lives. Companies run controlled web experiments to make data driven decisions, to provide an intuitive online experience. We see a big correlation between online customer behaviors and designs and personal treatment, which could be used to create better customer engagement. In this paper we have studied the impact of design elements on chat invites*, by running experiments on a small population, using machine learning algorithms. Based on this we identify significant elements and build the most opportune personalized messages on invites. Statistical results show that, more visitors on the website accept chat invites which are personalized and optimized for the design. At [24]7, we have experimented extensively on user interface designs and journey based personalization which resulted in positive impact on our annual revenue.

ABSTRACT: The current paper will describe the AI-based system of process mining that is specifically oriented to redesigning the customer experience in the insurance industry, relying on the high-quality data supply, the workflow optimization based on the AI model, and the real-time personalization. The combination of the three fundamental layers is its methodology and contains (1) Data Quality & Governance, (2) AI-driven Process Intelligence, and (3) Personalized Decision Support. To attain consistent, trusted, unified datasets, the solid data quality approach is exercised on automated metadata capture, master data management, anomaly recognition, and real-time data validation pipelines and assures coherent and trusted datasets in policy systems, claims, and customer interaction systems. It is an organized and clean database that will provide reconstruction of the customer journey and high-quality process models. The process mining methods (AI) are then followed by the first part of the process, which is necessitated by the analysis of the end-to-end workflows, and then extract event logs, sequence modelling, and the conformance checking. The machine learning models identify the existence of bottlenecks, foretell delays, and will and patterns that impact the complexity of the churn or claim. On generative AI, operationally generated insights, draft customer messages, and simulated what-if underwriting decisions and claims are created. Its impacts have already found their way into real-time applications: insurers have already shortened by 22-30% a claims cycle time, automated by 40-60% routine customer contacts with intent-conscious virtual agents, and improved fraud detection by 18-25% with beam-of-thought anomaly models. Individualized policy recommendations regarding customer life stage analytics, sentiment data, and situational indicators have resulted in a boost of responses and cross- sell conversion of more than 15%. Findings have revealed that with the convergence of process mining, predictive analytics, and customer-specific generative AI, the insurers would be able to provide more rapid, transparent, and personalized experiences at scale and portray a significant behavioral change where the operations of serving a client are reactive as compared to anticipatory and, as a result, customer-driven.

Amazon’s failed luxury stores initiative offers a critical case for examining the incompatibility between mass-market digital strategies and high-end service environments. This study extends the TCQ (touchpoints, context, qualities) framework by introducing a luxury-centric variant (L-TCQ), illuminating how symbolic, hedonic and prestige-driven value co-creation processes are undermined when convenience overtakes exclusivity. A thematic analysis was conducted on qualitative data from 35 international MBA students specializing in luxury brand management. Participants evaluated Amazon luxury stores in real-time, generating experiential feedback based on structured digital journey immersion. The results reveal that Amazon failed to deliver critical luxury-specific experiential qualities, including immersion, personalization and brand legitimacy. The study introduces “nonlinear process sequence” as a luxury-specific construct describing the nonlinear and symbolic navigation of digital services by high-end consumers. Luxury service providers should design digital platforms using the L-TCQ framework to foster symbolic engagement, emotional immersion and prestige signaling – key elements absent from mainstream customer experience (CX) design. This research contributes to services marketing by proposing the L-TCQ model, a theoretical refinement that incorporates luxury-specific service dimensions into the TCQ framework. It advances the field by theorizing how experiential, contextual and symbolic co-creation failures explain Amazon’s shortcomings and offers an actionable roadmap for digital luxury CX design.

The digital revolution has reshaped marketing through big data, hyper-connectivity, and sustainability demands. Big data analytics now drives consumer insights, omnichannel strategies, and AI-powered campaigns, shifting from mass advertising to real-time personalization. A key development is the merger of data and sustainability, where transparency combats greenwashing and aligns brands with eco-conscious values—exemplified by Patagonia and Unilever. The chapter explores the nonlinear Customer Decision Journey (CDJ), influenced by social media and instant feedback, while addressing ethical challenges like data privacy and algorithmic bias. Case studies reveal how data-driven sustainability builds trust and competitive edge. Ultimately, this convergence marks a paradigm shift, demanding agile, tech-integrated strategies. To thrive, brands must leverage data for personalized, transparent, and sustainable experiences in the digital economy. 

This study examines how artificial intelligence transforms destination branding from one-way promotion to dialogic engagement and AI-driven personalization. Using destination brand theory, smart tourism frameworks, and the DART model (Dialogue, Access, Risk-Benefits, Transparency), the strategic documents from Barcelona, Dubai, and Singapore were analysed to understand AI’s role as a co-creator of visitor experiences rather than merely a technical tool. The goal of the qualitative content analysis is to investigate how AI integrates with complementary technologies (big data, chatbots, social media analytics) to generate content, personalize experiences, and enable real-time engagement. Specifically, it examines how AI integrates with complementary technologies such as big data, chatbots, and social media analytics to act as a strategic co-creator of content for destination branding. Furthermore, the research investigates how AI enables stakeholder involvement in cocreating visitor experiences and enhancing the customer journey, thereby strengthening destination brand equity. By addressing these two research questions, the paper contributes to understanding the implications of AI-driven strategies for collaborative branding and experiential marketing in tourism destinations. Findings reveal three distinct models. Dubai’s governance-first approach, Barcelona’s infrastructure-first model, and Singapore’s technology-advanced but participation-limited strategy. The research demonstrates that AI-enabled co-creation requires governance infrastructure before technological infrastructure, with transparency and stakeholder collaboration as prerequisites for authentic brand narratives. Strategic recommendations for destination management organizations are presented in the conclusion.

The digital landscape continues to evolve rapidly as businesses adopt intelligent marketing platforms to deliver personalized data-driven customer experiences on a scale. Salesforce Marketing Cloud uses Artificial Intelligence (AI) and automation to convert traditional marketing methods into highly dynamic targeted strategies through its cloud-based platform. This paper investigates how Salesforce Einstein technology uses AI to deliver predictive analytics and behavioral insights and content personalization while Journey Builder and Automation Studio automate multichannel campaigns and customer engagement. The research evaluates essential features and advantages and practical implementations and technical obstacles to demonstrate how businesses can use AI and automation for better marketing performance and customer retention and business expansion. The paper examines upcoming trends and intelligent automation's potential influence on developing modern marketing strategies for the future.

: In the era of digital marketing, optimizing the consumer funnel in real-time has become a critical challenge for businesses seeking to maximize conversion rates and customer lifetime value. This paper presents a novel framework that leverages artificial intelligence (AI) and data science techniques to enhance the consumer funnel dynamically. By integrating machine learning algorithms, natural language processing, and real-time data analytics, our approach enables businesses to adapt their marketing strategies instantaneously based on consumer behavior and market trends. We introduce a multi-layered architecture that combines predictive modeling, sentiment analysis, and personalization engines to create a responsive and adaptive funnel optimization system. This research contributes to the growing field of AI-driven marketing automation and offers practical insights for implementing advanced analytics in consumer journey optimization

ABSTRACT The rapid proliferation of Financial Technology (FinTech) has fundamentally transformed customer engagement paradigms in the Indian financial services sector. This study empirically investigates the influence of Artificial Intelligence (AI)-driven personalization and automation tools on customer satisfaction, trust, and engagement among Indian FinTech users. Grounded in the Technology Acceptance Model (TAM), UTAUT2, Privacy Calculus Theory, and Information Systems Trust Theory, the research adopts a quantitative cross-sectional design. Primary data were collected from 200 FinTech users across India using a structured Likert-scale questionnaire spanning eight constructs. Statistical analyses comprising one-sample t-tests, Pearson correlation analysis, and one-way ANOVA were executed using SPSS. Key findings reveal that AI personalization exerts the strongest positive influence on customer satisfaction (r = 0.706, p < 0.01) and trust (r = 0.671, p < 0.01). Automation exposure significantly correlates with engagement (r = 0.438, p < 0.01). Transparency positively moderates trust (r = 0.478, p < 0.01), while privacy risk demonstrates only a weak negative effect on trust (r = −0.152, p < 0.05) without influencing satisfaction. Digital literacy exhibits no significant moderating effect (r = −0.064, p > 0.05), indicating that contemporary FinTech interfaces have democratized AI benefits across literacy strata. The study validates three of five hypotheses and contributes novel context-specific evidence to the global FinTech personalization literature, offering actionable implications for practitioners and policymakers. Keywords: FinTech, AI personalization, data-driven marketing, customer experience, automation, trust, privacy risk, digital literacy

Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.

Abstract This study investigates the impact of AI-driven personalization on customer loyalty in the context of Indian e-commerce, integrating the Stimulus-Organism-Response (SOR) framework with Relationship Marketing (RM) theory. Drawing on survey data collected, the study explores how personalized stimuli—such as recommendations, and tailored interactions—affect psychological mechanisms like trust, engagement, satisfaction, and perceived value. It also examines the moderating role of privacy concerns in this process. The results reveal that while AI personalization does not directly influence loyalty, its effect is mediated through trust and engagement. High privacy concerns weaken these positive effects. This research contributes to theory by extending the application of SOR and RM in AI contexts and offers practical implications for designing AI strategies that balance personalization benefits with privacy sensitivities. The findings underscore the importance of emotional and cognitive responses in shaping long-term customer relationships in digital commerce.

Machine learning-driven personalization has emerged as a transformative approach in e-commerce, fundamentally reshaping how businesses interact with consumers. This research investigates the impact of machine learning algorithms on enhancing customer behavior, experience, and satisfaction within the digital marketplace. By analyzing extensive customer data, including browsing habits, purchase history, and preferences, machine learning enables e-commerce platforms to provide tailored experiences that resonate with individual consumers. This personalization not only streamlines the shopping process but also fosters deeper emotional connections between brands and customers. Findings indicate that businesses implementing machine learning personalization strategies experience notably increased customer engagement, higher conversion rates, and improved retention rates. As consumers increasingly demand tailored shopping experiences, our study highlights the need for e-commerce platforms to leverage advanced machine learning techniques effectively. Additionally, ethical considerations regarding data privacy and the balance between personalization and consumer trust are critically examined. Overall, this research underscores the significance of machine learning-driven personalization as an essential tool for e-commerce businesses aiming to enhance customer satisfaction and achieve competitive advantage in a rapidly evolving digital landscape.

This technical article explores the transformative impact of artificial intelligence on retail personalization, focusing on how advanced AI solutions like Amazon Personalize and fine-tuned language models are revolutionizing product recommendations and customer engagement. It examines a case study of an online fashion retailer that implemented a hybrid personalization system, combining recommendation algorithms with generative AI for dynamic content creation. The multi-layered architecture captures subtle behavioral signals, processes them through sophisticated recommendation engines, and delivers contextually relevant product suggestions with personalized descriptions. The article analyzes the significant business outcomes achieved through this implementation and details the technical considerations that organizations must address when building similar systems, including data pipeline architecture, model training strategies, privacy controls, and experimentation frameworks. The article concludes by exploring emerging frontiers in retail personalization technology, including multimodal recommendation systems that integrate visual and textual data, emotion-aware personalization that adapts to customer mood, and cross-channel personalization that creates consistent experiences across all touchpoints.

To improve user engagement and business, artificial intelligence (AI) facilitates personalized digital marketing. Standard systems frequently offer an insufficient experience because these rely on generic techniques and are very event-driven and rigid in a real-time setting. The technology provides dynamic contextualization in personalization through real-time data analysis and machine learning (ML). With the use of advanced algorithms like sentiment analysis and hybrid filtering, the system is able to deliver highly customized marketing messages, product recommendations, and content. According to the test results, important performance metrics including retention rate (30%), conversion rates (5.3%), and CTR (4.2%) have significantly increased. The proposed system shows better accuracy and ability to identify sentiments in personalization, with a 29% open rate and only 5% negative feedback, compared to earlier models. The results demonstrate that AI-based personalization is superior in that it improves customer satisfaction and yields better commercial outcomes.

Enhancing customer acquisition in e-commerce requires a strategic integration of trust, customer engagement, transparent data policies, and AI-driven experiences. This study explores how these factors collectively shape consumer decision-making and brand loyalty. The research objectives include, Examining the influence of trust and transparency on first-time purchases, Evaluating AI’s role in personalization and customer retention and Identifying engagement strategies that drive acquisition. A quantitative research design using Structural Equation Modelling (SEM) was employed to analyse factor relationships. Data was collected from a diverse sample of 400 e-commerce consumers, spanning different age groups and online shopping behaviours. Key findings indicate that AI-powered personalization and chatbot efficiency significantly enhance customer engagement. Price transparency and clear policies strongly influence consumer trust, while brand reliability plays a pivotal role in fostering long-term customer relationships. Although customer engagement has a notable impact, trust and AI-driven personalization emerged as the strongest drivers of acquisition. Based on these insights, the study recommends: Enhancing AI-driven chatbots for better customer interaction, Improving pricing transparency to build consumer confidence, Strengthening trust-building initiatives to drive long-term loyalty Limitations include a geographically restricted sample and potential self-reporting biases. Future research could explore industry-specific applications and cross-cultural differences in customer acquisition strategies, refining digital commerce models for sustainable growth.

AI in the digital business world means the use of artificial intelligence technology to improve efficiency, productivity, and customer experience in various operational and strategic areas of a company. The Focus of this study is to see customer thoughts on the implementation of artificial intelligence in the online shopping sector. This study will be carried out in 4 different phases, including; comprised of data searching, data inputting, data processing, and editing phase. This research data was processed using the SmartPLS (Partial Least Squares) application.  The study's findings indicate that artificial intelligence (AI) has profoundly transformed the manner in which e-commerce platforms engage with consumers. The results of our study demonstrate that the provision of effective AI services can significantly improve customer satisfaction. These findings underscore the importance of implementing AI in e-commerce marketing strategies, as by understanding customer preferences and providing relevant recommendations, companies can increase customer engagement and loyalty. This study underscores the significance of AI services in enhancing customer experience and paves the way for further research that can deepen our understanding of the interaction between technology and consumer behavior in the context of e-commerce

This article explores how artificial intelligence is transforming Customer Data Platforms (CDPs) by enabling enhanced personalization while maintaining privacy compliance. As organizations face mounting pressure to deliver personalized customer experiences amid stricter data protection regulations, AI-driven CDPs provide a crucial technological bridge. The article examines four key dimensions of AI-enhanced CDPs: identity resolution and profile unification, real-time personalization and predictive analytics, privacy-preserving technologies, and implementation architecture. Through analysis of current inquiry and industry practices, the article demonstrates how machine learning models improve customer identification across touchpoints, enable predictive capabilities beyond traditional segmentation, incorporate privacy by design through techniques like federated learning and differential privacy, and require thoughtful architectural and organizational strategies for successful deployment. By addressing both technological advances and implementation considerations, this article provides a comprehensive framework for understanding how organizations can leverage AI to enhance customer engagement while respecting and protecting privacy.

A fashion e-commerce company offers a wide range of products from domestic and international brands that are popular with young people. However, there has been an increase in non-organically acquired customers, many of whom do not return to make repeat purchases. This has led to a higher customer churn rate, with a significant proportion of non-organically sourced customers failing to become repeat purchasers. Consequently, a churn analysis and prediction model were developed to address this issue. This paper employs the Recency, Frequency, and Monetary (RFM) framework for churn analysis and prediction. The framework is underpinned by three key dimensions: last purchase recency, purchase frequency, and total transaction value. Seven machine learning algorithms were evaluated to identify the optimal approach. Following a comparative analysis of these models, Random Forest emerged as the superior algorithm, demonstrating an accuracy of 0.99, precision of 0.97, recall of 0.99, ROC AUC of 0.98, and F1-score of 0.97. Consequently, this model will be utilized for churn prediction. Based on the analysis and modelling, several recommendations are offered to enhance customer retention for the fashion e-commerce platform. In addition to predicting churn, this paper provides insights into potential refinements to the churn prediction model, such as real-time monitoring, personalized customer experiences, analysis of customer feedback, and lifetime value analysis.

: Retailers use artificial intelligence (AI) to serve customers better. This study examines the role of chatbots in shaping attitudes of customers relating to usefulness, usability, and trust when shopping for groceries online. Automated conversational agents, or chatbots, not only understand customers, but also provide them product knowledge, and promote behavioral change. Chatbots, automated and cost-e ff ective as they are, provide e ffi cient first-level support because a human employee cannot answer the whole range of customer questions round the clock. For Analysis, Structural Equation Modeling (SEM) with the AMOS analysis programme version 23 is used in this investigation. The online questionnaire was circulated via Google forms (N-375) promoted on social media to respondents who are at least 18 years of age and have completed online transactions, selecting them using a sampling quota in the survey procedure. A novel model will be tested and compared to prior research. According to the study’s findings, attitudes are significantly influenced by usefulness, usability, and trust, whereas attitudes have a major impact on decisions. However, it appears that trust has little impact on consumers’ views about online purchasing. This conclusion could be yet more support for previous research that consistently claims that trust a ff ects customer purchases. In the case of online purchasing, not only is the trust element important for customers to make a choice, but it also matters more than the attitude factor because attitude is a product of many di ff erent aspects. Significantly consumer confidence in internet retailers is the factor that most a ff ects attitude. The quantitative investigation revealed that a deep majority of respondents were apprehensive about employing AI and online-retail (e-retail) chatbots, primarily due to concerns about their accuracy and security, but would like to seek the advice of chatbots for informative purposes.

This study examines the factors influencing low purchasing decisions for furniture products in the digital era, specifically focusing on Web Atmospheric Cues, Customer Journey, Customer Trust, Product Quality, and Social Media Marketing. The research population consists of online furniture consumers in Banda Aceh, with a sample size of 155 individuals determined through indicator multiplication. Structural Equation Modeling (SEM) using IBM SPSS-AMOS software version 22 is employed for data analysis. The findings reveal that customer trust plays a significant role in Purchase Intention, with the highest coefficient of influence (0.296), followed by Product Quality (0.283) and Web Atmosphere Cues (0.256). Conversely, the impact of social media marketing on purchase intention is relatively lower (0.152). Notably, customer trust acts as a critical moderating variable in the relationship between product quality, web atmospheric cues, social media marketing, and purchase intention. These findings emphasize the importance of businesses prioritizing customer trust through exceptional customer service, transparent practices, and reliable product information.

The rapid growth of online fashion makes live-streaming essential, yet research often overlooks the impact of the streamer’s “try-on” service on the viewer’s purchase journey. Grounded in information foraging theory, this study explores how the try-on services facilitate viewers’ transition from product search to evaluation, ultimately shaping their shopping behavior. Data were collected from 466 Chinese fashion live-streaming viewers and analyzed using partial least squares structural equation modeling (PLS-SEM). Additionally, importance-performance map analysis (IPMA) was employed to derive practical implications from the findings. This study reveals that while streamer attractiveness has an indirect effect on purchase intentions through emotional engagement, co-viewer involvement directly influences both emotional engagement and purchase intentions during the search phase. Furthermore, the ability to virtually examine products proves more influential than social pricing in determining how viewers evaluate product information. This research advances the theoretical understanding of live-streaming commerce by illuminating the complex interplay between streamer characteristics, viewer dynamics and purchase behavior. For practitioners, it offers evidence-based guidelines for fashion streamers to enhance viewer engagement and conversion rates through optimized try-on demonstrations and co-viewer interaction strategies.

In the era of digital transformation, Digital Content Marketing (DCM) has become a key strategy for increasing B2B customer engagement. Telkomsel Enterprise uses various digital platforms to distribute educational content, product promotions, and case studies supporting the customer journey. However, challenges remain in ensuring that content effectively aligns with customer journey stages to maximize engagement and business outcomes. This research aims to analyze the effect of content–customer journey stage fit on content engagement and firm engagement, while testing the mediating role of content engagement and the moderating effects of social media usage frequency and organizational position. This research employed a quantitative approach using scenario-based online experiments. Data were collected from 420 B2B customers of Telkomsel Enterprise who had been exposed to the company's digital marketing content. Analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine direct, mediating, and moderating relationships. Results show that perceived content–journey fit significantly increases content engagement, which fully mediates its effect on firm engagement. Social media usage frequency strengthens the impact of content–journey fit on content engagement, while higher organizational position weakens the effect of content engagement on firm engagement.

In Indonesia rapidly expanding e-commerce sector, cultivating customer repurchase intention is paramount for sustained growth and competitiveness. This study investigates the critical factors influencing online repurchase decisions, focusing on the roles of customer experience and e-service quality, alongside hedonic motivation, with customer satisfaction and trust serving as mediating variables. Adopting a quantitative research design, data were collected from 202 Indonesian online shoppers via an online questionnaire and rigorously analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that customer experience, hedonic motivation, and e-service quality significantly enhance both customer satisfaction and trust in online retailers. Crucially, both satisfaction and trust are identified as strong positive predictors of repurchase intention. These results underscore the imperative for e-commerce platforms to prioritize a seamless and emotionally engaging customer journey, coupled with consistent high-quality e-service, to foster enduring customer relationships and drive long-term business sustainability.

Post-purchase behavioral intentions for virtual goods are a critical yet underexplored aspect of the customer journey, particularly in avatar-mediated environments like video games. Building on avatar identification and psychological ownership theories, this study investigates how the perception of key in-game elements—avatars and virtual items—influences consumer satisfaction, repurchase intention, and positive word-of-mouth. We collected and analyzed survey data from 439 online gamers using structural equation modeling. The findings reveal that avatar identification significantly boosts consumer satisfaction, repurchase intention, and word-of-mouth through the psychological ownership of virtual items. These results emphasize the importance of fostering strong associations between players, avatars, and virtual items to encourage post-purchase behaviors. For game developers, focusing on enhancing these associations can lead to increased player retention and revenue.

No abstract available

Research background: This paper analyzes the outcomes of an exploratory review of the current research on the relationship between the global adoption of mobile payment technologies, social interactive consumer-oriented applications, and online purchasers’ decision-making process. Purpose of the article: The data used for this study was obtained and replicated from previous research conducted by Econsultancy and Statista. We performed analyses and made estimates regarding mobile e-commerce sales worldwide, frequency of mobile retail app usage according to U.S. smartphone shoppers, how well organizations understand the customer journey for certain audiences, share of Internet users who are likely to use mobile payments on their smartphone in the next year (by country), and time spent per mobile app category. Methods: Data collected from 6,200 respondents are tested against the research model by using structural equation modeling. Findings & Value added: The advent of smartphones redesigns the routine of shopping, thus altering the agency of users. Retailers have instant access to data on the geographical position of users that can be employed in addition to other information to regulate the decision-making process. Perceived effortlessness in utilization of the smartphone is somewhat similar for various mobile shopping application settings. The degree to which mobile purchasing applications are time critical and location sensitive may differ substantially. Mobile retailers can make public the personal, collaborative, and instantaneous buying experience that mobile shopping can offer to customers.

Perceived service quality effectively influences consumers’ purchases and repeated purchases. How to improve the service quality and enhance the customer perceived value is not only a practical operational problem to be solved urgently by star-rated hotels, but also a hot topic that is closely concerned in the academic field. This paper used the structural equation modeling (SEM) analysis method to explore the impact mechanism of perceived service quality on online booking decisions of star-rated hotel customers, and proposed promotion strategies for online booking of star-rated hotels. The empirical analysis revealed that among all the factors that constitute the perceived service quality, safety (0.81) is the most important influencing factor, followed by remediation (0.79), empathy (0.78), and tangibility (0.53). Other factors in the categories of hotel characteristics, platform recommendation, and customer reviews influence online booking decision as well.

No abstract available

Providing high-quality services and delivering a positive customer experience has become critical to the success of any restaurant. The purpose of the paper to analyse the customer journey during fast food service delivery, establish a correlation between sensory marketing and the customer journey, and improve the interaction with touchpoints by comprehending the sensory influences that can be used to enhance the customer experience throughout the journey. The theoretical basis of the study is comprised of classical marketing theory and the customer-centric approach. Structural equation modelling, descriptive statistics, regression and correlation analyses were used as research methods. The empirical framework includes the results of an online survey with 303 respondents, who are regular customers of four most popular fast food restaurants in Baghdad (Iraq). SPSS V.26 software was used to analyse and process the data. The study found that there is a significant relationship between sensory marketing and the customer journey. The fast-food restaurants use sensory marketing techniques (visual, auditory, olfactory, and gustative) to enhance the customer experience and better manage the customer's journey during food ordering. In the fast food industry, gustative and olfactory marketing techniques have the most significant impact on the customer journey. It was also revealed that the interaction with touchpoints is influenced by the sensory response of the customer.

PurposeCustomer-to-customer (C2C) interactions substantially affect the overall service experience. This study attempts to provide a better and deeper understanding of C2C interactions during the customer journey in the banking industry. The study aims to investigate the complexities of these interactions and to detect their outcomes and further implications in banking services.Design/methodology/approachThis study used a sequential mixed-method approach. Firstly, semi-structured interviews were conducted to identify the components of C2C interactions during the customer journey. Subsequently, thematic analysis was performed to categorize the data and extract relevant components. Secondly, structural equation modeling was used to investigate the role of C2C interactions in behavioral outcomes.FindingsThe findings reveal that during the customer journey, C2C interactions plays a key role by providing information, managing queuing behavior, providing resources, and addressing issues related to other customers’ misbehavior. Additionally, C2C interactions have a positive direct effect on the customer experience, satisfaction, and loyalty. Customer experience, in turn, affects customer satisfaction and loyalty.Originality/valueThis study highlights the need for academic scholars to prioritize customers’ interaction during the customer journey in financial services, addressing a gap between industry directions and academic research in customer experience. Also, the findings help service providers develop effective strategies to enhance the customer experience by focusing on C2C interactions during the customer journey.

ABSTRACT With the expansion of the internet and the reduction of impediments in the supply chain, online apparel retailing is expanding its reach for unserved people. This growth in the online customer base has posed challenges for e-retailers to provide a better online customer experience (OCX) in the post-purchase stage and retain them. However, in online retailing, OCX in the post-purchase stage is imperative, as the customer can experience apparel only after delivery. This study intends to examine the role of post-purchase OCX dimensions of online apparel retailing in determining customer satisfaction and repurchase intention. An online survey approach has been adopted to collect 491 valid responses from customers who are well-versed in online apparel shopping. A proposed model has been tested using confirmatory factor analysis with structural equation modelling and analysis support, which validates the hypotheses drawn. Findings imply e-commerce decision-makers scrutinise critical post-purchase touchpoints for making websites customer-driven.

The purpose of this study is to examine how customers’ perceptions of the shopping process across a multi-stage electronic retail delivery system (e-RDS) affect overall satisfaction. This study explores the perceptions of process across four stages of e-RDS: search, agreement, fulfilment and after-sales service. This study also investigates how customers’ perception of one stage influences the subsequent stages. The data are collected from 341 online shoppers and analysed using structural equation modelling. This study suggests that customers’ perception of a particular stage in multi-stage e-RDS impacts the subsequent stages of service delivery. This study confirms that a smooth process experience at each stage has a positive impact on satisfaction and other factors, including perceived ease of use, perceived usefulness, perceived control and perceived flexibility. Interestingly, perceptions of process at the fulfilment and after-sales service stages are particularly crucial for customer satisfaction. This paper finds that it is equally important for e-retailers to enhance customer experience by focusing on process features in all four stages of e-RDS, in addition to product quality and price. This research distinguishes itself by examining how customers’ perceptions of the e-RDS process affect overall experience. This study is one of the few studies that address the significance of process dimensions in evaluating customer experience within the e-retail customer journey. By holistically studying multi-stage e-RDS from a process perspective, this paper offers detailed insights for e-retailers. Understanding process perceptions throughout the customer journey is crucial for shaping customer experience, ultimately leading to positive evaluation of customer satisfaction.

This study presents the interacting phenomena of perceptions of tourist destination online content (TDOC) and tourists’ behavioral intentions with a mediating role of tourists’ satisfaction, which is as yet under-explored in hospitality and tourism research. A model based on three main constructs, namely TDOC (with sub-constructs of online information quality and user-friendly accessibility), satisfaction, and tourists’ behavioral intentions [with sub-constructs of intentions to visit a tourist destination and electronic word-of-mouth (eWOM)], is presented to determine the growth of tourism business with the internet. Data were collected via a questionnaire-based survey from 413 tourists staying at hotels in Lahore city in Pakistan. Partial least square structural equation modeling was used to statistically analyze the gathered data. The findings indicate that tourists’ perceptions of TDOC directly influence their behavioral intentions, while tourists’ satisfaction exerts a mediating influence between tourists’ perceptions of TDOC and their behavioral intentions. Taking advantage of an economical and widespread online environment, destination marketing organizations could attract more tourists by fostering confidence in TDOC and positive eWOM to remain competitive in the long run. Important theoretical and practical implications are discussed.

No abstract available

ABSTRACT With the spread of m-commerce usage around the globe, mobile commerce can become the primary approach for many users in transactions. Such a method is considered an innovative way for the financial wallet but with some encumbrances. Trust in mobile commerce is one of the challenges yet to receive in-depth studies regarding linear and non-compensatory relationships. This study aims to evaluate the determinants of trust in mobile commerce using hybrid three-stage Fuzzy Delphi-Structural Equation Modeling (SEM)-Artificial Neural Network (ANN) approach. A hybrid approach was performed on 344 users mobile-commerce activities. The results of this study provide valuable insights for mobile commerce firms to come up with effective plans that increase customer trust in mobile commerce. Practically, the relative importance of specific determinants of trust in mobile commerce contributes to providing deep insight into the factors of customer trust in mobile commerce. The proposed model predicts 94.80% trust in mobile commerce with social presence and social support theories. A novel perspective was provided in this study to encourage e-marketing among consumers to persuade customers to trust mobile commerce.

"Grass-planting" marketing is an emerging content strategy in the era of new media. Against the backdrop where brand competition has expanded to consumers' choices, many new media practitioners effectively utilize the "grass-planting" model relying on the unique content ecology of online platforms such as RedNote. Many bloggers have established positive emotional connections with consumers by providing practical suggestions, shopping guides and other measures, which has effectively enhanced brand awareness and marketing effectiveness. This study considers the core mechanism of “Grass-planting” marketing of new media and the balance between the authenticity of product content and promotion needs as core issues. Aiming to combine the relevant theories of user-generated content (UGC), this study uses the case analysis method to analyse typical "grass-planting cases" on the RedNote platform. Through this, it explores the complex interactions among key opinion leaders (KOLs), key opinion consumers (KOCs) and ordinary consumers in the new media environment, clarifies the current situation and principles of "Grass-planting" marketing, and provides further strategic guidance on "Grass-planting" marketing for merchants and self-media.

Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of impression \rightarrow click \rightarrow conversion is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works. These methods alleviate the data sparsity problem for long-path sequential tasks as the positive feedback becomes sparser along with the task sequence. However, the error accumulation and negative transfer will be a severe problem for downstream tasks. Especially, at the beginning stage of training, the optimization for parameters of former tasks is not converged yet, and thus the information transferred to downstream tasks is negative. In this paper, we propose a prior information merged model (PIMM), which explicitly models the logical dependence among tasks with a novel prior information merged (PIM) module for multiple sequential dependence task learning in a curriculum manner. Specifically, the PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. Following an easy-to-difficult curriculum paradigm, we dynamically adjust the sampling probability to ensure that the downstream task will get the effective information along with the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and the online experiments also demonstrate the effectiveness of PIMM.

No abstract available

With the rapid proliferation of the internet and the widespread adoption of mobile devices, short video platforms have evolved into a central medium for digital content consumption, profoundly influencing daily life, social interaction, and commercial activities. Beyond their primary role as entertainment channels, these platforms have increasingly become critical tools for marketing, offering unique opportunities for financial institutions to engage potential customers. This study examines how short video platforms affect user conversion in financial product marketing, emphasizing the interplay between platform features, content presentation, and consumer behavior. Specifically, we analyze how algorithm-driven recommendations, personalized feeds, interactive engagement mechanisms, and visually compelling short-form content shape users' decision-making processes and trust perceptions, thereby influencing their likelihood of adopting financial products. Furthermore, the research investigates current promotional approaches on these platforms, including influencer collaborations, targeted advertisements, gamified campaigns, and educational content designed to simplify complex financial concepts. By assessing the effectiveness of these strategies in driving user engagement, retention, and conversion, the study identifies key factors that enhance the persuasive impact of marketing efforts. Building on these insights, the study proposes a set of optimization recommendations, such as leveraging data analytics for precise audience targeting, integrating storytelling techniques to strengthen brand narratives, and designing interactive elements that promote user participation and commitment. By combining empirical analysis with practical recommendations, this research provides a comprehensive framework for financial product marketing on short video platforms. The findings aim to guide financial institutions in crafting adaptive, user-centered marketing strategies that not only enhance brand visibility but also foster long-term customer relationships. Ultimately, this study contributes to a deeper understanding of how digital channels can be harnessed to optimize marketing performance, maximize user conversion, and respond proactively to evolving market dynamics in the financial sector.

When the competition in e-commerce rises, it is essential to ensure content is creative as well as effective in driving conversions. Using language generation from LLMs, we suggest a process that includes prompt engineering, multi-objective training and post-processing to make marketing texts engaging and effective for sales. We use a two-step process that applies sentiment modification, boosts diversity and adds calls to action. The way we test offline and online helps achieve a 12.5% increase in click through rate (CTR) and 8.3% increase in conversion rate (CVR). With this solution, creating texts can be automated and it will guide future improvements in live, customized user experiences.

  In the face of the challenges of intensifying competition in the rapidly growing world of e-commerce, this research focuses on investigating, identifying, and optimizing online marketing strategies to increase sales and support the growth of the e-commerce industry. This research aims to provide in-depth insights to online business owners by thoroughly understanding consumer behavior, technology trends, and digital market dynamics.This research adopts a mixed qualitative and quantitative approach by analyzing data from multiple sources. Customer surveys, sales data analysis, and case studies on successful e-commerce platforms provide the foundation for exploring consumer needs and preferences. Special emphasis is placed on assessing the sustainability of digital marketing strategies, utilization of social media, and implementation of the latest technologies in the e-commerce ecosystem.The research results are expected to provide comprehensive guidance for online marketing practitioners and e-commerce business owners. The findings not only include an in-depth understanding of consumer desires and market opportunities, but also provide valuable information on the effective use of social media, content strategies, and the latest ways to increase brand awareness, consumer engagement, and sales conversion. The use of technology is also emphasized as a key element in improving the competitiveness of e-commerce companies in the era of increasing digitalization.  

Abstract E-commerce platform occupies an important position in the modern economy, how to improve the precision marketing effect of the platform through online marketing has become the key to the development of enterprises. At the same time, the development of smart tourism services also puts forward higher requirements for personalized recommendations and precise marketing. The widespread application of big data technology and machine learning methods provides new opportunities, making it possible to optimize platform marketing and services through data-driven strategies. In this paper, we collect and process data from e-commerce users to construct an e-commerce user profile model. The model is used to accurately categorize users, and NSE precision marketing strategies are developed and implemented. Decision trees, random forests, support vector machines, and LightGBM algorithms are used to predict users’ purchasing behavior and interest preferences. Meanwhile, the smart tourism service model was used to generate a list of Top-K attractions as a recommendation list for users to provide personalized tourism services. The empirical analysis results show that the online marketing strategy based on these techniques can effectively improve the user conversion rate and increase the overall revenue of the platform, and the number of orders on the e-commerce platform after the use of the platform increased from 138 to 245, which is an increase of 77.62%. Furthermore, the level of personalization of smart tourism services has been significantly enhanced. After structural equation analysis, it can be seen that the standardized coefficients of the influence of smart tourism marketing experience on behavioral intention and perceived value are 0.136 and 0.193 respectively while the P-value is less than 0.05, which indicates that the smart tourism marketing experience has a positive influence on tourists’ behavioral intention and perceived value. The study shows that the combination of big data and machine learning provides a strong technical support for the optimization of e-commerce platforms and tourism services, and can help enterprises to achieve the precise marketing objectives and the enhancement of user service experience in the changing market environment.

The research objective of this work will explore the means by which the online consumers in China, could be persuaded to translate their purchase intentions into action; through improving the users’ experience design of website interfaces for the cultural and creative products. This work combines GT with Quality Function Deployment (QFD) research method. The research also looks at the role of web page styles in the Conversion Rate Optimization (CRO) for online advertising of cultural and artistic goods. This is through a series of interviews and a detailed study of the target client group, together with the analysis of the available cases, in an attempt to ascertain the most effective ways to improving the online conversion rates of cultural and creative products. The implications for marketing are pointing out that improved strategies must aim at decreasing user frustration while using a website, at increasing the want/need desire of a user, at increasing website utility, and at moderately restricting the physical requirement on the consumer when it comes to using the website to go through web pages. Thus, it lays a reference reference and theoretical framework for innovative web interface design of cultural and creative products in China.

In the context of the deep development of the digital economy, artificial intelligence (AI) technology has become the core driving force for promoting the transformation and upgrading of online marketing. This article focuses on the practical field of AI technology empowering online marketing. Firstly, it outlines five key technical systems: data collection and processing, user insights and analysis, marketing content generation and optimization, precision delivery, and customer relationship management; Furthermore, the prominent issues of AI marketing in terms of technological stability, application adaptability, management mechanism, and ethical standards were analyzed; The ultimate goal is to build a four in one improvement path of "technical optimization - application adaptation - management upgrade - ethical standards". The research aims to provide theoretical support and practical reference for enterprises to overcome the bottleneck of AI marketing implementation and improve application efficiency, promoting the development of the online marketing industry towards precision, compliance, and high quality.

The rapid advancement of artificial intelligence (AI) has emerged as a transformative force in online marketing, reshaping how businesses engage with consumers. This study explores the impact of AI-driven chatbots and intelligent automation strategies on consumer behavior, analyzing their effectiveness in enhancing user experience, and personalizing interactions of consumers. This paper analyzes the effectiveness of chatbots in driving customer engagement, improving conversion rates, and fostering brand loyalty. Additionally, it examines the ethical considerations and challenges associated with AI-driven marketing automation. The integration of artificial intelligence (AI) in online marketing has revolutionized consumer interactions, particularly through chatbots and AI-driven customer support. This study examines the impact of AI-based technologies on consumer behavior, with a specific focus on how chatbots, artificial intelligence, and AI-driven customer support (independent variables) influence consumer behavior and consumer trust (dependent variables). Additionally, the study explores the moderating role of demographic factors in shaping consumer responses to AI-driven marketing strategies. By analyzing real-time engagement, personalized experiences, and automated assistance, this research highlights the effectiveness of AI in enhancing customer satisfaction and brand loyalty. Findings suggest that businesses leveraging AI-based marketing strategies can achieve higher engagement rates, improved customer retention, and enhanced operational efficiency. However, the success of such implementations depends on balancing automation with human touchpoints to maintain authenticity and emotional connection. This study provides valuable insights for marketers, business leaders, and AI developers, offering recommendations on optimizing chatbot functionality and automation strategies for a more seamless and consumer-centric digital experience.

As social media's role in tourism decision-making becomes increasingly prominent, the field of tourism marketing urgently requires a scientific input-output evaluation framework and decision support system. Focusing on the core characteristics of tourism social media marketing, this study systematically constructs an input-output model incorporating a multidimensional indicator system and dynamic functional relationships. By defining quantitative dimensions for core elements such as content, interaction, data, and technology, it proposes a comprehensive theoretical framework covering resource input, value conversion, and feedback optimization. Building upon this foundation, the study further designs a situational diagnosis method based on model outputs, a resource adaptation strategy generation mechanism, and a data-driven closed-loop decision path, forming a complete methodological system from theoretical construction to practical application. This provides a theoretical basis and practical guide for tourism enterprises to achieve optimal allocation and dynamic adjustment of marketing resources in complex social environments.

Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts. Compared with traditional conversion uplift modeling,revenue uplift modeling exhibits higher potential due to its direct connection with the corporate income. However, previous works can hardly handle the continuous long-tail response distribution in revenue uplift modeling. Moreover, they have neglected to optimize the uplift ranking among different individuals, which is actually the core of uplift modeling. To address such issues, in this paper, we first utilize the zero-inflated lognormal (ZILN) loss to regress the responses and customize the corresponding modeling network, which can be adapted to different existing uplift models. Then, we study the ranking-related uplift modeling error from the theoretical perspective and propose two tighter error bounds as the additional loss terms to the conventional response regression loss. Finally, we directly model the uplift ranking error for the entire population with a listwise uplift ranking loss. The experiment results on offline public and industrial datasets validate the effectiveness of our method for revenue uplift modeling. Furthermore, we conduct large-scale experiments on a prominent online fintech marketing platform, Tencent FiT, which further demonstrates the superiority of our method in real-world applications.

This study aims to develop and evaluate a deep learning-based autonomous intelligent system for customer behavior prediction and marketing strategy optimization in the retail sector. A hybrid architecture combining Long Short-Term Memory (LSTM) networks with Transformer models in a multi-task learning framework was designed. Evaluation included offline cross-validation and online A/B testing using 1.5 million customer interactions, followed by a 12-month case study implementation in a multinational e-commerce platform. The model achieved a 15% increase in AUC-ROC for purchase prediction and a 22% improvement in Mean Average Precision for product recommendations compared to state-of-the-art benchmarks. The case study revealed substantial enhancements in click-through rates (35%), conversion rates (28%), and customer retention (22%). The hybrid LSTM-Transformer model with a multi-task learning framework significantly outperforms traditional methods, demonstrating the effectiveness of deep learning for customer behavior prediction and marketing optimization. Retailers can leverage this system to enhance personalized recommendations, optimize pricing strategies, and improve customer engagement, resulting in measurable business performance improvements across diverse retail segments.

Under the concurrent waves of educational digitalization and short video popularization, primary school short video education has emerged as a crucial pathway for the digital transformation of basic education. However, this sector faces significant challenges, including superficial knowledge dissemination, low course conversion rates, and a disconnect between marketing and teaching objectives. This study examines scenario-based marketing as an innovative approach that centers on user needs and scenario construction to address these challenges. By aligning with primary school students' cognitive characteristics and parents' educational expectations, this model enables dual empowerment of knowledge transfer and course conversion efficiency. Drawing upon constructivist learning theory and experiential marketing principles, this research conducts a systematic analysis of the compatibility between scenario-based marketing and primary school short video education. The study identifies and analyzes critical issues in the current integration process, including fragmented knowledge delivery, homogeneous scenario construction, and incomplete marketing feedback loops. To address these challenges, we propose a comprehensive improvement framework based on four key pillars: precise scenario positioning, scenario-based content creation, full-link scenario penetration, and data-driven optimization. This research contributes valuable theoretical insights and practical guidelines for primary school short video education providers, offering a roadmap to overcome development obstacles and achieve simultaneous enhancement of knowledge dissemination effectiveness and course conversion rates. The findings have significant implications for educational institutions seeking to optimize their digital learning platforms and marketing strategies in the evolving landscape of primary education.

Abstract Artificial intelligence (AI) has emerged as a transformative force in digital and performance marketing, enabling data-driven decision-making, real-time optimization, and measurable performance outcomes. While digital marketing focuses on customer engagement across online platforms, performance marketing emphasizes accountability through metrics such as click-through rates, conversion rates, customer acquisition cost, and return on investment. This study empirically examines the impact of AI on digital and performance marketing using a secondary-data-based research design, drawing evidence from academic literature, industry reports, and AI-enabled advertising platforms. The findings indicate that AI-driven personalization, predictive analytics, automation, and real-time optimization significantly enhance marketing efficiency and effectiveness. The study also discusses ethical challenges related to data privacy, algorithmic bias, and organizational readiness. By providing empirical insights and a structured analytical framework, this paper contributes to the growing body of research on AI-driven marketing and offers practical implications for marketers and technology-driven organizations. Keywords: Artificial Intelligence, Digital Marketing, Performance Marketing, Predictive Analytics, Marketing Automation, Empirical Study

This study analyse the effectiveness of online marketing through search engine advertisements on Google and Bing among consumers in Chennai. With the fast growth of digital marketing, companies are increasingly relying on search engine ads to reach potential customers. Using a combination of quantitative and qualitative methods, the study analyses data from various campaigns run on both platforms, alongside consumer interviews and surveys. The results of this study indicate significant differences in user demographics, ad engagement, and conversion effectiveness between Google and Bing (Xing & Lin, 2006). Google ads determine a broader reach and higher engagement rates, whereas Bing ads show higher conversion rates among the audience. The findings provide valuable information for marketers seeking to optimize the search engine advertising strategies in Chennai also it contributes to the growing knowledge on digital marketing effectiveness and offers recommendations for companies aiming to enhance their online advertising efforts.

Under the accelerated evolution of the digital marketing environment, cross-channel marketing budget allocation faces challenges such as complex interaction effects between channels and lagging dynamic responses. Traditional methods rely on empirical rules or static optimization models, making it difficult to capture the causal relationship between customer behavior paths and the time-varying nature of the market. To address this, a hybrid intelligent decision support framework is proposed, integrating a causal discovery algorithm with deep reinforcement learning. First, a constraint-based causal network is constructed to analyze historical marketing data and identify the causal topological structure of the channel conversion path. Second, a hierarchical deep deterministic policy gradient algorithm is designed, which encodes the causal network as a priori constraint in the state space and quantifies the balance between causal effect and immediate reward through a dynamic reward function. Results show this method improves cross-channel ROI by 18.6% and budget allocation efficiency by 23.4% over traditional dynamic programming. Furthermore, causal constraints enhance strategy robustness by 31.2% in volatile markets. Compared with a single deep reinforcement learning model, it increases long-term customer retention by 14.9%. In summary, by jointly optimizing causal discovery and deep reinforcement learning, this study effectively resolves the conflict between dynamic adaptation and interpretability in marketing budget allocation and provides a novel paradigm for digital decision support in complex business scenarios.

In the era of digital transformation, artificial intelligence (AI) has become a strategic enabler in digital marketing management, particularly for optimizing online sales performance. This study explores the roles of key AI functionalities—predictive analytics, conversational AI, and personalization engines—across different stages of the digital marketing funnel. The research is motivated by the growing importance of AI in enhancing customer engagement, targeting precision, and conversion optimization, while also recognizing the challenges firms face in adoption, including resource constraints, ethical concerns, and regulatory issues. The study employed a qualitative literature review, systematically analyzing and synthesizing findings from academic journals, books, industry reports, and empirical case studies published within the last five years. The analysis shows that predictive analytics significantly improves targeting efficiency and click-through rates at the awareness stage, conversational AI enhances engagement and conversion by delivering responsive and personalized interactions, and personalization engines optimize purchase decisions by increasing conversion rates and average order value (AOV). Findings also highlight that AI tools are most effective when integrated into a holistic framework, rather than applied in isolation. The research offers a conceptual model linking AI tools to measurable sales performance metrics and provides implementation guidelines tailored to different organizational contexts, including large enterprises, SMEs, and firms in emerging markets. By integrating AI into digital marketing strategies, firms can achieve not only operational efficiency but also sustainable competitive advantage.

The object of this study is the strategy of online retail marketing campaigns, particularly in the context of utilizing a modified ID3 decision tree algorithm to improve predictive effectiveness regarding consumer responses. It addresses challenges in audience segmentation, campaign evaluation, and market adaptation, while also tackling technical issues such as overfitting, prediction errors, and data imbalance. These challenges often hinder businesses from accurately identifying and targeting potential customers, leading to inefficient marketing strategies and resource allocation. The dataset was split into 80:20 and 70:30 ratios, and the model was tested across decision tree depths from max_depth 1 to max_depth 20. The highest accuracy occurred at max_depth 6, ensuring optimal computational efficiency. However, increasing tree depth led to declining accuracy and rising computational costs, highlighting the risk of overfitting. Key factors influencing consumer response include income, education level, and recent company interactions. These variables help determine purchasing behavior and engagement levels, making them crucial in refining marketing strategies. Class imbalance introduces bias, affecting model performance by favoring the majority class while underrepresenting minority groups. The modified ID3 model outperforms ID3 Shannon, offering better precision for the majority class but lower recall for the minority class. Limiting campaign offers to one or two improves consumer responsiveness and prevents information overload. A data-driven marketing strategy ensures promotions align with consumer preferences and market trends. The developed model enables businesses to better target campaigns, increase conversion rates, and optimize resource allocation, ensuring an effective balance between tree depth and model accuracy

In today's digital landscape, businesses must allocate online resources efficiently. Data-driven AI methods are increasingly adopted for customer journey management. This study enhances existing frameworks with three key propositions, integrating deep learning and optimization to create a three-step revenue optimization model using online customer data. First, we apply K-means clustering to analyze online user data, constructing a behavior model. Then, convolutional neural networks (CNN) and long short-term memory (LSTM) networks predict user behavior and conversion values from sequential data. Finally, the heuristic algorithm optimizes revenue within budget constraints based on conversions. From an academic perspective, our study provides an empirical, theory-grounded model for service and marketing management. Technologically, we identify three key findings: stacking LSTM with CNN effectively processes sequential online user data, outperforming traditional machine learning methods; optimization methods and decision trees improve model interpretability and address marketing attribution challenges by understanding user behavior and channel impacts; and traditional integer programming models fall short in solving high-dimensional online channel planning problems, necessitating heuristic algorithms. Our model aids companies in setting online channel standards and budgets, offering valuable insights and practical guidance to decision-makers.

The continuous iteration of digital technology and the popularization of technologies such as 5G and artificial intelligence have driven structural changes in consumer behavior, and traditional marketing models are no longer suitable for the decision-making logic of the new consumer era. Based on the public data of China Internet Network Information Center (CNNIC), iResearch Consulting and other authoritative organizations, this paper systematically analyzes the characteristics and driving forces of consumer behavior change in the digital context, focusing on content adaptation, channel collaboration, experience optimization and data compliance dimensions, building a digital marketing practice path that conforms to the law of behavior change, and verifying the feasibility of the path with real business cases in multiple industries. Research has found that the socialization, rationalization, and personalization of consumer behavior have forced digital marketing to shift from "traffic harvesting" to "value resonance". This study can provide ideas for solving the pain points of high customer acquisition costs and low conversion efficiency in enterprises, and help promote the high-quality development of the digital marketing industry.

No abstract available

Digital advertising activities include various strategies and tactics carried out online to promote products, services, or brands, such as creating weekly promotional advertisements, influencer marketing, website marketing, interactive content creation, weekly content creation, and e-commerce. The digital advertising activity process implemented by the marketing communications sub-division has different paths. Marketing communications in marketing Bulog products plays an important role from the beginning of the pre-production process to post-production in digital marketing activities. Technical barriers are obstacles that originate from supporting tools in carrying out digital advertising such as the equipment and facilities used. This research aims to analyze the use of digital marketing carried out by Perum BULOG in marketing products effectively. The data collection techniques used were active participation, in-depth interviews, and literature study through journals and e-books. The object of this research is marketing communications activities carried out by the marketing communications sub-division. This type of research uses descriptive qualitative methods and uses AISAS theory. This research found that the implementation of digital marketing communications carried out by Perum BULOG used social media, Instagram and websites, to create content and promote products.

Direct message is an Instagram feature that allows customers to communicate more intimately with companies. Direct Messages are a feature of Instagram that customers can access to interact more closely with brands. By using the principle of micro interaction in one of the features on Instagram, it is possible to increase brand loyalty through customer interaction. As a social media marketing tool, Instagram is the right strategic platform for businesses, including beauty businesses, which allows for in-depth interaction with consumers regarding the products and services offered by beauty clinics. The aims of this research is to determine the brand loyalty of a beauty clinic account which is measured using social marketing activities through online consumer engagement. The research method used a causality-associative approach. An explanative survey by questionnaire distributed to 400 respondents chosen using purposive sampling was conducted. This research used path analysis and the result of this research concluded that all variables correlated positively and strongly with amount of correlation coefficient 0.852 and $72.6{{\% }}$ simultaneous effect $({{R}^2})$. Therefore, the relationships among variables are positive, and that all intensely correlate to each other very intensely. The overall results conclude that brand loyalty will increase significantly by optimizing social media marketing activities efficiently and integrating them with active customer engagement via social media, especially Instagram.

Increased activity in Indonesia’s online learning market has forced companies to compete more fiercely for the attention of recent graduates and young workers. However, many emerging platforms still face challenges in building brand visibility, credibility, and conversion effectiveness, despite the extensive use of social media for information search and learning evaluation. This study aims to develop an integrated digital marketing strategy for Graduats by analyzing customer preferences toward Social Media Marketing Activities (SMMAs) and relevant elements of the 7P service marketing mix, as well as how these factors influence purchase intention. A survey-based quantitative method was utilized, focusing on individuals between 18 and 30 years old to align with the primary demographic of Graduates. The findings reveal that social media content significantly influences purchase intention, particularly when it communicates credibility, perceived learning value, and user engagement. Instagram is identified as the most dominant platform for both social interaction and upskilling-related information search. Moreover, the combined influence of entertainment, interactivity, trendiness, and e-WOM explains 81.3% of the variance in purchase intention, emphasizing the strong role of content-driven strategies. Based on these results, this study proposes an integrated digital marketing framework comprising an SMMA-based content strategy and an implementation design using the RACE model. This research provides managerial implications for optimizing social media marketing investments and contributes academically by extending the application of SMMA to Indonesia’s online upskilling sector.

Background and Aims: The property industry has shifted toward digital platforms as consumers increasingly search for properties online. This change has driven companies to adopt more structured and measurable digital marketing strategies. This study aims to analyze the implementation and effectiveness of digital marketing strategies employed by a property company in Yogyakarta, specifically for the Vilas Palagan project, and to identify the factors hindering sales conversion. Study Design: This research adopts a qualitative approach with a case study design. Place and Duration of Study: Vilas Palagan Project, Yogyakarta, Indonesia, between January 2024 and May 2025. Methodology: Data collection involved observations of digital marketing activities, in-depth interviews with key informants (including the Head of Estate Management and Marketing communication staff), and comprehensive documentation analysis. The collected data were examined using thematic analysis to identify patterns in digital engagement and sales obstacles. Results: The findings indicate that while the use of social media, digital advertising, and online property platforms significantly increases brand awareness and generates potential leads, effectiveness is limited by fragmented platform integration and a heavy reliance on specific visual content. Challenges also persist in maintaining consistent consumer engagement throughout the long property-buying cycle. Conclusion: The study concludes that optimizing customer data utilization, such as CRM system, enhancing campaign personalization, and improving the coordination between marketing and sales teams are critical for increasing lead quality and boosting final sales conversion in the digital era.

The purpose of the article is to substantiate the role of strategic content marketing management in increasing the brand’s market value and to develop a system of indicators to evaluate the effectiveness of content management as an intangible asset of the enterprise. The article explores the strategic role of content marketing in increasing the brand’s market value in the digital economy. Content is considered a strategic intangible asset of the enterprise, capable of generating long-term economic value. The key characteristics of an effective content strategy are defined, including quality, relevance, systematicity, and analytical manageability, which shape customer lifetime value and consumer retention rate and reduce the costs of attracting new customers. The feasibility of applying a cyclical model of strategic content management is substantiated, which includes planning, creation, distribution, and analysis of the economic response to content, as well as the author’s “trust funnel” model, which describes the sequence from brand awareness to advocacy and monetization of consumer trust. The evaluation of content marketing effectiveness is conducted by integrating ROMI, Retention Rate, and CAC metrics, enabling enterprises to make scientifically grounded decisions about content investments and build brand capital. The scientific novelty of the article lies in formulating a conceptual approach to strategic content management as an investment asset and developing a methodology for evaluating its effectiveness. The practical significance of the results is reflected in recommendations for enterprises to develop a content strategy to increase the brand’s market value and optimize marketing costs. Prospects for further research include scaling the achieved results through paid channels, particularly PPC (Pay-per-Click) and programmatic solutions, thereby combining the organic and paid effects of content marketing to maximize ROI. Keywords: content marketing, strategic management, brand capitalization, digital marketing, intangible assets, LTV, cost-effectiveness.

In the era of digital transformation, training institutions are required not only to attract new participants but also to build long-term relationships. Funnel marketing is an effective strategy, directing potential participants from awareness to becoming loyal customers. This study explores the application of funnel marketing to increase customer lifetime value (CLV) at LKP Syntax Training Centre, a certified training institution in Indonesia. Using a blended approach with a case study design, data was collected through digital marketing and management team interviews, promotional documentation analysis, and CRM data related to conversions, retention, and repurchases. The results show that at each stage of the funnel—awareness through educational content and digital advertising, interest through webinars and email nurturing, decisions through certification promo programs, and retention through loyalty programs and communication automation—can increase CLV, with retention being the most crucial stage, contributing 35% to repeat revenue. Research confirms that a systematic funnel marketing strategy can create a more meaningful participant experience, encourage repurchase, and extend relationships with institutions. Practical implications include strengthening content in the early stages to attract relevant participants as well as leveraging automation and data analytics to maintain loyalty. These findings are expected to be a strategic reference for other training institutions in optimizing digital marketing with a long-term impact.

The article explores the impact of digital technologies on marketing effectiveness in the banking sector and presents an enhanced evaluation method – Digital Maturity & Impact (DMI). The method combines an assessment of digital maturity, analysis of technology-driven consumer behavior, and evaluation of marketing strategy performance. Its application enables the identification of key effectiveness factors and the design of strategies to improve digital tools. Findings show that banks with higher digital maturity achieve stronger conversion, customer retention, and return on investment, with practical recommendations for optimizing digital marketing.

No abstract available

In the context of the ever-increasing digital transformation of the economy, traditional systems for assessing the effectiveness of marketing activities clearly demonstrate growing methodological limitations. The relevance of the study is due to the growing gap between the speed of change in marketing practices and the increasingly noticeable inertia of the classical methods used to assess them, which do not take into account such key aspects of the digital environment as audience engagement, the effectiveness of online channels and the quality of customer experience. The purpose of this article is to develop an improved methodological approach to assessing the effectiveness of marketing, integrating proven classical indicators with relevant metrics of the digital era. The study used a set of general scientific and special methods, including systemic, comparative and structural-functional analysis, economic and mathematical modeling, the method of case-study and expert assessments. The main result of the study was the developed system of indicators, including the coefficient of digital presence, the index of seamless customer experience and a modified formula for calculating the competitiveness of marketing activities (CCM_digits). Practical testing has shown that the proposed methodology allows you to identify hidden problems and growth points that are invisible when using traditional approaches, providing a balanced assessment that combines financial results with indicators of digital maturity and customer centricity.

Abstract The paper explores the transformation of sales funnel levels under the influence of digital trends in marketing, emphasizing the relevance of optimizing customer interaction in a rapidly changing digital environment. The purpose of the study is to refine theoretical and methodological approaches to understanding the sales funnel and to justify the necessity of its transformation according to digitalization processes. The research applies system analysis, generalization, comparative and content analysis, as well as modeling methods to study customer movement through the sales funnel and to visualize key interaction stages. The Ukrainian market situation during 2022-2023, marked by increased demand for energy equipment, illustrates the need for adaptive marketing tools. The study proposes a Customer Journey Map model for an online wholesale company as a practical example of sales funnel optimization. The findings substantiate that digital tools and CRM systems enhance communication efficiency, adaptability, and sustainable business development under the conditions of global digital transformation.

ABSTRACT Background The rise of Artificial Intelligence (AI) has significantly transformed the landscape of digital marketing, enabling brands to create hyper-personalized customer experiences. This shift is driven by the increasing need for businesses to engage effectively with consumers across various digital platforms. Purpose The purpose of this paper is to explore how AI-driven personalization enhances customer experiences in digital marketing, focusing on its impact on consumer engagement, brand loyalty, and overall marketing effectiveness. Objective The objective is to analyze the integration of AI technologies, such as machine learning, natural language processing, and predictive analytics, in creating tailored marketing strategies that resonate with individual customer preferences.

The rapid integration of artificial intelligence (AI), machine learning (ML), and advanced marketing technology platforms represents a fundamental transformation in how organizations engage customers across digital ecosystems. This research examines the convergence of AI-driven technologies and marketing technology (MarTech) stacks, analyzing their impact on marketing effectiveness, customer lifetime value, and organizational ROI. Through comprehensive analysis of data from leading consulting firms (McKinsey, Gartner, Forrester, Deloitte), market research organizations, and empirical industry studies, this paper demonstrates that organizations leveraging integrated AI-MarTech solutions achieve significantly higher performance metrics: 20-30% marketing ROI increases, 287% higher purchase rates, and 2.5x greater likelihood of exceeding revenue goals. The paper further explores emerging technologies including privacy-first marketing strategies, Customer Data Platforms (CDPs), conversational AI, predictive analytics, and omnichannel integration. Our findings indicate that the global MarTech market will reach $2.86 trillion by 2034 (CAGR 18.6%), with Customer Data Platforms experiencing 39.9% annual growth. The research concludes that successful digital marketing transformation requires integrated approaches combining AI automation, first-party data strategies, omnichannel orchestration, and privacy- compliant personalization. This study contributes to understanding how technology-driven marketing strategies can create sustainable competitive advantages in an increasingly data- driven business environment.

The article presents methodological recommendations for assessing digital marketing effectiveness based on the experience economy. It addresses the growing importance of users' emotional responses to digital marketing. The proposed methodology uses an integral impression assessment indicator formed through expert analysis for comprehensive campaign evaluation. It identifies optimal indicators for assessing impressions across websites, social networks, and advertising platforms. The decision tree method systematizes management decisions while considering various factors. The system enables dynamic monitoring and quick response to digital environment changes. These recommendations help enterprises allocate marketing budgets more effectively and adjust strategies while increasing personalization and improving user experience. Implementation improves digital marketing results and builds long-term customer loyalty.

Customer segmentation is essential to successful digital marketing because it allows businesses to customize their strategies for a variety of consumer groups. Nevertheless, conventional segmentation techniques frequently depend on static demographics and scant behavioral insights, which limit their ability to adapt to changing market conditions. This paper examines the employ of Machine Learning (ML) methods for predictive customer segmentation, emphasizing how data-driven models can improve campaign effectiveness and personalization. The focus is on both supervised and unsupervised learning approaches, such as clustering algorithms like Weighted Multi-View Evidential Clustering (WMVEC) as well as predictive models like Quantum Kernel Self-Attention Networks (QKSAN). ML models can uncover hidden patterns and make highly accurate predictions about future behavior by examining client behavior, purchase history, and engagement patterns in real-time. By using SPSS software, correlation and t-test analyses are conducted to assess the relationships between variables and validate the effectiveness of segmentation. According to our research, predictive segmentation helps businesses better meet the changing needs of their customers while also boosting personalization and customer happiness. The article ends with a framework for applying segmentation tactics powered by machine learning in contemporary digital marketing ecosystems.