Volume 10 (2024-25)

Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. Articles published in Volume 10 include:

Volume 10 Number 2

Editorial
What customer experience professionals should be doing as the battle lines between capitalism and individual rights continue to take shape
Paul Lima, Managing Partner, Lima Consulting Group, LLC
Practice Papers
Embracing cookieless advertising with AI
Ian Thomas, Founder/Chief Data Officer, Yew Tree Data Consulting

After several delays, the end of unrestricted use of third party cookies is now drawing near, forcing all parts of the digital advertising industry to reconsider how they can drive campaign performance and inventory monetisation without gathering user data. Major browser-makers such as Google are deploying and testing technologies to replicate some of the key capabilities of cookies to enable advertisers to continue working as they have before but the industry needs to find a new way of thinking about driving campaign performance which relies less on the idea of finding the perfect audience and more on implementing a set of connected optimisation techniques that drive performance while maintaining privacy. Fortunately, recent developments in AI (artificial intelligence), especially Generative AI, provide some valuable techniques for achieving this, while at the same time improving the online experience for consumers by serving them more relevant ads that more closely match the context in which they are seen.
Keywords: AI; Google; cookies; creative automation; optimisation; privacy; targeting
How consumer perceptions drive online ratings
Bradley Taylor, Company Owner, GITR Innovation, Insights and Analytics

In marketing, it is a commonly accepted belief that the image of a brand will drive, to some extent, the sales of the brand. The better the image within key areas, the better the sales. In the world of online retailers, the rating is assumed to play an important function, too, when it comes to new purchases. This paper seeks to demonstrate the relationship between ratings and brand image. However, it introduces the nuance that not all brand image perceptions drive ratings to the same degree. We will test these beliefs by using product reviews for snacking goods within the USA. Depending on the statistical results, correlation, causation or matching to randomness, we can strengthen or correct the beliefs that brand image and product experience affect the online rating. We also desire to show which is more robust, the image or experience, and if there is any interference in the brand from the realities of purchasing goods online, such as delivery issues.
Keywords: brand image; emotional connections; online ratings
Using patient lifetime value to future-proof your dental practice
Rosie Pritchett, Dentist, Dental Core Training, South Tyneside and Sunderland NHS Foundation Trust, et al.

Dental practices are a small business. Like any other business, they need cash flow management and financial planning to be viable, if not highly profitable. What a lot of practices may not realise is that they are sitting on a treasure trove of data to be used in more ways than plain accounting and financial forecasting. This paper focuses on longitudinal data, such as the timing of each patient's visits and the value of their treatments since joining the practice. It aims to show how such data can be used by practices to understand their patient base and make plans for the future development of the business. There are plenty of business metrics. Here, the focus is on one metric: patient lifetime value (PLV), derived from customer lifetime value (CLV). CLV is well established in retail and other sectors. This paper shows that patient appointment data can be used in methods adapted from CLV via the bespoke concept of PLV. The paper describes different approaches to calculating PLV, the advantages and disadvantages of each approach and the ways in which they can benefit a dental practice.
Keywords: business improvement; commitment; customer lifetime value; data science; decision making; loyalty; patient lifetime value; statistical models; trust
The three critical thinking skills marketers need to excel at for impactful data storytelling
Caroline Florence, Insight Narrator

Data democratisation means marketers have ever-greater access to data to help inform decisions. No longer in the hands of specialists, data is more readily available to use than ever before. However, despite increasing access to data tools, capabilities within marketing teams have not kept up with the pace of change. Investment in data tools will not lead to better data-enabled decision making unless supported by the right marketing capabilities. Critical thinking skills are often overlooked in data storytelling best practice, with more emphasis on visualisation and creative communications. However, it is the critical thinking skills that enable marketers to extract the value from data to ensure the stories both stand up to scrutiny and stand out from the noise. This paper focuses on the needs to build capabilities in three core areas: identifying the ‘so what?’ insights from the data, distilling insights into a few salient points of view and clarifying the ‘now what?’ recommendations.
Keywords: analysis; critical thinking; data literacy; data storytelling; insight; marketing effectiveness; skills
Research papers
Overcoming analysis paralysis in bulk: Efficient methods for extensive key driver analyses
Michael Dupin, Adjunct Professor, Merrimack College and Sophia Tannir, Data Scientist

In the rapidly changing field of market research, identifying the key drivers of customer relationships is essential for enhancing business strategies and customer satisfaction. This paper explores the application of driver analysis, a critical methodology that assists businesses in pinpointing the crucial factors influencing customer behaviour and satisfaction. By effectively distinguishing impactful elements from less relevant ones, this technique enables more precise decision making and strategy development. The core of this paper introduces an innovative method for categorising drivers into primary and secondary groups, simplifying the complex data landscape and focusing on the most influential factors. This new grouping method significantly reduces the analytical complexity typically associated with traditional models, making the insights more accessible and actionable for businesses. A case study utilising the Kiwis Count survey — a comprehensive public service evaluation in New Zealand — serves to illustrate this methodology in a real-world context. By applying the proposed method to this survey, the paper provides a detailed examination of how various demographic groups perceive public services and what drives their satisfaction. The results reveal distinct patterns in how different demographics value aspects of service delivery, from staff competence to trust and transparency. By focusing on the most impactful drivers, organisations can allocate resources more effectively, enhance customer experiences and ultimately achieve greater customer loyalty and success.
Keywords: Key Driver Analysis (KDA); categorisation; data-driven strategy; enhanced data interpretation; noise reduction
Machine learning and AI in marketing analytics: Leveraging the survey data to find customers
David Fogarty, Associate Professor, National University and Xinlei Cui, Graduate Research Assistant, New York University, Stern School of Business

The field of marketing analysis in the digital era faces numerous challenges. Despite the availability of vast amounts of structured and unstructured data, practitioners have yet to fully harness the potential of machine learning models. This paper addresses this gap by investigating how to find targeted customers and expand the emerging market by implementing machine learning models to process survey text data and provides empirical evidence through model evaluation experiments. The research problem focuses on demonstrating the effectiveness of machine learning and AI models in optimising value creation and enhancing competitive advantages in marketing practices. The paper employs mixed methods and presents experimental results, leading to conclusions highlighting the benefits of improving data quality to strengthen the performance of machine learning models. This research also provides insights into model selection and offers a foundation for future researchers and marketing analysts to interpret and evaluate machine learning models effectively by multiple efficient metrics.
Keywords: analytics; artificial intelligence; automated classification; consumer targeting; machine learning; marketing
Crossed signals: The negative effects of manufacturer warranty length on brand share in an independent retail channel
Robert J. Fisher, Alberta School of Business Research Chair in Marketing, University of Alberta, Yu Ma, Associate Professor of Marketing & Bensadoun Faculty Scholar, McGill University, and Barry Scholnick, Professor, Alberta School of Business, University of Alberta

Manufacturers’ attempts to signal quality via longer warranties can have unintended effects on independent retailers’ behaviour in categories where extended service contracts (ESCs) are offered. The results of this research indicate that independent retailers are likely to promote brands with shorter rather than longer manufacturer warranties to maximise the likelihood of selling an ESC as an option. This research provides an explanation as to why manufacturers find it difficult to use warranty length as a signal of product quality. This research is based on a field study of brand shares of household appliances and consumer electronics by a major retailer over a ten-year period. The results have important implications for manufacturer and retailer marketing strategies.
Keywords: extended service contracts; extended warranties; quality signalling; retailer incentives; warranty length

Volume 10 Number 1

Editorial
Simpler is better
Denis Malin, Editorial Board Member
Practice Papers
The evolution of digital marketing in the era of AI
Kelly Cutler, Business Leader, Author and Educator, Northwestern University

This paper explores the far-reaching impact of artificial intelligence (AI) on the evolving digital marketing landscape. In the digital age, marketing is undergoing a profound transformation led by data privacy and changing technology, with a focus on the benefits and the risks of adopting AI. Increased productivity and the value of AI must be balanced with careful ethical, legal and privacy considerations. Technologies like third party cookies, once the cornerstone of digital advertising, are being retired and replaced. Simultaneously, ad blockers, wielded by users seeking respite from intrusive advertisements, are reshaping the marketing paradigm. Marketers are at a crossroads. Insights and ideas will be explored pertaining to how AI is poised to reshape the way that marketers do business. While emphasising the role of AI in aspects of marketing such as customer engagement, personalisation, marketing automation, content curation, predictive analytics, campaign creation and more, there is also the need for oversight, management and responsible deployment. This can be accomplished by combining automation and technology with human intervention and direction. Careful examination of the potential benefits and the multifaceted risks posed by AI will define how marketers move into a privacy-centric digital future. This paper delves into the rapidly evolving digital marketing ecosystem as it adapts to changing user behaviours, tightening regulations and new technologies.
Keywords: digital marketing; data privacy; transparency; artificial intelligence and marketing; generative AI; AI and digital marketing
Customising generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights
Dakota Crisp, Senior Manager of Data Science, Jacob Newsted Data Engineer and Data Scientist, Brendon Kirouac, Data Scientist, Danielle Barnes Senior Director of Data Science, Catherine Hayes Senior Director of IT, and Jonathan Prantner Chief Analytics Officer, OneMagnify

This study delves into the transformative impact of leveraging large language models (LLMs) in marketing analytics, particularly emphasising a paradigm shift from fine-tuning models to the strategic application of document retrieval techniques and more. Focusing on innovative methods, such as retrieval augmented generation and low-rank adaptation, the paper explores how marketers can now activate against vast and unstructured datasets, such as call centre transcripts, unlocking valuable insights that were previously overlooked. By harnessing the power of document retrieval and adaptation, marketers can bring their data to life, enabling a more nuanced and adaptive approach to understanding consumer behaviour and preferences. This research contributes to the evolving landscape of applied marketing analytics by demonstrating the efficacy of document retrieval in enhancing the utilisation of LLMs for dynamic and data-driven marketing strategies.
Keywords: generative AI; marketing analytics; call centre; natural language processing; document retrieval techniques; retrieval augmented generation; low-rank adaptation
Improving voice of the customer analysis with generative AI
Jim Sterne, Applied Technology Evangelist and Thomas H. Davenport, Professor, President’s Distinguished Professor of Information Technology, Babson College

This paper explores the integration of generative artificial intelligence (GenAI) in voice of the customer (VoC) analysis to provide deeper understanding of prospects and customers. GenAI has enormous potential to enhance customer satisfaction, refine products and services and improve the customer experience. This speculative paper illustrates how GenAI can keep pace with increasing customer expectations and the volume of feedback by uncovering nuanced sentiments, trends and customer needs through context comprehension and its conversational query capabilities. The paper explores the power of GenAI in VoC analysis for improving customer satisfaction, accelerating troubleshooting and resolution and upgrading products and services. Additionally, this paper addresses the role of GenAI in advanced communication routing, agent support, multilingual support and sentiment analysis, showcasing its ability to provide comprehensive and context-aware insights.
Keywords: generative AI; GenAI; voice of the customer; VoC; customer satisfaction; sentiment analysis; customer feedback analysis
The recipe for success in creating frictionless customer journeys
Stephanie Burton, Expert Solution Consultant, Data & Insights, Adobe

Customers now demand seamless experiences across all points of contact. To deliver on this expectation, organisations need a reliable marketing architecture for accurate omnichannel customer journey analysis. This allows them to identify and eliminate friction points that hinder customer satisfaction. However, disparate channels of data often utilise different customer identifiers, creating a challenge in unifying data for comprehensive analysis. By focusing on a strong customer identity strategy and leveraging technological advancements to seamlessly combine data at the individual level, organisations gain an advantage in crafting consistent and frictionless customer experiences. This approach follows a specific recipe, with key steps to identify and remove friction points. Professionals involved in creating or measuring omnichannel experiences will find valuable insights and practical tips within this paper, along with learnings from renowned customer-centric companies like Uber, Netflix and Amazon. Delivering consistently exceptional customer experiences allows companies to command premium prices, build long-term brand loyalty and solidify their reputation as truly customer-focused organisations.
Keywords: customer experience; customer journey; customer identity; frictionless; seamless; omnichannel analysis; cross-channel analysis
The power of clarity: Understanding how the effective use of data storytelling can improve the decision-making process
Giovanna Fischer, Independent Consultant and Founder, Escola de Insights

This paper discusses the importance of communication in the data field and its potential to improve decision-making processes in corporations. This study adopts an approach intended to understand the current state of the art in the field. The paper proposes a framework for developing data storytelling as a tool that supports data-driven decisions.
Keywords: data; communication; storytelling; business intelligence; data storytelling; data-driven decisions; management
Strategies to mitigate hallucinations in large language models
Ranjeeta Bhattacharya, Senior Data Scientist, BNY Mellon, AI Hub

In the world of enterprise-level applications, the construction and utilisation of large language models (LLMs) carry a paramount significance, accompanied by the crucial task of mitigating hallucinations. These instances of generating factually inaccurate information pose challenges during both the initial development phase of LLMs and the subsequent refinement process through prompt engineering. This paper delves into a variety of approaches such as retrieval augmented generation, advanced prompting methodologies, harnessing the power of knowledge graphs, construction of entirely new LLMs from scratch etc, aimed at alleviating these challenges. The paper also underscores the indispensable role of human oversight and user education in addressing this evolving issue. As the field continues to evolve, the importance of continuous vigilance and adaptation cannot be overstated, with a focus on refining strategies to effectively combat hallucinations within LLMs.
Keywords: LLM; large language model; hallucination; prompt engineering; RAG
Research Papers
Predicting maintenance costs of an IT system using AI models
Nathan Bosch, Machine Learning Engineer, Lyft, Emmanuel Okafor, Postdoctoral Researcher, SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Marco Vriens, CEO, Kwantum and Lambert Schomaker, Professor in Artificial Intelligence, University of Groningen

Predictive maintenance is a maintenance policy where the goal is to detect potential future maintenance risks in a system so that the maintenance process can be optimised before system faults occur. This paper describes a deep learning model that does not require domain expertise. Deep learning approaches have several benefits over explicit statistical modelling: (1) they require far less domain-specific knowledge; (2) if the underlying data-generating mechanism of assets changes, a deep learning model would only need to be retrained to learn these new changes; (3) they can capture non-linear and complex multidimensional relationships; and (4) they may outperform rule-based or statistical methods. The paper describes how the model predicts maintenance-relevant events, along with the cost of the upcoming event and the time when it will happen. The paper describes the use of a long short-term memory architecture for our deep learning model. By doing so, the cost values represent a real, quantitative value of the potential maintenance costs in the future of an asset. Event, cost and time prediction are all achieved with high accuracy. This allows for the development of maintenance solutions without the initial high degree of domain process knowledge required. The artificial intelligence model can be used to raise an alarm when the cost values exceed some threshold, when the frequency of high-cost events increases significantly over the lifetime of an asset, or when the expected cost exceeds the cost of maintenance.
Keywords: predictive maintenance; deep learning; long short-term memory; LSTM; cost prediction; time prediction
Navigating compliance and regulations in marketing analytics: Upholding ethical standards and consumer trust
Animesh Kumar Sharma, Research Scholar, and Rahul Sharma, Professor, Lovely Professional University

This paper delves into the multifaceted marketing analytics compliance and regulation landscape across diverse business sectors and legal frameworks. It discusses a spectrum of norms with respect to overseeing data collection, processing and utilisation in marketing endeavours. Stringent global laws govern the handling of personal data, necessitating strict adherence. The paper scrutinises pivotal compliance elements like consent, transparency and data security alongside pivotal legislation like the California Consumer Privacy Act and the General Data Protection Regulation. It assesses the implications for marketing analytics, emphasising rights regarding personal data access, erasure anonymisation methods and ethical data use. Non-compliance repercussions, encompassing legal and financial risks and reputational harm, are highlighted, as many industries are facing distinct regulatory challenges. The paper details the essential components of policies, training, monitoring and enforcement that are crucial to ensuring marketing compliance. It stresses the role of technology, advocating for marketing compliance software to streamline processes, monitor compliance and adapt swiftly to regulatory shifts. It elucidates the collaborative nature needed within marketing teams to achieve effective compliance management. The conclusion highlights how compliance software helps with regulatory updates, data privacy, monitoring and content assessment. This paper emphasises the dynamic nature of marketing analytics compliance, urging vigilance with regard to legislative alterations and technological advancements. The paper provides a comprehensive insight into managing compliance challenges in this evolving field while upholding ethical standards and fostering consumer trust.
Keywords: marketing analytics; compliance and regulation; data protection legislation; non-compliance consequences; data compliance; risk management
Book Review
Search Marketing: A Strategic Approach to SEO and SEM, by Kelly Cutler
Reviewed by Shashi Bellamkonda, Principal Research Director, Info-Tech Research Group, and Adjunct Professor, Digital Marketing, Georgetown University