Volume 12 (2026-27)

As organisations race to deploy GenAI for a range of customer and internally facing applications, a critical gap has emerged: the assumption that enterprise knowledge is ready for AI consumption. Research indicates that more than 80 per cent of AI projects fail to deliver measurable business value,1 while Gartner found that 50 per cent of GenAI projects failed in 2025 due to lack of business value, poor data quality, increasing costs, regulatory and risk challenges, and inadequate change management.2 This paper argues that these failures are not primarily model problems, but rather knowledge architecture problems. Drawing on a comprehensive 74-question AI Readiness Maturity Assessment framework and a scoring methodology to predict content visibility for AI Retrieval Readiness, this paper provides marketing analytics leaders with a practical roadmap for building the semantic foundations that enable reliable AI-powered marketing systems. The paper examines how enterprises can evaluate content readiness across four critical domains — knowledge readiness, operational readiness, technical readiness and governance readiness — and translate these assessments into actionable transformation initiatives. Using a case study from the financial services sector, the paper demonstrates how an organisation can establish an objective baseline score on an AI readiness assessment and then systematically address foundational gaps in knowledge, operational, technical infrastructure and governance readiness, while delivering immediate business value through targeted use case deployment. The paper also introduces information architecture-directed retrieval augmented generation (IAD-RAG) — an approach for improving the reliability of retrieval augmented generation in marketing and service applications. IAD-RAG can serve as the architectural response to the content gaps identified in the assessment. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: AI readiness; content operations; generative AI; marketing analytics; RAG; knowledge architecture; semantic governance; marketing AI governance; content quality metrics; enterprise AI transformation; GenAI

Marketing analytics was developed for an environment in which value could be scaled through increased exposure, engagement and volume-based optimisation. This paper argues that those economics are being reshaped by fundamental changes in content production, attention dynamics and decision making, driven by advances in AI and the growing role of autonomous systems. As content becomes abundant and customer interactions are increasingly mediated by algorithmic agents, traditional metrics such as impressions, reach and conversion rates lose predictive and economic relevance. This paper introduces the concept of the relationship economy, in which value is created through ongoing, data-mediated exchanges that progressively reduce uncertainty around identity, intent and context. Central to this framework is the law of relationship fidelity, which holds that organisations with a superior understanding of customer identity and intent operate at a lower marginal cost of value creation and thus capture greater market share. The analysis explores how identity resolution and intent inference function as key economic inputs, enabling more precise decision making, reduced coordination costs and scalable personalisation. It concludes by outlining implications for marketing analytics, including the shift from campaign-based measurement to continuous decision governance, and the increasing role of analytics in enabling real-time, relationship-driven value creation at scale. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com /business.
Keywords: relationship economy; relationship fidelity; customer lifetime value; customer experience; customer data platforms; agentic AI in marketing; personalisation at scale; customer relationship management (CRM)

Despite significant investment in marketing analytics platforms, dashboards and AI-driven tools, organisations continue to expect improved performance and decisionmaking. Yet many initiatives fail to deliver meaningful value. This paper argues that the root cause is not a lack of technology but a lack of data quality, rooted in how data is created, managed and maintained. Inconsistent, incorrectly entered and ungoverned sales and marketing data can distort metrics, undermine trust and consume significant operational time. Drawing on practical experience and the principles outlined in ‘Optimizing Sales & Marketing Data’,1 this paper reframes data quality as a performance layer rather than a hygiene task. It demonstrates how poor data quality impacts operational efficiency, decision-making and marketing effectiveness, and how these risks are amplified in AIdriven environments. A structured approach to improving data quality is presented, based on standardisation, categorisation and governance. Organisations that adopt this approach can unlock the full value of their analytics investments, enabling more reliable insights, faster decision-making and improved marketing performance. This article is also included in The Business & Management Collection which can be accessed at http:// hstalks.com/business.
Keywords: marketing analytics; data quality; AI; customer relationship management; segmentation; governance; marketing performance

Digital marketing effectiveness varies across cultural contexts, yet most research examines individual tactics rather than integrated configurations. This paper applies morphological analysis to identify how combinations of digital marketing practices influence customer satisfaction in Japan. Seven dimensions are mapped: content, channels, personalisation, response time, tone, trust/consent and loyalty. These form the basis for generating and assessing strategic configurations. Four culturally feasible configurations emerge: trust-driven omni-channel engagement, community-oriented emotional connection, efficiency-focused digital service and hybrid politeness with innovation. Three cultural filters shape consumer responses: risk aversion, collectivism and politeness norms. The paper offers a configuration-based and culturally grounded account of digital marketing effectiveness with practical guidance for firms operating in Japan. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: digital marketing; customer satisfaction; morphological analysis; omni-channel strategy; personalisation; cultural context

Digital business transformation, increasingly complex consumer behaviour and the growing demand for evidence-based marketing have intensified interest in neuromarketing. Drawing on cognitive neuroscience and consumer behaviour theory, neuromarketing offers technology-driven insights that are reshaping marketing decision making. This paper identifies the neuromarketing tools most widely used in practice and examines the benefits they bring to marketing research. The study uses data obtained through semi-structured interviews with neuromarketing experts and examines the material through qualitative content analysis. The study informants highlighted eye tracking, facial coding or facial expression recognition, galvanic skin response, electroencephalography and AI-based visual analysis systems as the tools they use most frequently. They emphasised the value of objective behavioural data, clearer assessments of emotional responses, more effective testing of advertising and design and better-informed marketing decisions. These findings contribute to international debates on neuromarketing and extend understanding of how these tools support marketing research. Although most neuromarketing studies focus on larger markets, the Lithuanian context offers lessons for smaller European markets and other developing neuromarketing environments. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: neuromarketing; neuromarketing tools; application; benefits; marketing research; neuromarketing experts

This paper presents a methodological comparison focused on reproducibility between GPT-4o (via the ChatGPT interface) and an executable text mining workflow in KNIME, in the context of market research. Rather than treating the analytical statements generated by the chatbot as computed results, we distinguish between model-generated statements and executable results, and validate the quantitative statements against a documented KNIME process (Term Frequency-Inverse Document Frequency [ TF-IDF], k-means clustering and lexicon-based sentiment analysis). Using 9,795 online reviews of a fresh food retailer as an illustrative case, we compare the two approaches in terms of pre-processing, descriptive term analysis, clustering and sentiment classification. The comparison shows partial agreement at the level of general themes, but substantial discrepancies in pre-processing-dependent results and sentiment distributions. These findings suggest that GPT-4o can assist in workflow articulation and preliminary interpretation, but that analytical claims generated by the chatbot should not be treated as reproducible results without external, executable validation. The study provides comparative evidence for the prudent use of large language model (LLM)-assisted workflows in marketing text analysis. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: consumer behaviour insights; natural language processing (NLP); large language model (LLM); GPT-4o (via ChatGPT); KNIME; text mining

Marketing analytics is entering a period of accelerated computational growth. Advances in artificial intelligence (AI), automation and emerging optimisation techniques promise gains in modelling power, personalisation and real-time decision making. At the same time, these forces are exposing a growing weakness: the declining reliability of identity data. Here, ‘identity’ refers to the persistence and fidelity of analytical identifiers used for measurement and decisioning, rather than legal identity or personally identifiable information. Synthetic activity, autonomous agents, behavioural mimicry and the fragility of credential systems under post-quantum threats increasingly undermine identity continuity across channels. As systems become faster and more autonomous, identity instability intensifies: attribution models become fragile, segmentation accuracy degrades and optimisation engines converge on signals that may not reflect genuine human behaviour. Analytical confidence can increase even as trust in identity signals declines, widening the gap between model sophistication and insight reliability. By 2030, a primary constraint on marketing effectiveness will be the continuity and authenticity of identities behind the data. This paper examines why identity integrity becomes a bottleneck for measurement, audience definition and optimisation performance in high-compute environments. It introduces an identityresilient analytics framework integrating post-quantum-ready identity binding, identity confidence scoring, AI containment and identity-aware governance, and concludes with a practical roadmap to help organisations preserve analytical reliability and operate with confidence in an increasingly synthetic digital ecosystem. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: identity integrity; marketing analytics; agentic AI; synthetic behaviour; attribution accuracy; post-quantum identity

As customer journeys in the enterprise sector become increasingly complex, involving multiple stakeholders and prolonged sales cycles, traditional last touch attribution (LTA) models fail to capture the contribution of early and mid-stage interventions. This practical implementation case study documents how a high-touch marketing organisation transitioned from LTA to a data-driven multi-touch attribution framework built on Markov chain modelling. The paper demonstrates how shifting to an enterprise attribution model reveals the correlational value of ‘nurturing’ touch points — such as blogs and webinars — that are often undervalued by single-touch heuristics. The paper also addresses the organisational challenges of this transition, proposing a three-phase implementation roadmap: technical proof-of-concept, internal marketing education and sales alignment. The findings indicate that although algorithmic models offer a more holistic representation of the customer journey, their effectiveness depends on addressing the perceived ‘black box’ nature of the methodology. Reframing the output around ‘marketing influence’ rather than credit allocation helps build trust in the model and fosters shared accountability between sales and marketing. This article is also included in The Business & Management Collection which can be accessed at http:// hstalks.com/business.
Keywords: multi-touch attribution; Markov chains; B2B marketing; sales-marketing interface; change management; marketing analytics

The digital revolution has accelerated the adoption of artificial intelligence (AI) in marketing and sales to enhance organisational resilience and efficiency. This study combines bibliometric analysis with expert interviews to examine how AI is applied in these functions. A systematic review (2006–2024) using PRISMA criteria was analysed with VOSviewer and Bibliometrix, and complemented by interviews with 23 marketing professionals. Six key themes emerged: sales, artificial intelligence, supply chain management, sustainability, COVID-19 and information systems. These highlight AI’s role in optimisation, customer engagement and operational efficiency. Interview findings reinforce the importance of predictive analytics, personalised marketing and automated decision making. The study provides an integrative overview of how AI supports marketing and sales resilience, with implications for practitioners and future research. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: artificial intelligence; marketing resilience; sales resilience; sales strategy; digital transformation; sustainability

Neuromarketing has expanded rapidly over the past two decades as neuroscience tools have become more accessible to marketing practitioners and researchers. Nevertheless, the field still lacks an integrated practice-oriented overview that explains how its main themes have developed. This study addresses that gap by applying a structured text-mining approach to 4,292 scholarly publications drawn from major academic databases. Using natural language processing techniques such as text cleaning, keyword extraction, topic modelling and cluster analysis, the study identifies the main research streams and shows how conclusions were derived from the data. The analysis demonstrates that neuromarketing has evolved into a broad multidisciplinary domain shaped by technologies such as fMRI, EEG, artificial intelligence, virtual reality and wearable sensors. Ethical concerns have also intensified, with growing attention to autonomy, privacy and the responsible use of consumer data. While established theories such as the theory of planned behaviour and dual-process models continue to guide research, new directions focus on technology—consumer interactions, emotional processing and predictive analytics. The findings clarify where the field has generated genuinely new insights, for example through multimodal measurement of consumer emotion, and where it primarily reinforces existing knowledge. Despite significant progress, challenges remain in theoretical integration, methodological consistency and the incorporation of practical neuroethical guidelines. By showing how large-scale text mining can map conceptual evolution and reveal hidden patterns, this paper offers researchers and practitioners a clearer understanding of the field’s trajectory and guidance for developing more coherent, transparent and ethically informed neuromarketing practices. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: neuromarketing; natural language processing; text mining; consumer neuroscience; neuroethics

Retail membership clubs are an important yet under-researched area of scholarly research in retailing. This paper examines how shoppers belong to, and shop at, membership clubs. It situates membership clubs within the broader context of the human need for belonging, alongside recent trends towards greater social isolation. Using a largescale syndicated survey, the paper analyses self-reported behaviours related to membership clubs. It explores the social functions these clubs serve and examines how membership relates to shopper characteristics, marketing influences, happiness levels and other aspects of people’s lives. The results indicate that membership is strongly associated with being married, having children, higher income and higher overall spending levels. The most prevalent membership programmes are found to be Amazon Prime, Costco and Walmart+, with most members belonging to multiple clubs. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: retailing; membership clubs; belonging; shopper experience; happiness; retail analytics

Emotions play a key role in marketing, yet their role in sequential decision making is not well understood. Common wisdom holds that positive and neutral emotions are best, but underestimates the value of negative emotions. This paper examines how emotional states affects decisions in marketing negotiations through the lens of game theory. Building on empirical research using ultimatum games, this paper demonstrates that both positive and negative emotional priming increase generous behaviours compared with neutral conditions. The findings of this paper offer actionable insights for marketing practitioners seeking to optimise negotiations, promotional offerings and customer engagement strategies. This paper challenges traditional rational actor models by showing how systematic emotional effects influence economic decisions that marketers can measure, test and strategically incorporate into customer experiences. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: marketing decision making; emotions; game theory; generosity