International Review of Artificial Intelligence in Marketing
The International Review of Artificial Intelligence in Marketing (IRAIM) (e-ISSN: 0000-0000; p-ISSN: 0000-0000) is a scientific journal dedicated to advancing research in artificial intelligence (AI) applications in marketing, consumer behavior, and digital business strategies. We are proud to announce that International Review of Artificial Intelligence in Marketing has achieved a significant milestone by being officially indexed in SINTA X, under the decree of the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia No: xxx/E/KPT/202x. Published by the Fakultas Sosial Ekonomi & Humaniora, Universitas Nahdlatul Ulama (UNU) Purwokerto in collabration with the Business, Technology, and Sustainability Research Institute (BTSRI), this journal covers a broad range of research topics, including but not limited to machine learning in marketing, AI-driven customer analytics, predictive marketing, chatbots and virtual assistants, AI-powered advertising, personalization and recommendation systems, sentiment analysis, ethical AI in marketing, and emerging trends in AI-driven business strategies. Articles in this journal are published biannually in January–June and July–December, ensuring the timely dissemination of new research findings and innovative AI marketing applications.
Fakultas Sosial Ekonomi & Humaniora, Universitas Nahdlatul Ulama (UNU) Purwokerto
Measuring Cross-Channel Ad Synergy with Machine Learning: TV, Social, and Search Integration
This article examines how cross-channel ad synergy is currently measured when TV, social, and search are integrated in a single campaign, and what role machine learning plays in that measurement. In increasingly fragmented media environments, advertisers suspect that exposures across channels interact rather than simply add up, but existing evidence and tools are scattered and often channel centric. The study conducts a systematic literature review of peer-reviewed articles published between 2019 and 2023, focusing on research that applies machine learning or advanced data-driven methods to multi-channel advertising settings. The reviewed studies are coded by channel configuration, data sources, modelling approach, treatment of synergy or interaction effects, and outcome metrics. The findings show that machine learning is widely used for targeting, prediction, and multi-touch attribution, but that explicit modelling of synergy among TV, social, and search is rare, often methodologically fragile, and highly context dependent. The article concludes by outlining a research agenda for causal, interaction-aware machine learning frameworks for cross-channel ad synergy.
Strategic Positioning Through AI-Based Customer Insights: A Resource-Based View Perspective
This article examines how AI-based customer insights contribute to strategic positioning when viewed through the lens of the resource-based view. In increasingly data-rich markets, firms rely on AI and advanced analytics to understand evolving customer needs, yet existing research often treats these tools as operational rather than strategic assets. This study conducts a systematic literature review of peer-reviewed articles published between 2019 and 2023 to synthesize how AI-enabled customer insight capabilities are conceptualized, how they are linked to firm-level capabilities, and under what conditions they support positional advantage. The review codes studies by theoretical lens, operationalization of AI-based customer insights, and outcome variables, with particular attention to segmentation, differentiation, and value proposition design. The findings indicate that AI-based customer insights are best understood as higher-order marketing capabilities that enhance market sensing and resource orchestration, but that empirical work rarely measures strategic positioning explicitly and is highly contingent on complementary organizational structures, culture, and alignment with business strategy.
Determinants and Strategic Pathways of AI Integration in SME Marketing Practices
This article investigates how small and medium enterprises (SMEs) integrate artificial intelligence (AI) into their marketing practices and what determinants shape different strategic pathways of integration. It asks to what extent technological, organizational, and environmental factors enable SMEs to move beyond tool level adoption toward more capability based and strategic use of AI in marketing. Using a systematic literature review of peer reviewed studies published between 2018 and 2023, the study synthesizes evidence on AI applications such as AI enhanced CRM, chatbots, recommendation engines, and customer analytics. Descriptive mapping is used to summarize contexts, methods, and types of AI use, while thematic analysis identifies recurring determinants and clusters of integration patterns. The review finds that perceived benefits, digital infrastructure, leadership support, skills, and ecosystem pressures jointly shape whether AI remains experimental or becomes embedded in SME marketing routines. It also highlights important methodological and contextual gaps and outlines future research directions on longitudinal dynamics, performance outcomes, and governance issues in AI enabled SME marketing.
Holistic Customer Understanding Through Multimodal Artificial Intelligence in Marketing Analytics
This article examines how multimodal artificial intelligence (AI) contributes to holistic customer understanding in marketing analytics. It asks to what extent combining text, images, and other data modalities moves firms beyond narrow, touchpoint-specific predictions toward richer views of customer experience and behavior. Using a systematic literature review of peer-reviewed studies published between 2019 and 2023, the study identifies convergences and gaps in current applications of multimodal AI to sentiment analysis, recommendation, and behavioral prediction. Descriptive mapping summarizes dominant contexts, modality combinations, and model families, while thematic synthesis explores how studies conceptualize and operationalize “customer understanding”. The review finds that multimodal models consistently outperform unimodal baselines and capture nuanced affective and contextual cues, but remain concentrated on single tasks and public data sources, with limited integration into customer journey or CRM frameworks. Methodological limitations and reporting heterogeneity are highlighted to qualify the strength of existing evidence. Future research directions are outlined to embed multimodal analytics within longitudinal, cross-channel, and ethically governed approaches to customer insight.
AI-Enhanced Loyalty Programs: Personalized Rewards and Their Effects on Long-Term Customer Value
This article examines how AI-enhanced loyalty programs use personalized rewards to influence long term customer value, with a particular focus on customer lifetime value (CLV). The study conducts a systematic literature review of peer reviewed journal articles published between 2019 and 2023, addressing the question of how artificial intelligence, machine learning, and advanced analytics are applied to design, personalize, and evaluate loyalty programs. Screening and coding procedures across major databases identify empirical and conceptual studies that link AI driven personalization with outcomes such as satisfaction, loyalty, and financial value. The review finds that loyalty programs are evolving into dynamic, data intensive systems where AI supports continuous segmentation, reward optimization, and CLV based decision making, while personalization of rewards is a key driver of engagement when customers trust data practices. It also highlights important limitations, including reliance on short term metrics, limited longitudinal evidence on CLV, and insufficient integration of ethical and privacy considerations, and outlines directions for future research.
The Influence of AI-Based Product Recommendation Systems on Impulse Buying in Mobile Commerce
The article investigates how AI based product recommendation systems shape impulse buying in mobile commerce, focusing on when personalized suggestions intensify unplanned purchasing. It asks how recommendation features and mobile interface cues are conceptualized as stimuli, which psychological mechanisms link them to impulsive outcomes, and which contextual or consumer factors condition these effects. Using a systematic literature review of peer reviewed studies published between 2018 and 2022, the study extracts data on theoretical frameworks, mobile settings, recommendation designs, measures of impulse buying, and reported mediators and moderators. The synthesis shows that AI based recommendations typically raise perceived relevance, reduce search effort, and guide attention, which strengthens positive affect, perceived value, flow, and urge to buy, especially in hedonic and socially rich mobile environments. Evidence also highlights moderating roles of demographic characteristics, social influence, and privacy concerns, while revealing heavy reliance on cross sectional self-report designs. The review identifies key gaps in causal evidence, algorithmic transparency, and ethical considerations around AI mediated impulse buying.
Customer Engagement Scoring with Machine Learning: Integrating Clickstream, Social, and CRM Data
This study investigates how machine learning is used to construct customer engagement scores from clickstream, social media, and CRM data in recent marketing research. It asks how engagement is conceptualized and operationalized, which data sources and feature representations are employed, and to what extent multi source integration is implemented in existing models. Using a systematic literature review of peer reviewed studies from 2017 to 2022, the article maps contexts, data characteristics, modelling approaches, and evaluation practices. The synthesis shows that most studies model engagement as a single channel behavioral outcome and rely on task specific indicators, with deep learning and tree-based methods frequently outperforming traditional models. Empirical work that jointly exploits clickstream, social, and CRM data in unified engagement scoring architectures remains limited. The review develops an integrative view of current design patterns, identifies conceptual and methodological gaps, and outlines priorities for future research on cross channel, value-oriented engagement scoring with machine learning.
Generative AI for Visual Advertising: Impacts on Creativity, Brand Consistency, and Consumer Response
This article examines the impacts of generative artificial intelligence on visual advertising, with a specific focus on creativity, brand consistency, and consumer response. Using a systematic review of peer-reviewed studies published between 2018 and 2022, the study consolidates fragmented evidence on the use of generative models in formats such as display advertising, social media content, online video, and synthetic or CGI influencers. The results indicate that generative AI primarily augments rather than replaces human creativity, is particularly effective for large-scale variation, performance optimization, and rapid testing, and can encode brand guidelines to deliver visually consistent identities across channels and segments. At the same time, the review highlights risks of creative homogenization, brand safety incidents, and intensified consumer concerns about authenticity, transparency, and manipulation when AI-generated visuals are used without clear disclosure or ethical safeguards. The article discusses these dynamics through creativity, branding, and consumer psychology perspectives, and concludes with a research agenda that prioritizes longitudinal, comparative, and field-based studies on AI-human creative collaboration in real campaigns.
The Role of AI in Omnichannel Retail: Coordinating Online and Offline Customer Experiences
This article examines how artificial intelligence (AI) contributes to coordinating online and offline customer experiences in omnichannel retail. It addresses the question of whether and how AI enabled tools help retailers deliver seamless journeys across physical and digital touchpoints. Using a systematic literature review of peer reviewed studies published between 2017 and 2022, the study synthesizes evidence on AI applications that support personalization, context aware interaction, and data driven decision making in omnichannel settings. The results show that AI primarily operates through two layers: front stage applications that tailor content and service across channels, and back stage analytics that integrate data to support forecasting, inventory, and logistics decisions. The article discusses these findings by organizing the literature into themes of omnichannel integration, technology enabled in store experiences, AI driven personalization, and data and governance capabilities. It concludes that AI has significant potential to strengthen omnichannel coordination, but its benefits are contingent on integration quality, strategic alignment with customer centric goals, and robust data governance.
Human-AI Collaboration in Marketing Decision-Making: Enhancing Managerial Judgment or Creating Dependence?
This article examines whether human-AI collaboration in marketing decision-making enhances managerial judgment or creates new forms of dependence. Adopting a systematic literature review of peer-reviewed studies published between 2018 and 2022, it synthesizes evidence on how marketers use AI tools to support tasks such as targeting, forecasting, and campaign optimization. The review identifies benefits of AI as an analytical and advisory partner, including improved data processing, pattern detection, and support for evidence-based strategies, alongside risks of overreliance, automation bias, deskilling, and diffusion of responsibility. The article organizes the literature into themes of augmentation, conditions shaping reliance and resistance to algorithms, and emerging concerns about governance and ethics in AI-mediated marketing decisions. It concludes that outcomes depend on how collaboration is designed, highlighting the need for transparent systems, clear accountability, and investment in human capabilities to ensure that AI serves as a tool for augmentation rather than a source of managerial dependence. Future research directions are outlined to guide more responsible and effective deployment of AI in marketing practice.
Evaluating AI-Driven Personalization Strategies and Their Effects on Digital User Engagement
This article examines how AI-driven personalization strategies shape digital user engagement across platforms such as social media, mobile apps, and omnichannel retail. It asks which types of AI-based personalization are most commonly used, how engagement is defined and measured, and under what conditions personalization enhances or undermines user responses. The study adopts a systematic review of peer-reviewed research published between 2017 and 2021, synthesizing evidence from marketing, information systems, and digital communication. The results indicate that personalized recommendations, curated content feeds, and adaptive interfaces can increase clicks, time spent, and repeat use when they deliver relevant, contextually appropriate experiences that users perceive as helpful and legitimate. However, privacy concerns, perceived surveillance, and lack of control can dampen or reverse these effects, particularly in highly data-intensive settings. The article discusses these patterns by organizing studies by personalization strategy and engagement dimension, and concludes that effective AI-driven personalization must balance optimization goals with transparency, user agency, and long-term relationship outcomes.
Machine Learning Approaches to Customer Churn Prediction in Subscription Markets
This article examines how machine learning has been applied to customer churn prediction in subscription markets such as telecommunications, media streaming, Software as a Service (SaaS), and financial services, where recurring revenue makes customer retention critical. It asks which algorithms and data practices are most commonly used, how they handle issues like class imbalance and high-dimensional features, and whether models are evaluated in terms of business value as well as statistical accuracy. The study employs a systematic review of peer reviewed articles published between 2017 and 2021, synthesizing evidence across telecom, broadband, banking, and other subscription contexts. The findings show that tree-based ensembles, stacking, and deep learning generally outperform traditional statistical models, especially when paired with targeted feature engineering and imbalance handling techniques. The article discusses these patterns by comparing sectors, methodological choices, and evaluation criteria, and concludes that future research should integrate economic objectives, temporal dynamics, and interpretability to make churn models more actionable for subscription businesses.
Chatbots in Customer Service: A Systematic Review of Adoption, Performance, and Customer Outcomes
This article examines how chatbots are adopted and used in customer service and how they influence service performance and customer outcomes. It asks which factors drive customer and provider adoption, under what conditions chatbots improve efficiency and service quality, and how chatbot interactions shape satisfaction, trust, loyalty, and negative reactions such as perceived creepiness or privacy concerns. The study conducts a systematic review of peer reviewed research published between 2018 and 2021, synthesizing quantitative and qualitative evidence across industries and countries. The results show that chatbots can enhance response speed, perceived service quality, and compliance when they are usable, responsive, transparent, and clearly positioned as complements to human agents. However, poorly calibrated social cues, opaque data practices, and weak escalation paths can undermine trust and loyalty, limiting the benefits of automation. The review discusses these patterns by organizing studies around adoption, performance, and customer outcomes and concludes that future work should employ integrated, multi method approaches that link specific design features to both operational metrics and long-term relational outcomes.
Programmatic Advertising Optimization Using AI Bidding Strategies
This article examines how artificial intelligence is used to optimize bidding strategies in programmatic advertising and real time bidding environments, focusing on the tension between short term performance gains and broader marketing and consumer outcomes. The study conducts a systematic literature review of peer reviewed journal articles published between 2016 and 2021, asking which AI and optimization methods are applied to bidding, which objectives and constraints they address, and what impacts they report. Across the reviewed studies, profit oriented models, reinforcement learning policies, control based pacing, and advanced click or conversion prediction generally outperform rule-based bidding on efficiency metrics such as clicks, conversions, and return on ad spend, but often under narrow objectives and proprietary data settings. The article discusses these results by grouping studies according to algorithmic approach and optimization focus, and by contrasting technical findings with evidence on media quality, privacy concerns, and brand outcomes. The main conclusion is that future work should integrate multi objective optimization and consumer centric evaluation into AI bidding research and practice.
Transforming Customer Journey Mapping with AI: From Static Pathways to Dynamic Orchestration
This study examines how artificial intelligence is transforming customer journey mapping from static diagrams into dynamic orchestration across omnichannel environments. It adopts a systematic literature review of peer reviewed journal articles on artificial intelligence enabled journeys, customer experience, process mining, and journey orchestration. The synthesis reveals a clear shift from linear paths toward data driven systems that use contextual signals to personalise content, timing, and channel choice in real time. Studies highlight the growing role of chatbots, voice based assistants, recommender systems, and process mining techniques in reshaping touchpoints and visualising actual paths, but also show that these applications are often implemented in organisational silos. The review conceptualises artificial intelligence enabled customer journey mapping as an integrated capability that spans data infrastructure, analytical models, experience design, and governance. It concludes that alignment between journey representations and decision engines is required to achieve continuous, cross channel orchestration. The article also outlines future research directions on governance and performance outcomes.
AI Persuasion in Marketing: A Systematic Review of Algorithmic Influence on Consumer Decision-Making
This article examines how artificial intelligence (AI) systems in marketing shape consumer decision-making, focusing on algorithmic personalization, recommendation, and conversational agents. The study’s role is to integrate fragmented evidence from 2016-2021 into a coherent picture of “algorithmic persuasion” and its implications for autonomy, privacy, and fairness. Using a systematic literature review of peer-reviewed empirical studies, the article identifies how AI increases message relevance, compresses consideration sets, and alters interaction experiences, thereby enhancing short-term outcomes such as attention, attitudes, and behavioral intentions. The results are synthesized narratively by grouping studies according to AI application type, persuasion mechanism, and consumer response patterns, and by comparing findings across contexts and methods. The review finds that AI-based persuasion is consistently effective but ambivalent: it improves decision efficiency while simultaneously creating vulnerabilities related to perceived surveillance, manipulation, and overdependence on algorithmic guidance. These findings underscore the need for ethically informed design, transparency safeguards, and longitudinal, cross-cultural research on AI-mediated persuasion.
Measuring the Return on Investment of AI in Marketing: A Multi Industry Study
This study examines how organisations measure the return on investment of artificial intelligence in marketing across different industries. Drawing on a systematic literature review of peer reviewed journal articles, the study synthesises evidence on where artificial intelligence is deployed in marketing, which financial and non financial metrics are used, and how organisational capabilities shape outcomes. The review shows that artificial intelligence has evolved into a strategic marketing capability that supports granular segmentation, personalisation, dynamic pricing, and service automation, but that most studies still evaluate outcomes through intermediate indicators such as engagement, conversion, and satisfaction rather than audited financial returns. The findings highlight the central role of data infrastructures, cross functional integration, and clear strategic focus on artificial intelligence in translating technological investments into sustained market and financial performance. The paper concludes by outlining implications for managers seeking to justify artificial intelligence investments and for researchers aiming to design more rigorous, comparable frameworks for measuring artificial intelligence related returns in marketing.
Trusting the Machine: Consumer Perceptions of AI Versus Human Recommendations
This study investigates how consumers perceive and trust artificial intelligence based recommendation systems compared with human recommendations across decision contexts. Using a systematic literature review method, the article synthesises empirical evidence on perceived competence, impartiality, empathy, transparency, learning capability, and privacy concerns linked to artificial intelligence driven advice. The review identifies trust as a multidimensional judgement that combines beliefs about technical performance with inferences about benevolence and integrity. The findings show that consumers may value artificial intelligence recommenders for their efficiency and perceived objectivity, yet often experience them as opaque and threatening to personal control and privacy. Algorithm aversion emerges when visible errors lead consumers to penalise artificial intelligence more harshly than human advisors, although demonstrations of learning and clear explanations can partially restore trust. Overall, the review concludes that willingness to follow artificial intelligence recommendations depends on how systems are governed and communicated, and on whether human warmth and fairness are seen as necessary in the specific decision domain.
Gamification and AI: Personalized Reward Mechanisms in Loyalty Programs
This study investigates how the integration of gamification and artificial intelligence can strengthen the effectiveness of loyalty programs in competitive digital markets. Using a systematic literature review, the study synthesises empirical and conceptual research on game based loyalty mechanics and data driven personalization. The findings show that game elements such as challenges, levels, and progress feedback can transform loyalty schemes from transactional discount tools into experiential systems that enhance enjoyment, perceived value, and behavioural loyalty. However, many programs still rely on static, one size fits all reward structures that limit their capacity to adapt to heterogeneous customer motivations. In parallel, research on artificial intelligence demonstrates strong capabilities for personalizing offers and interactions based on granular behavioural and contextual data, yet these tools are rarely applied to gamified rewards. The review concludes that next generation loyalty systems should employ artificial intelligence to dynamically orchestrate personalized game like reward mechanisms while safeguarding transparency, fairness, and long term relationship quality and trust outcomes.
AI-Enabled Market Forecasting: Improving Demand Prediction in Volatile Environments
This study examines how artificial intelligence based demand forecasting can enhance decision making in increasingly volatile product markets. Shorter product life cycles, intensive promotions, technological disruption and supply chain shocks make small forecasting errors translate into stockouts, excess inventory and financial underperformance. Traditional statistical time series models perform well only under stable conditions and struggle when demand is intermittent, promotion driven or influenced by fast moving external variables. A systematic literature review of empirical and review studies on retail, electronic commerce and supply chain contexts synthesises evidence on machine learning and deep learning applications for sales and demand forecasting. The findings show that algorithms such as random forests, gradient boosting, support vector regression, recurrent neural networks and hybrid deep learning architectures generally outperform conventional benchmarks, especially when high frequency market and supply chain signals are incorporated. However, the review also highlights persistent challenges related to data quality, feature drift, model transparency and integration into existing sales and operations planning processes.
Sustainability Marketing and AI: Targeting Conscious Consumers with Green Messages
This study investigates how sustainability marketing and artificial intelligence are combined to target conscious consumers with green messages. Using a systematic literature review, the paper synthesises empirical and conceptual research on sustainable marketing strategies, green consumer behaviour, and artificial intelligence enabled personalisation across digital touchpoints. The review shows that embedding environmental and social responsibility in core brand positioning, supported by sustainable brand equity and marketing innovation, can strengthen loyalty, trust, and market performance among conscious consumers. At the same time, the findings highlight substantial heterogeneity in green consumer responses, driven by differences in environmental concern, perceived green value, social norms, and perceived behavioural control, which underscores the need for nuanced segmentation and tailored sustainability communication. Finally, the review demonstrates that artificial intelligence can enhance the precision and timeliness of green targeting, but also raises critical risks related to privacy, opacity, bias, and sophisticated greenwashing. The study concludes with implications for responsible governance and future research in marketing and public policy
Emotional AI in Advertising: Recognizing and Responding to Consumer Affective States
This study examines how emotional artificial intelligence can reshape contemporary advertising by recognising and responding to consumer affective states. Drawing on a systematic literature review of work in affective computing, emotion recognition, advertising, and ethical analysis, the paper synthesises evidence on the technical capabilities, applications, and risks of emotionally responsive systems. The review shows that automated emotion measures based on facial expressions, voice, physiology, and multimodal fusion can capture meaningful reactions to advertising and enhance diagnostics and pre-testing. However, these systems produce probabilistic indicators rather than direct access to “true” feelings, and their validity depends on context, data quality, and theoretical interpretation. Empirical studies reveal that emotional information is mostly used for evaluation rather than in closed loop, real time adaptation of messages. Across the literature, concerns about privacy, manipulation, and bias highlight the need for strong governance and transparency. The study outlines research and design implications for developing emotionally intelligent advertising that supports both effectiveness and consumer welfare in advertising practice.
Copyright (c) 2024 International Review of Artificial Intelligence in Marketing
Consumer Acceptance of AI in Retail: The Mediating Roles of Perceived Usefulness and Risk
This study examines how artificial intelligence reshapes retail and how consumers ultimately accept or resist artificial intelligence enabled services. Using a systematic literature review of peer reviewed journal articles on retail, online shopping, and artificial intelligence based voice assistants and chatbots, the study synthesises evidence on benefit related and risk related pathways. The review confirms that perceived usefulness is a central mechanism through which automation, personalisation, and decision support translate into favourable attitudes, stronger purchase intentions, and even impulse buying in artificial intelligence mediated retail environments. At the same time, perceived privacy, performance, and security risks operate as countervailing forces that weaken trust and willingness to rely on artificial intelligence, even when functional benefits are salient. Across the literature, perceived usefulness and perceived risk are still modelled mainly as separate predictors rather than as integrated mediators. The study therefore proposes a dual pathway framework that explains consumer acceptance through simultaneous benefit and risk evaluations for more consumer centric retail strategies
The Dark Side of AI-Driven Persuasion: Algorithmic Manipulation and Consumer Autonomy
This article examines the dark side of artificial intelligence driven persuasion in digital markets by synthesising interdisciplinary research on algorithmic personalisation, dark patterns and consumer autonomy. Drawing on a systematic literature review of peer reviewed work in marketing, consumer behaviour, human computer interaction, information systems and law, the study maps how artificial intelligence based systems have shifted persuasion from discrete campaign level tactics to an always on infrastructure that continuously predicts, tests and optimises individual responses. The review identifies recurring mechanisms through which granular targeting, interface level design strategies and opaque optimisation practices blur the boundary between legitimate persuasion and manipulation and create structural risks for consumers. Across the literature, consumer autonomy emerges as the central normative concern, as artificial intelligence simultaneously supports and erodes people’s capacity to act on their own reasons and values in practice. The article concludes by outlining implications for responsible design and governance that seek to retain the benefits of personalisation while preventing systematic manipulation.
Personalized Email Campaigns Powered by AI: Effects on Engagement, Conversion, and Unsubscribe Rates
This article examines how artificial intelligence powered personalised email campaigns influence engagement, conversion, and unsubscribe behaviour in contemporary digital marketing. Using a systematic literature review approach, the study synthesises evidence from peer reviewed research on email marketing, interactive marketing, and artificial intelligence enabled personalisation. The review shows that data driven personalisation generally increases open and click through rates, and can raise conversion when messages are grounded in meaningful behavioural, contextual, and volunteered data rather than superficial cues. Behavioural and triggered campaigns based on browsing or cart abandonment signals are particularly effective in directing persuasive effort toward high propensity recipients. However, the findings also reveal that unsubscribe behaviour and spam complaints operate as systematic indicators of micro churn, especially when contact frequency is high or personalisation is experienced as intrusive or manipulative. Cultural context and customer journey stage further moderate these effects. Overall, the review highlights a central trade off in which artificial intelligence amplifies both the benefits and risks of personalised email strategies.
Leveraging Computer Vision for Visual Merchandising: AI Applications in Online Retail
This conceptual article examines how computer vision can be leveraged as a strategic resource for visual merchandising in online retail. It first reviews marketing and neuroscience evidence showing that digital visual design elements such as image quality, layout, and aesthetic coherence strongly shape engagement, perceived value, and purchase intentions in electronic commerce interfaces. The study then synthesises prior research on image based recommendation, visual search, and virtual try on, which use deep learning models to curate visually consistent assortments, enable “shop by image” journeys, and create interactive fitting room experiences on consumer devices. Using a systematic literature review approach, the article maps these applications onto core visual merchandising objectives related to product presentation, assortment framing, and cross selling. The resulting framework highlights that the greatest potential of computer vision lies in integrating these tools into coordinated visual journeys that enhance customer experience and commercial outcomes, while opening avenues for future empirical and design oriented research in diverse electronic commerce categories.
AI-Based Dynamic Pricing Strategies and Perceived Price Fairness in Digital Platforms
This article examines how artificial intelligence based dynamic pricing on digital platforms shapes perceived price fairness and subsequent customer responses. The study focuses on revenue management practices in electronic commerce, ride hailing, online travel, and accommodation sharing services where machine learning models continuously adjust prices based on behavioural and contextual data. Using a systematic literature review, the article synthesises peer reviewed evidence on the design of algorithmic pricing, the role of personalization intensity and transparency, and the psychological mechanisms that connect price perceptions with trust, satisfaction, and loyalty. The findings show that artificial intelligence based pricing can enhance revenue and capacity utilisation, but that opaque and highly personalized prices often generate perceptions of unfairness, privacy concern, and feelings of betrayal, which weaken platform relationships. The review concludes that dynamic pricing strategies must treat fairness as a central design constraint and align pricing logic and communication with evolving expectations of contractual fairness on digital platforms and with emerging regulatory and ethical standards.
Reimagining Brand Equity Measurement with AI: From Surveys to Behavioral and Unstructured Data
This article reimagines brand equity measurement in light of advances in artificial intelligence, big data analytics, and unstructured digital traces. It highlights how dominant consumer based frameworks still rely on cross sectional surveys and aggregate indices that only partially capture the dynamic ways in which customers interact with brands across digital touchpoints. Drawing on a systematic review of peer reviewed studies, the article synthesises evidence on the emergence of behavioural and trace data sources, such as clickstreams, transactions, mobile usage, social media communication, and online reviews, as continuous signals of brand related behaviour and meaning. The review shows that artificial intelligence and machine learning methods are increasingly used to model complex customer journeys, yet are rarely embedded inside core brand equity metrics. The article concludes by outlining a research agenda that calls for hybrid measurement frameworks which integrate survey indicators with behaviour based and content based data to construct more dynamic, context sensitive, and actionable views of brand equity today
Data Quality and Bias in AI Marketing Models: Sources, Consequences, and Mitigation Strategies
This article reviews how artificial intelligence transforms marketing analytics while remaining constrained by data quality and algorithmic bias. Drawing on a systematic literature review of peer reviewed studies, the paper synthesises evidence on how artificial intelligence supports segmentation, personalised targeting, dynamic pricing, and automated service when embedded in integrated data and model architectures. The findings show that information quality, consistency, and governance across data pipelines are preconditions for reliable prediction, because missing, noisy, or poorly integrated data distort customer classification and managerial insight. The review also reveals that historical, sampling, measurement, and feedback loop biases in training data and modelling choices can generate systematically unfavourable outcomes for vulnerable customer groups, eroding trust, brand equity, and regulatory compliance. Overall, the study argues that accurate and fair artificial intelligence marketing models require socio technical solutions that combine robust data governance, transparent model monitoring, and organisational accountability for outcomes across customer subgroups. The article concludes with an agenda for future interdisciplinary research directions.
Customer Churn Prediction Using Deep Learning: Implications for Retention Marketing
This study reviews how deep learning is transforming customer churn prediction and its implications for retention marketing in service industries. Using a systematic literature review, the article examines empirical work on churn models that exploit behavioural and transactional data in sectors such as telecommunications, banking, and e commerce. The findings show that deep learning architectures, including convolutional and representation learning models, consistently outperform traditional statistical and machine learning methods by capturing nonlinear and temporal patterns in customer behaviour. However, the review reveals that many studies still optimise generic accuracy metrics rather than aligning model design with profit oriented objectives and customer value. Research that incorporates profit based loss functions and recognises churn heterogeneity shows that integrating predictive scores with segmentation, campaign cost, and expected response can enhance the impact of retention programmes. The study concludes that future research should embed deep learning churn models within holistic retention systems that link data, models, and decision rules into prioritised interventions over time.