Volume 11 (2025-26)

Artificial intelligence (AI) is rapidly transforming marketing, but realising its full value requires more than isolated tools — it demands a strategic, layered implementation. This paper examines how marketing organisations can deploy AI in three integrated layers: (1) a semantic data foundation to ensure clean, consistent data for AI; (2) autonomous analytical agents for deeper insights and root cause analysis; and (3) AI-driven campaign optimisation for adaptive, real-time execution. Recent industry findings and examples illustrate that, when implemented correctly, AI can significantly improve marketing efficiency, personalisation and return on investment (ROI). For instance, companies that have integrated AI into core marketing workflows have achieved double-digit boosts in sales ROI and conversion rates. However, common challenges around data quality, talent gaps and trust must be addressed. This paper identifies trends such as the increasing use of conversational AI and reinforcement learning in customer journeys, highlighting the importance of robust data governance and ethical frameworks. In conclusion, the reader will gain a clear framework for integrating AI into marketing — from data architecture to analytics and campaign optimisation — along with best practices for change management and oversight. This comprehensive approach enables marketers to harness AI’s potential for unprecedented personalisation and agility, while maintaining human creativity and strategic control. The result is a marketing function that is smarter, faster and more attuned to customers, positioning organisations to thrive in the evolving digital landscape. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence; autonomous agents; campaign optimisation; marketing analytics; marketing automation; semantic data layer

This paper explores the significance of thought leadership in marketing, emphasising the importance of calculating its return on investment (ROI). Thought leadership is increasingly recognised as a crucial element in the marketing mix, often referred to as the ‘8th “P” of marketing’. Drawing on groundbreaking research surveying more than 4,000 C-suite executives globally, this paper demonstrates that thought leadership delivers an astounding 156 per cent return on investment — 16 times greater than traditional marketing approaches. This research also shows that thought leadership drives US$265bn in annual purchasing globally. By interrogating the data underpinning the credible calculation for thought leadership ROI, the paper aims to provide a comprehensive understanding of how thought leadership can generate quantifiable business value and serve as a strategic platform for modern marketing activities. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.
Keywords: KPIs; content marketing; marketing ROI; thought leadership

Marketing performance measurement systems (MPMS) offer many benefits, such as strengthening strategic alignment through monitoring, enhancing marketing capabilities and cross-departmental communication, improving marketing effectiveness and efficiency and increasing the credibility of marketing within a company. Nonetheless, managers often regard MPMS endeavours — especially comprehensive MPMS endeavours — as disappointing. In fact, more than 50 per cent of senior managers report disappointment in MPMS results, often due to strategic failures and ineffective orchestration. This paper identifies three reasons why comprehensive MPMS often fail to improve the ability to measure performance: poor contingency fit, ‘MPMS glass ceilings’ (legal/platform/method limits) and intensified socio-technical frictions. Based on 33 expert interviews and two focus groups, the paper focuses on intensified socio-technical frictions and conceptualises the ‘comprehensiveness paradox’: the phenomenon in which increasing the comprehensiveness of MPMS diminishes the ability to measure marketing communication mix performance. To this end, the paper identifies seven socio-technical conditions that help managers reduce the most common frictions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: attribution; experiments; marketing mix modelling; marketing performance measurement systems

The intersection of artificial intelligence (AI) technology, political marketing and political campaign strategies in today’s era of fast-moving technological advancement has had a major impact on how citizens engage in democracy. This paper reviews how AI-based marketing in politics affects voter participation and election results in countries moving toward democracy. It is based on 68 peer-reviewed articles, institutional reports and empirical studies published between 2018 and 2025. The review draws on five main theories: technology acceptance, political marketing, the persuasion-knowledge model, the theory of democratic participation, and algorithmic governance. The findings are grouped into four areas: (1) AI-facilitated microtargeting and message personalisation; (2) predictive analytics for election forecasting; (3) automated content creation and chatbots; and (4) ethical and regulatory concerns. The results indicate that AI can predict voter behaviour with an accuracy of 15–25 per cent in emerging democracies, compared with 8–15 per cent in more developed democracies. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence; electoral outcomes; emerging democracies; political marketing; systematic literature review; voter engagement

This paper investigates the impact of augmented reality (AR) filters created by brands to drive consumer–brand engagement on social media platforms. A quantitative research design was employed, using an online survey of 240 social media users who had interacted with brand-generated AR filters. The stimulus-organism-response framework and experiential value theory were used as the theoretical lens to analyse the psychological and behavioural effects of AR filters on consumer–brand engagement. The data were examined using the partial least squares structural equation modelling (PLS-SEM) to evaluate the hypothesised relationships and validate the recommended conceptual model. The results show that brand AR filters significantly enhance experiential values like hedonic and social value, promoting higher levels of consumer engagement and brand advocacy. Through the integration of AR technology and experiential marketing, the study offers both useful insights for brands using AR filters to produce meaningful consumer experiences and theoretical additions to the literature on digital marketing. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: AR filters; augmented reality; consumer–brand engagement; hedonic value; social media platforms; social value

Small and medium-sized enterprises (SMEs), especially those in the restaurant sector, often lack the resources and expertise needed for data-driven decision making, which puts them at a competitive disadvantage. This paper addresses this gap by proposing a practical two-stage machine-learning framework to predict when restaurant customers are likely to report high satisfaction. Using a publicly available dataset of 1,500 customer records, the first stage uses a Chi-square test to identify significant, actionable factors, such as service rating, online reservation and meal type, while removing demographic variables that do not contribute useful information. The second stage applies logistic regression to classify and predict high satisfaction. Because the dataset is heavily imbalanced (far fewer ‘high satisfaction’ cases), the study shows that undersampling is necessary, with random undersampling producing the best balance and an AUC—ROC of 0.833. The results give SME restaurant managers a clear set of evidence-based priorities for improving customer experience and a practical tool for forecasting how customers may respond to future changes, helping reduce operational risk and improve marketing efficiency. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business/.
Keywords: applied marketing analytics; customer satisfaction prediction; feature selection; logistic regression; small and medium enterprises (SMEs)

Marketing analytics has transformed analysts from report producers into strategic advisers, yet many organisations keep them occupied on the hamster wheel of ad hoc reporting. This paper examines the critical shift required for analysts to move beyond dashboard delivery and toward trusted advisory roles that influence business outcomes. Being a mere report and dashboard generator runs the risks of overproduction, cognitive overload and stakeholder disengagement when data are presented without interpretation or context. Drawing on concepts from data storytelling, analytics adoption and cognitive psychology, the paper discusses how analysts can reduce information fatigue, build credibility and foster decision confidence through narrative communication. Storytelling integrates semantic precision, rhetorical persuasion and pragmatic relevance, enabling analysts to translate complex signals into actionable recommendations. Further, the role of the ‘analytics translator’ is explored as a bridge between technical teams and business leaders, emphasising the necessity of domain knowledge, communication skills and contextual framing. Barriers to adoption are addressed through a staged maturity model, showing how challenges such as inconsistent data definitions, cultural resistance and organisational silos evolve as analytics functions mature from reactive reporting to strategic influence. Practical steps are offered for reframing analytics as a process rather than a means of report production. The goal is to align with stakeholder incentives and demonstrate value through outcome-focused communication. The paper concludes that generative artificial intelligence will accelerate the automation of reporting, raising the bar for human analysts. To remain indispensable, practitioners must embrace roles as translators, storytellers and advisers who bring empathy, business literacy and contextual insight to data interpretation. Ultimately, the trusted data adviser is defined not by the volume of dashboards delivered but by the quality of decisions influenced. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: business intelligence; data storytelling; data-driven culture; decision-making; generative AI; marketing analytics; translator

Artificial intelligence (AI) powered tools, from generative copywriting assistants to predictive analytics engines, are transforming how marketing teams operate. As marketers increasingly rely on these technologies to automate content creation, accelerate campaign execution and personalise outreach at scale, ethical considerations often fall to the wayside amid rapid market adoption. While much of the existing discourse on AI ethics focuses on product development or data governance, there remains a critical lack of guidance for marketing teams that use AI internally to shape their day-to-day decision making and external brand presence. This paper will address that ethical gap by introducing FAIR, a practical ethical framework informed by applied marketing experience and wider principles drawn from traditions in applied ethical theory. The framework emphasises four core values: focus on intent, accountable teams, informed consent and responsible scaling. Drawing from real-world use cases and research, the paper presents five actionable best practices in ethical AI using the FAIR method. Together, the framework and practices will form a practical guide for marketing leaders, content strategists and operations teams seeking to adopt AI responsibly within marketing teams while grounding work in organisational integrity and human-centred ethical evaluation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI in marketing; ethical AI; ethical decision making; FAIR framework

This paper traces the evolution of video marketing measurement from the pre-internet era to the digital age, highlighting the shift from simplistic models to complex, data-rich environments. It then delves into the artificial intelligence (AI) era, where probabilistic measurement challenges traditional frameworks such as media mix modelling, attribution and experimentation. The paper proposes branded search volume as a realtime ‘conversion’ metric for brand measurement that also strongly correlates with sales. Ultimately, it explores how cutting-edge AI capabilities, including large language models interpretability and advanced attention measurement, provide revolutionary ways to understand brand impact and drive marketing effectiveness in today’s dynamic landscape. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: brand search; AI; brand measurement; machine learning; modern marketing; attribution

In business-to-business (B2B) sales environments, the use of intuition and manual lead qualification methods over data-driven techniques frequently results in missed revenue opportunities, inefficient use of scarce resources and, ultimately, revenue leakage. This paper presents a hybrid artificial intelligence (AI) approach to predictive opportunity scoring that addresses the technical, organisational and cultural challenges commonly faced by B2B firms, such as low conversion rates and misaligned sales and marketing efforts due to poor data quality and inconsistent lead management. Emphasising interpretability, stakeholder engagement and incremental deployment, the approach integrates machine learning with domain expertise to deliver measurable gains in sales performance, forecasting accuracy and cross-functional alignment. Drawing on a real-world case study in B2B manufacturing, the paper outlines practical strategies for implementing predictive scoring with imperfect data, fostering adoption among sales teams, and aligning marketing and sales departments through a shared, data-driven framework. The results show that even with imperfect data and dispersed global teams, a well-managed predictive scoring initiative can increase win rates and deliver tangible return on investment, offering B2B organisations a scalable and effective path toward transforming lead qualification into a strategic growth engine. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: B2B sales; lead scoring; marketing analytics; opportunity scoring; predictive analytics; sales forecasting

This paper examines how targeted gamification at checkout impacts conversion and loyalty, addressing the persistent problem of friction-filled, under-optimised online payment flows. The paper synthesises academic evidence, practitioner experiments and market data into five deployable techniques: progress indicators, personalisation/ interactive prompts, unlockable rewards, social proof and loyalty ‘levelling’, and integrates them in line with the checkout journey. Methods include a literature scan (eg Fogg Behavior Model; experimental work on scarcity/competition, haptics and gamified promotions), triangulation with industry studies and a comparative framework summarising expected uplift, risks and contexts of best fit. Findings indicate near-term conversion gains from progress indicators and personalisation/accelerated inputs (haptic/visual feedback improving perceived usability), while unlockable rewards and social proof raise purchase intention via anticipation and competitive arousal. Loyalty tiers primarily shift longer-horizon key performance indicators (repeat purchase, average order value), with evidence of 15–25 per cent revenue lift among redeemers and behavioural change among most members. Trends show the strongest impact when tactics reduce cognitive load on mobile, surface value at the pay moment and avoid dark-pattern urgency. The paper proposes an ethics-first, A/B-driven playbook that prioritises wallets/autofill and progress pacing, layers in rewards and social validation judiciously, and measures both immediate completion and downstream loyalty outcomes. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: checkout gamification; checkout personalisation; conversion rate optimisation; customer life-cycle value; loyalty tiers; online payments; progress indicators; scarcity; social proof; unlockable rewards

This research paper explores how an agentic, Bayesian optimisation driven marketing mix modelling (MMM) framework offers significant advantages over traditional, static regression-based methods, advancing MMM from its econometric roots to better meet the needs of modern marketers. Traditional MMM often relies on linear regression with predefined transformations and fixed decay rates, leading to sub-optimal parameter estimations and limited predictive power. Our proposed agentic approach leverages Bayesian optimisation to dynamically discover optimal adstock (carryover) and diminishing returns (saturation) parameters for each marketing channel. This data-driven, adaptive parameter tuning, combined with robust feature engineering for seasonality and automated model logging and drift detection, results in more accurate sales attribution, improved return on advertising spend (ROAS) predictions and enhanced strategic budget allocation. This paper details the methodological advancements, presents the underlying mathematical formulations, and discusses the practical implications and benefits for marketing practitioners seeking to maximise their marketing effectiveness. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: B2B sales; predictive analytics; lead scoring; opportunity scoring; sales forecasting; marketing analytics

Many A/B testing programmes collect, store, share and analyse individual-level customer data, even though the privacy-by-design principle of data minimisation holds that only the minimal data necessary to achieve the intended purpose should be used. This paper provides a method for A/B testing that supports privacy goals through data minimisation. An easy-to-apply solution is presented that relies on K-anonymity — a privacy method for enforcing data minimisation — and ordinary least squares regression. To illustrate the general utility of this approach, two advanced use cases in A/B testing are offered: (1) a method to discover both potential pairwise A/B test interactions and to surface segment-level average treatment effects (conditional ATE); and (2) a covariateadjusted framework for A/B tests based on ANCOVA2 for enhancing test precision and/ or reducing testing time. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: A/B testing; K-anonymity; ordinary least squares; privacy by design; privacy engineering

Customer segmentation is important for marketing and campaign management, and the RFM (recency, frequency and monetary) model is widely used for this purpose. However, existing algorithms typically segment customers based on overall similarity in R, F and M scores, which may not provide the most meaningful insights for retailers. A more effective approach involves grouping customers according to the distribution of their R, F and M scores rather than clustering those with similar values. To address this limitation, this paper introduces the DbRFMSS (distribution-based RFM score segmentation) method. The proposed method categorises customers based on the distribution of R, F and M scores, offering more actionable segmentation. While many tools, including Excel and other applications, can perform RFM analysis, the DbRFMSS method leverages machine learning algorithms to handle large datasets more precisely. Comparative analysis with the traditional RFM approach demonstrates that the proposed method performs better in terms of computation time, iteration efficiency and silhouette measures. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: RFM; frequency; k-means; monetary; recency; segmentation

Today’s digital marketing analysts must navigate evolving regulations, stringent privacy policies, an assortment of tools, a fragmented technical landscape and the rapidly evolving fields of machine learning (ML) and artificial intelligence (AI). This paper addresses these challenges by proposing a comprehensive framework that splits the digital marketer’s role into three areas: marketing strategy, marketing operations and marketing technology. Within this context, the paper reviews how digital marketing analysts currently leverage ML and analytics. It then explores recent advancements in AI that are hoped will address the shortcomings associated with current ML solutions, before turning its attention to the shortcomings associated with AI. The paper underscores how digital marketing analysts need to weave AI strategically into all aspects of their role if they are to remain competitive and relevant in the Web3 era. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI ethics; algorithmic bias; artificial intelligence; data privacy; digital marketing; machine learning; marketing ROI; marketing operations; marketing strategy; marketing technology; personalisation

The next major wave of value creation from generative artificial intelligence (AI) will come from enterprises implementing agentic systems. Previous waves of AI innovation (such as machine learning) have proven very effective at automating and enhancing largescale processes which have a quantitative element. In the marketing and advertising industry, this has been evident in the impact of predictive modelling for targeting and personalisation. However, other parts of the marketing industry, such as business-tobusiness account-based marketing, have proven harder to improve, because these rely on a lower volume of decisions with much higher dimensionality, supported by unstructured data that includes e-mail exchanges, proposal documents, product descriptions and so on. Generative AI agents possess the potential to add real value in these areas as they can reason over such data very effectively and generate actions that can be automated through account-based marketing and customer relationship marketing systems, enhancing business-to-business sales and marketing effectiveness. While much has been written about the strategic importance of agentic AI (and major industry players such as Hubspot and Salesforce are pushing agentic capabilities hard), this paper provides a practitionerfocused guide to the underlying architectural patterns and implementation choices and explores the governance and agent management practices that organisations will need to implement to reduce risks associated with agentic AI. This article is also included in The Business and Management Collection which can be accessed at http://hstalks/business.
Keywords: B2B marketing; RAG; agentic AI; governance; marketing automation platforms

This paper demonstrates the impact of a specific campaign setup best practice — frequency capping — on media performance, highlighting potential cost savings from optimising this setting. Focusing on this single practice, we show how its implementation can improve efficiency and reduce media waste. Analysing thousands of campaigns across DV360 and Meta, we assess whether frequency capping was enabled (compliant/non-compliant) and examine its influence on media spend and reach. An optimised XGBoost model, trained via grid search and cross-validation, estimates media spend based on delivered results. Counterfactual simulations on 500 campaigns show that enabling frequency capping can decrease media spend by 27–38 per cent without negatively impacting outcomes.1 These findings underscore the value of frequency capping as a targeted, data-driven strategy for enhancing advertising efficiency and managing budgets effectively. This article is also included in the Business & Management Collection which can be accessed at http://hstalks/business.
Keywords: XGBoost; advertising; efficiency; frequency capping; reach goals

This paper examines the transformative shift in AI-driven churn management toward dynamic, real-time and individualised interventions enabled by advanced predictive analytics platforms. By analysing recent industry trends and empirical case evidence, the study highlights how organisations leverage high-dimensional behavioural, transactional and contextual data to uncover micro-segments and emergent patterns that inform precise retention strategies. Central to effective AI implementation are robust data infrastructure, explainable and transparent models, and ethical governance frameworks that ensure responsible data stewardship and cross-functional collaboration. The paper further discusses emerging technologies such as reinforcement learning, multi-modal data integration and federated learning, which promise to enhance personalisation and adaptive retention efforts. Finally, it offers practical recommendations for researchers and practitioners to advance the efficacy and ethical deployment of AI in churn management, emphasising continuous model adaptation, interdisciplinary cooperation and the systematic evaluation of business outcomes. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence; customer churn; customer retention; data ethics; machine learning; personalisation; predictive analytics

This paper reviews the expanding literature on AI’s role in sales-force effectiveness, spanning lead generation, customer relationship enhancement, forecasting accuracy, personalised selling, team management and emerging applications such as generative artificial intelligence (AI) and reinforcement learning. Building on empirical studies that demonstrate up to 30 per cent gains in lead qualification, 20 per cent improvements in forecast accuracy, and notable productivity increases from AI-driven coaching and dynamic pricing, it highlights technological capabilities and ethical challenges around data quality, algorithmic bias and governance. Managerial implications emphasise the need for robust data infrastructure and phased AI deployment via pilot projects, cross-functional collaboration and continuous upskilling; they also underscore the importance of explainability and human–AI collaboration to maintain trust and strategic alignment. Concluding with practical guidance, the paper argues that organisations integrating AI responsibly, balancing innovation with ethical oversight, will secure competitive advantages, while setting an agenda for future research on sustainable, human-centred AI in sales management. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI; artificial intelligence; sales forecasting; sales team management; sales-force effectiveness; sales-force productivity

This paper advances total unduplicated reach and frequency (TURF) analysis by introducing a customisable reach definition. Traditional TURF methodologies, often reliant on complete enumeration, are computationally intensive and lack flexibility. The current authors’ binary linear programming model defines reach as customers selecting at least θ items, where θ may be any integer ≥1, enabling a more nuanced engagement analysis. The authors’ implementation can incorporate survey weights and produce multiple solutions despite using a binary linear programming framework that typically defaults to a single solution. The paper is complemented by a publicly accessible R Shiny web app, providing a practical, computationally feasible and adaptable TURF analysis tool that aligns with contemporary consumer research needs. Using a dataset from 200 female multivitamin/mineral gummy consumers, we demonstrate the model’s efficacy, revealing a product line that consistently ranks among the best flavour portfolios under varying assumptions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: TURF; binary linear programming; frequency; maximum coverage; product line optimisation; reach

The COVID-19 pandemic significantly impacted the airline sector, which has seen a shift in passenger behaviours and a decline in revenue. To navigate this challenging environment and regain customer trust, airlines must prioritise actions that focus on improving customer satisfaction, as satisfaction is a key driver of post-pandemic revenue growth. This paper proposes a novel predictive model for customer satisfaction using ensemble learning techniques. The analysis provides a comparison between the results of single supervised machine-learning methods, such as K-nearest neighbours and decision trees, and those of ensemble methods. The AdaBoost method with a decision tree as the base learner is found to achieve the highest accuracy, at 90.74 per cent. By enabling airlines to proactively address customer concerns and personalise offerings, this model has the potential to significantly improve customer satisfaction and ultimately drive sustainable revenue growth in the post-pandemic era.
Keywords: AdaBoost; customer satisfaction; ensemble classification; machine learning; random forest

This paper explores what the next generation of marketers should be learning about artificial intelligence (AI) and how undergraduate and graduate marketing curricula need to be updated to create the appropriate learning opportunities. Potential areas of focus range from understanding the design and marketing applications of AI to considering the ethical, regulatory and legal issues associated with the use of AI. By sharing perspectives on the question of what aspiring marketers need to know about AI, it is hoped that this paper will both elicit feedback and help marketing leaders develop their own roadmaps for training themselves and their teams. This paper is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: AI; generative AI; marketing education; marketing training; AI marketing

In today’s competitive landscape, poor audience targeting can drain resources, diminish campaign performance and erode customer trust. Brands that fail to deliver personalised, timely experiences risk low engagement and missed growth opportunities. To succeed, marketers must activate first-party data to deliver tailored messaging aligned with users’ real-time behaviours and preferences. Instant BigQuery Machine Learning (iBQML) provides a powerful, accessible way to operationalise machine learning on firstparty Google Analytics 4 data. It enables brands to run propensity models that predict the likelihood of high-value customer actions — helping refine targeting strategies, increase conversion efficiency and deepen customer relationships. By focusing on high-potential segments, brands can improve return on advertising spend, optimise conversion rates and foster long-term loyalty through personalised remarketing campaigns. As a lightweight alternative to more complex platforms like Vertex AI or DataRobot, iBQML is ideal for organisations looking to quickly adopt machine learning without deep technical investment. Nevertheless, it comes with limitations — such as a narrow range of model types, a lack of parameter tuning, and potentially high costs at scale — that should be carefully considered before long-term implementation. This paper discusses how iBQML can help businesses to stay competitive in an increasingly data-driven world. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: audience segmentation; iBQML; GA4; machine learning; propensity modelling; personalisation; first-party data

Quantifying the short-term activation and long-term brand-building role of advertising is a key part of commercial marketing mix modelling (MMM). To directly isolate each effect, a common practice applies both low and high-retention Adstock transforms to the same advertising variable. However, if, as is often claimed, activation and brandbuilding are different mechanisms, such a ‘dual-Adstock’ approach is inconsistent and inherently flawed. Not only does it characterise each effect in terms of the same type of distributed lag process, it approximates long-term behaviour with an ad hoc drift term. This can lead to spurious correlation issues, telling us nothing about the dynamics of long-term brand-building. In this paper we demonstrate the theoretical and statistical pitfalls of the dual-Adstock model with two empirical case studies. We argue that the only reliable way to separate both effects is to first identify whether a permanent (longterm) component is actually present in the sales data. If so, observed sales can be meaningfully decomposed into short-term stationary variation and long-term base sales evolution. Brand-building then equates to the role of advertising in driving the base, whereas activation corresponds to transitory movements around the base. To illustrate the point, we present three extant approaches, each focusing on an appropriate time series treatment of the long-term component. Contributions to the literature are twofold. Firstly, we demonstrate how a popular industry approach can lead to spurious claims of long-term effects, highlighting the need for more credible measurement techniques in MMM work. Secondly, we show how long-term effects can mean different things depending on the time series properties of the data. Consequently, if MMM practice is to provide meaningful industry generalisations, long-term effects must be clearly defined and measured. The paper concludes with managerial and industry implications, followed by avenues for future research. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: Adstock; marketing mix; short-term effects; long-term effects; unobserved component modelling; vector autoregression; cointegration

This paper investigates two fundamental problems in marketing mix analysis: (1) the lack of a standardised criterion variable across different media categories and nonmedia categories, and (2) the choice of analytical techniques. Using survey-based media and marketing influence variables, the paper uses multiple analytical models to predict top spenders in the category of women’s clothing. The analytical techniques considered include logistic regression, classification regression trees, cooperative game theory and Bayesian belief network. The Bayesian network is found to be the best-performing technique for delivering overall influence. All the analytical models significantly outperform simple pairwise selection models. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: Bayesian networks; media influence; media planning

Thanks to its ability to detect causality, A/B testing has become the standard method for identifying superior variants of advertisements or product features. At the same time, however, it has also become an obstacle to implementation, due to its reliance on large sample sizes and test durations to identify significant effects. Approaches such as stratification, CUPED (Controlled-Experiment using Pre-Experiment Data), p-value adjustment and multi-armed bandits have been developed to mitigate these issues, but managers still struggle to understand them and to select the best methods for established experimentation platforms. This paper addresses this problem by providing a comprehensive overview of selection criteria for these methods and a decision framework to assist managers in selecting the most appropriate approach for their specific situation to reduce time and opportunity costs. While stratification proves advantageous for reducing sample and time requirements in settings affecting primarily new users, CUPED benefits most in time and sample size constrained settings, affecting existing users. If time and sample size constraints persist despite the regular utilisation of these methods, p-value threshold adjustments can be contemplated at the organisational level. Multiarmed bandits have proven most suitable when immediate optimisation is imperative and relevance would have already passed, before a superior version could have been reliably identified using other methods. In conclusion, it is important to consider the organisational requirements as well as the specific task at hand when choosing an experimental method. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: experimentation; A/B testing; data-driven marketing; marketing performance measurement; marketing efficiency; p-value

This paper investigates how hyper-personalisation could revolutionise the marketing process with the aid of marketing analytics. As consumption data become more accessible, and as analytics advances, it is now possible for brands to create very specific campaigns. In this paper, best practice case studies of hyper-personalisation in some of the leading brands are critiqued with emphases on their methods, effectiveness and sustained value for enhancing customer engagement and loyalty. The principal findings are based on the examination of campaigns for diverse sectors such as retail, technology and entertainment, and cover customer categorisation, prediction and real-time personalisation. A literature review is provided, and several case studies are discussed to identify and understand the hyper-personalisation process. The paper outlines trends, patterns and lessons in the use of distributed data management technologies. It assesses the implications of hyperpersonalisation on brand—consumer intercourse, and proposes that any personalised endeavour should be harmonised with customers’ expectations and the norms of acceptable behaviour. This analysis also draws attention to how marketing analytics is important for precision marketing and for building consumer connections. Hence, the study emphasises key activities that foster hyper-personalisation success, including the use of data and information about the customer, immediate response, and emphasising value addition. Moreover, the paper expands on the issues arising from hyper-personalisation: data privacy and ethical issues, and interactions with technologies. The insights presented here aim to help marketers to better harness hyper-personalisation for competitive advantage and, at the same time, build accountability and strengthen trust among consumers. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: hyper-personalisation; marketing analytics; targeted campaigns; customer engagement; predictive modelling; data privacy; brand loyalty

This paper examines the role of user-generated content (UGC) in shaping brand image and fostering trust within the e-commerce industry. The study reveals that UGC significantly influences consumer decision-making processes, contributes to brand equity formation and is a critical trust-building mechanism in online retail environments. The analysis also identifies several research gaps, including the need for additional studies on UGC’s long-term effects, cross-platform UGC dynamics and cultural differences in UGC perception and creation. Drawing on these insights, we propose managerial implications for leveraging UGC effectively in e-commerce strategies. These include developing holistic, cross-platform UGC approaches, focusing on long-term brand equity building, adapting strategies to cultural contexts, balancing algorithmic curation with transparency and harnessing UGC for product innovation. The paper contributes to the growing body of literature on digital marketing and e-commerce while providing actionable insights for practitioners navigating the evolving landscape of online retail. This paper is also included in The Business & Management Collection which can be accessed at https://hstalks. com/business/.
Keywords: user-generated content; brand image; consumer trust; digital marketing; online retail