Volume 9 (2023-24)

Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. Articles published in Volume 9 include:

Volume 9 Number 4

Editorial
Michael Wexler
Practice Papers
Unlocking SEO testing insights: Leveraging quasi-experimental designs for effective experiments
António Fernandes, Marketing analyst, SEO Analytics, Toptal

With the development of marketing's digital landscape, experimentation has become crucial for companies' business planning. Testing can now be set and delivered within seconds, making optimisations faster than ever. However, traditional models, such as A/B testing and multi-variate testing, face challenges as marketing professionals seek to expand experimentation to areas where controlled conditions cannot be met. Among these is the case of search engine optimisation (SEO) experimentation, an area that is steadily becoming a crucial element in modern marketing strategies. This paper explores the use of quasi-experimental designs to overcome these challenges, allowing for the collection of insights when traditional experimental setups are unfeasible. Examples are provided to show how quasi-experiments can be effectively applied in multiple SEO scenarios, assessing the performance of the treated pages and using correlation to generate comparable control groups. It advocates for a shift in the mindset of digital marketing professionals, stressing the importance of adaptability in experimental approaches, underscoring the necessity to embrace quasi-experimental designs in modern marketing strategies by highlighting their pivotal role in achieving data-driven insights in an increasingly complex digital world.
Keywords: quasi-experimental design; marketing experimentation; SEO testing; digital marketing; search engine algorithms; marketing strategies; data-driven insights
The future of personalisation: What new technologies are learning about customers
Carolyn Tang Kmet, Clinical Associate Professor, and Jonathan Copulsky, Senior Lecturer, Northwestern University

Existing and emerging digital technologies provide marketers with more robust data and contextual knowledge that enables them to improve consumer receptivity to marketing communications and offers by more effectively targeting and personalising interactions. Consumers may grant permissions to many of these new technologies, allowing the technologies to gather extensive amounts of personal information without consumers fully realising what information is being collected and how this information is being applied. The explosive growth of these technologies requires better customer education, a more robust regulatory framework and greater transparency from marketers.
Keywords: personalisation; data; context; receptivity; privacy; regulation; biometrics
In search of customer delight: Integrating customer satisfaction and NPS metrics
Art Weinstein, Professor of Marketing, Nova Southeastern University

Successful companies know that they must delight their customers to ensure their loyalty and grow their businesses. Building on a dual perspective of academic and industry findings, this paper clarifies the customer delight construct. According to the marketing literature, customer delight (CD) may be viewed as: (1) a distinct emotional state where customer expectations are exceeded, or (2) an extreme form of customer satisfaction (CS). These two positions are assessed with particular attention paid to measurement approaches and challenges. CS and net promoter score (NPS) metrics are often used to capture and explicate the meaning of customer delight. Given the strategic importance of these well-established measurement tools, CS and NPS are reviewed and critiqued. The need for a marketing metrics mindset is also discussed. The paper concludes with a 9-point plan for marketing management to successfully implement and evaluate customer delight in organisations.
Keywords: customer centricity; customer delight; customer obsession; customer satisfaction; marketing metrics; net promoter score; service experience
Research Papers
ChatBot preference and personality
Martin P. Block, Professor Emeritus of Integrated Marketing Communications, Medill School, Northwestern University

Chatbots are a growing subject of interest to marketers. This paper analyses the comparison of Prosper Insights and Analytics September 2023 data on consumer preference for chatbots to a live person, including the Big Five personality dimensions. Chatbot preferers are younger, have children and are also more likely to work at home. They prefer contactless retail activities and use ChatGPT. The personality characteristics that most strongly associate with chatbot preference are conscientiousness (efficient versus extravagant), emotional stability (sensitive versus confident), agreeableness (friendly versus critical) and openness (curious versus cautious). Most chatbot preferers report more emotional conditions, such as anxiety, depression, loneliness and anger. Emotional involvement in chatbot preference varies by personality characteristics. High levels of chatbot preference are associated with big spending plans; therefore understanding them is crucial for marketers.
Keywords: chatbots; Big Five personality dimensions; ChatGPT; AI
How is your loyalty programme going? A framework for a loyalty programme performance indication system
Hyung Su Kim, Professor, Hansung University, Dae Yun Park, Research Professor, California State University, and Su Hyun Kim, Researcher, Customer Management Institute

One of the root causes of the failure of membership programmes introduced by many companies is the lack of a systematic performance evaluation framework that can design, monitor and evaluate membership programmes tailored to the company’s own circumstances. This paper proposes a loyalty programme performance evaluation framework called MemPIS to help use the loyalty programme as a strategic platform for digital customer management and to provide a standard for optimising a company's loyalty programme. Using the MemPIS framework, this paper emphasises profitability, stability, growth potential and activeness of membership when evaluating programmes from a financial perspective, and suitability of tier design, reward design, point design and membership operation for the evaluation of programmes from a non-financial perspective. After developing candidate evaluation indicators and measurable matrixes for each evaluated subject, these are applied to a Korean cosmetics company to test the practical validity of the research and to suggest improvements to the membership programme to the company. Weights for all evaluative perspectives, subjects and indicators evaluated were calculated through AHP analysis. This research found that MemPIS can not only reveal the problems of a company's membership programme, but also suggest directions for improvement in the future.
Keywords: loyalty programme; CRM; membership programme; loyalty programme performance measurement
Demystifying metaverse data from user-technology interaction
Reshmi Manna, Associate Professor, Dr. Vishwanath Karad MIT World Peace University, Ankit Singh, Business Analyst, Lintl Clothing, and Monica Apte, Associate Professor, Dr. Vishwanath Karad MIT World Peace University

Metaverse data is the collection of detailed information generated from the user's interactions that take place within virtual and augmented realities. Metaverse data includes capturing motion information, recognising gestures, interaction and speech, emotion state assessment and evidence of eye metrics to understand user behaviour in virtual worlds. This paper will explore the different types of metaverse data as well as their implications for marketing efforts. It will also discuss how these technologies can help businesses better understand their customers and cater to their needs in an ever-evolving digital landscape.
Keywords: metaverse data; artificial intelligence; machine learning; synthetic data; virtual reality; augmented reality; capturing motion data; recognising gestures; interaction and speech data; emotion state assessment; eye metrics
Eco-friendly FMCG products and premiumisation purchasing habits
Armand Faganel, Vice-Dean and Marketing Professor, University of Primorska and Maurizio Dessardo, Customs Declarant, Fersped

This paper analyses consumers' awareness using the example of fast-moving consumer goods (FMCG). In this regard, two concepts are discussed in the theoretical part: premiumisation and eco-friendly products (EFPs). The empirical part utilises two methods for collecting primary data: semi-structured interviews with representatives of two companies and a questionnaire survey for end customers. Subsequently, consumers' awareness and purchasing habits have been examined based on a large international company's FMCG products, specifically for personal hygiene, cosmetics and detergents. Management in the FMCG sector has to focus on changing consumer habits. Marketers have to be aware of the desires, trends and demands of their customers.
Keywords: eco-friendly products; EFPs; premiumisation; fast-moving consumer goods; FMCG; sales trends; awareness; consumers

Volume 9 Number 3

Special issue: Generative AI and its impact on marketing analytics
Guest editor: Jim Sterne, Founder, Marketing Analytics Summit

Editorial
Jim Sterne, Guest-editor
Practice Papers
The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide
Jeff Coyle and Stephen Jeske, MarketMuse

AI-powered collaboration is quickly advancing due to a convergence of technologies like large language models and language model programming. These developments have spawned the rise of AI (artificial intelligence) copilots, which are changing the way marketing analysts make decisions and boost productivity. This paper explores the capabilities of AI copilots and delves into the challenges, ethical considerations and data privacy issues that come with their adoption. It discusses real-world applications and future trends in the AI copilot landscape. The paper also emphasises the importance of data integration and personalisation in marketing strategies and offers insights into training and skill development for effective collaboration with AI copilots. This comprehensive analysis aims to equip marketing analytics professionals for the future of AI-powered collaboration.
Keywords: artificial intelligence copilot; AI-powered collaborator; large language model; knowledge graph; language model query language
What executives need to know about knowledge management, large language models and generative AI
Seth Earley, Earley Information Science

This paper discusses the opportunities and risks presented by large language models (LLMs), which power the popular and widely adopted Chat-GPT types of applications. The potential benefits include support for enhancing the customer journey and efficient management of an ever-increasing volume of information for employees. Risks include hallucinations (made up answers by generative AI that are not factually correct), exposure of corporate intellectual property (IP) to training models, lack of traceability and audit trails and misalignment with brand guidelines. The approach to handling risk described in this paper is retrieval-augmented generation (RAG), which references corporate knowledge and data sources in order to identify precise answers and retrieve exactly what users want. The paper also outlines the need for a knowledge architecture which enables enriched embeddings into vector databases which retain the context of intelligently componentised content. Using RAG requires knowledge hygiene and metadata models, and the paper discusses an experiment in which results were measured with and without the knowledge architecture. The improvement was significant: 53 per cent of questions were answered correctly without the model versus 83 per cent with the model. The use of RAG virtually eliminated hallucinations, secured corporate IP and provided traceability and an audit trail.
Keywords: RAG; retrieval augmented generation; generative AI; ChatGPT; LLMs; large language models; KM; knowledge management; LLM challenges; LLM solutions; knowledge models; metadata models; knowledge architecture
Extracting actionable insights and advocating for data-driven change
Brandie Green, Boston Scientific

The vast majority of leaders in organisations report that they have low confidence in their employees to leverage data in ways that will allow their companies to undergo data-driven transformation. As technology has evolved, businesses are struggling to define clear data-focused roles and the data literacy knowledge gap has become more apparent. This paper provides a framework for data advocates to create an environment in which stakeholders can understand how web data fits into the organisation's overall strategy and discover how to transform their business by leveraging actionable insights.
Keywords: data literacy; data advocacy; data-driven change; analytics; change management; insights
Can data save small businesses? Benefits and challenges of data analytics adoption among small-sized retailers
Naeun (Lauren) Kim, Terry Haekyung Kim and Jinsu Park, Design, Housing, and Apparel, College of Design, University of Minnesota

Small businesses were recently hit hard by the COVID-19 pandemic, and there has been a crucial need for them to meet changing consumer behaviour and industry trends through data analytics. The purpose of this study is to understand the status of the adoption and utilisation of data analytics among small-sized retailers. Through in-depth interviews with US small business owners in the retail industry, the findings provide an overview of data analytics implementation status among small businesses. With regard to technological factors, the advantages of data analytics utilisation were identified (ie a better understanding of customers, strategic decision making, optimisation of marketing tools and accurate sales/trends forecast). Technological difficulties in understanding data and data analytics tools appeared as challenges. With regard to organisational factors, the small size of the firm and top management support were identified to be influential factors in the data analytics adoption process. However, lack of time, training resources and human resources were identified as major organisational challenges. As for environmental factors, macroeconomic contexts such as the COVID-19 pandemic and dynamic market trends influenced small businesses to adopt data analytics. Based on the technology-organisation-environment framework, the findings contribute to SME research and become a stepping stone to deriving a new framework.
Keywords: SME; data analytics; technology-organisation-environment framework; COVID-19; social media data
Use cases of large language models in marketing analytics
Katherine Robbert, Christopher Penn and John Wall, Trust Insights

This paper explores the use cases of large language models (LLMs) in marketing analytics. The authors introduce generative artificial intelligence (AI) and its application in marketing, focusing on LLMs and their underlying architectures of transformers and diffusers. The paper discusses various use cases of LLMs in marketing, including marketing strategy analysis, data summarisation and recommendations, analysis and insights generation, bias reduction, increased productivity, trend spotting and risk management. It emphasises the importance of skilled team collaboration, subject matter expertise and careful preparation when implementing generative AI in marketing analytics. The authors also address the risks and measurement of performance associated with the use of generative AI in marketing.
Keywords: large language models (LLMs); marketing analytics; generative artificial intelligence (AI); marketing strategy analysis; data summarisation; recommendations; analysis and insights generation; bias reduction; increased productivity; trend spotting
Using generative AI to turbocharge digital marketing
Ian Thomas, Yew Tree Data Consulting

AI has been used in marketing for some time to enable better targeting and optimisation of messages, but some of the benefits of these approaches have been limited by the ability to create personalised content at scale and the ability to measure the effectiveness of this content in a structured way. Generative AI offers a way to address these challenges and to facilitate integrations that make it easier to execute and measure marketing in a fragmented ecosystem. However, the technology presents a series of technical, privacy and copyright challenges that organisations will need to overcome in order to use it effectively.
Keywords: generative AI; OpenAI; digital marketing; GDPR; LLMs; privacy; personalisation
Customer journey optimisation using large language models: Best practices and pitfalls in generative AI
Vaikunth Thukral, Teradata, et al

Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.
Keywords: large language models; LLM; generative AI; GenAI; AI; marketing; customer experience; CX

Volume 9 Number 2

Editorial
Martin Squires, Visiting Professor Geography/Geospatial Analysis & Computing, UCL; Senior Solutions Consultant (Advanced Analytics), Merkle
To ChatGPT or not to ChatGPT: A note to marketing executives
Jacques Bughin, CEO, MachaonAdvisory

Generative AI technologies have taken the world by storm recently, and are quickly invading the sphere of enterprise, especially in sales and marketing functions. Various stages of the evolution of AI, and what makes generative AI a possible breakthrough, with a variety of examples in the context of marketing, are discussed in this paper. Caution, however, should be adopted when using generative AI technology, even if competition is frantically adopting this technology and claims great success in using generative AI. In this paper five questions are proposed that should serve as an acid test on what posture marketing executives should adopt when using these new, powerful AI technologies.
Keywords: generative AI; neuronal AI; neurosymbolic AI; ChatGPT; generative AI marketing
A new model for optimal advertising impression allocation across consumer segments
Joel Rubinson, President, Rubinson Partners, Neil B. Morley, Vice President of Marketing Solutions Product, TransUnion, Vassilis Bakapoulos, Senior Vice President – Global Head of Measurement, Insights & Strategy, MMA Global and Marc Vermut, Vice President of Marketing Solutions Knowledge Lab, TransUnion

With conflicting recommendations and marketer practices about advertising impression allocation approaches (ie ‘the media strategy’), from approaches centred on reach (‘go broad’) versus targeting (‘get specific’), the debate rages on: ‘Are marketers targeting too much, not enough, or simply targeting the wrong consumers with their advertising?’ This paper interprets the issue of targeting as an advertising impression allocation question and instead of leading with case study evidence which by its nature is parochial, uses a novel mathematical approach to create an ad impression allocation model based on probability of choice. This contrasts with broad reach strategies and is different from other targeting schemes, eg key demographic, high lifetime value consumers, non-buyers for conquest, proprietary segments of interest. The findings suggest that targeting Movable Middles, ie those with a 20–80 per cent probability of choosing the brand of interest, can lead to 50 per cent improvement in return on ad spending (ROAS) versus broad reach media plans. The results are then supported with two in a large scale market case study. The Movable Middle, a segment of category buyers with a 20–80 per cent probability of choosing a brand, are shown to generate 2–23 times more ROAS than other category buyers who are mostly non-buyers of a brand. This pattern was uncovered mathematically but then subsequently verified empirically. By shifting about 10 per cent of ad impressions to audiences that have high concentrations of Movable Middles, a typical 10 per cent share brand can expect a 50 per cent improvement in campaign ROAS and a 13 per cent improvement in converting non-buyers. This leads to better serving brand needs for both quarterly sales and for long-term growth via customer acquisition. This new media strategy is not just limited to digital campaigns; it can be implemented across any media channel, including linear TV, radio and print.
Keywords: advertising; targeting; Movable Middle; lift; media strategy; return on ad spending (ROAS); probability of purchase
Automated cluster generation and labelling of peer groups for marketing reporting
Dakota Crisp, Analytics Manager, OneMagnify, Jonathan Prantner, Chief Analytics Officer, OneMagnify, Grant Miller, Data Scientist, USIC, Jack Claucherty, Analytics Manager, OneMagnify, Tom Thomas, Vice President of Data Strategy, Analytics & Business Intelligence, FordDirect and Danielle Barnes, Analytics Director, OneMagnify

In today's data-driven marketing landscape, clustering data helps businesses better understand themselves and their customers. However, clusters derived from machine learning can be difficult to interpret and obtain buy-in from stakeholders. This paper details a method for automated cluster generation and labelling using machine learning. Two automotive case studies are provided where clustering enhanced business value and gained stakeholder buy-in. The first details segmenting dealerships based on their media environment to produce higher quality media models for lead generation. The second entails the creation of peer groups to enhance performance reporting across a diverse set of dealerships.
Keywords: clustering; automotive; labelling; peer groups; segmentation
Generative AI: A master or servant of market research analysis?
Andy Buckley, Global Solutions Partner, Human8

This paper explores generative AI's potential impact on the analytics element of the market research process, examining whether AI is destined to become an analysis master (which reduces humans to a minor role), or whether it will play the role of a faithful, trusted and tireless servant to human researchers. Version 4.0 of ChatGPT was used to conduct a series of tasks ranging from the analysis of desk research to primary research qualitative transcripts, quantitative survey open-ended comments and numerical data. The paper concludes that the hype around generative AI is indeed justified. In its current state of evolution, ChatGPT is an extraordinarily efficient extractor, organiser, processor and summariser of qualitative, quantitative and secondary research data. However, its capability is more akin to that of a competent junior consultant; for projects which require a greater experience and understanding of the human condition (empathy, intuition, creative and abstract thinking), humans remain as, if not more, important than ever in helping brands to remain relevant and grow in an increasingly fast-moving and complex world. The paper concludes that a generative AI like ChatGPT 4.0 is an extremely smart, tireless, diligent collaborator which frees (or perhaps forces) human researchers to up their game so they can apply their uniquely human skills and value to the research process.
Keywords: artificial intelligence; generative AI; ChatGPT; market research; disruption; analysis; human
Using customer feedback to prioritise remediation return on investment and improve customer experience
Manya Mayes, VP Data Science, 1440 Consulting

With the myriad of customer comments available on digital media, it is paramount for organisations to identify the breadth of compliments and complaints that customers are discussing, to analyse and understand drivers of customer sentiment and to know where to prioritise available resources such that the resolution of issues produces the biggest rewards for customers — and the business. Many organisations already successfully identify the issues that customers report (and there may be dozens of them). Often, they select the issues with the highest volume (impacting the most customers) and focus on resolving those first. Additionally, they may target the issues that have the highest negative sentiment. While both approaches are useful, they usually lack the ability to track newly developing issues/trends and, most importantly, are unable to accurately prioritise where to start (in the case of issues with similar volumes) and how to quantify the return on investment (ROI) associated with the remediation of each issue. This paper focuses on the technical capabilities needed to be able to identify the drivers of customer conversations and sentiment, the approaches needed to quantify both the importance of taking action from the customer's standpoint and the impact for the business in doing so, allowing a measured approach to improved customer experience. Analysing, prioritising and resolving cross-channel issues creates happier customers, higher rates of acquisition, increased repeat business and, ultimately, improves the bottom line.
Keywords: sentiment analysis; natural language processing; sensitivity analysis; brand and reputation management; competitive intelligence; customer experience; marketing analytics; digital analytics
Considerations in artificial intelligence-based marketing: An ethical perspective
Animesh Kumar Sharma, Research Scholar, and Rahul Sharma, Professor, Mittal School of Business, Lovely Professional University

The growing use of artificial intelligence (AI) in marketing poses several ethical concerns. Marketers must ensure the secure and productive application of customer data when using artificial intelligence. Moreover, despite its supposed impartiality, they must acknowledge the probability of partiality within AI. To ensure ethical practice, engineers and marketers should take measures such as respecting consumer privacy, verifying data accuracy and preventing algorithmic bias. Numerous kinds of research have demonstrated biases in facial recognition applications of artificial intelligence and machine learning. This has sparked intense study into the subject of fairness in machine learning and to promote algorithms some toolkits have been created to reduce biases and understand black box models. This study addresses ethical issues in the application of artificial intelligence in marketing and provides an overview of fairness concepts, methodologies and tools as they apply to marketing activities.
Keywords: ethics; artificial intelligence; machine learning; AI; ML; marketing
Data-driven attribute selection for hardware technology products: A multi-criteria framework
Evgeny A. Antipov, Associate Professor and Elena B. Pokryshevskaya, Associate Professor, HSE University

This paper outlines a multiple-criteria approach for supporting manufacturers in making decisions about tech products' technical, aesthetic and price characteristics. The authors propose a predictive modelling approach that shortlists efficient product designs based on their expected profit margin, consumer rating and demand. The method involves collecting SKU (stock keeping unit)-level data on product features from an online marketplace and estimating regression models. These models include a hedonic pricing model, a demand model and a satisfaction model to identify the factors that drive sales, prices and satisfaction. Analysing the model coefficients and their significance allows for identifying cost-efficient product features that positively impact sales and satisfaction. The models also enable predicting the outcomes for various new specifications making it possible to shortlist Pareto-efficient product designs. The approach uses publicly available data and allows for frequent updates, although it has some limitations, such as omitted variable bias and the use of a demand proxy. The authors suggest ways to extend the framework to account for uncertainty in predictions and include more outcomes of interest.
Keywords: product design; consumer preferences; demand estimation; hedonic pricing; satisfaction; regression analysis; machine learning; Pareto efficiency; multi-criteria comparison
Web3 and marketing: The new frontier
Brandon Chicotsky, Assistant Professor of Professional Practice in Marketing, Texas Christian University

In recent years, there has been a growing interest in decentralised technologies and the potential of Web3. These technologies have the potential to upend traditional business models and disrupt several industries. There is also a growing recognition of the need for new marketing approaches to promote these technologies and drive adoption. The ‘tech stack’ of Web3 is complex and still evolving, making it difficult to communicate its benefits to a mainstream audience. Additionally, the decentralised nature of these technologies presents unique marketing challenges since there is no single company or organisation that controls the narrative. This paper explores the market for Web3 technologies and the opportunities and challenges for marketing in this space. It begins with an overview of the Web3 marketplace, including a discussion of the size and scope of the market and the major trends driving growth. This is followed by a section on marketing in Web3, including a discussion of the unique challenges and opportunities posed by technology. Finally, the paper concludes with a case study of an effective marketing campaign for a Web3 project.
Keywords: decentralised technologies; Web3; business models; marketing approaches; tech stack; target marketing; marketing technology

Volume 9 Number 1

Editorial
Leslie Ament, Chief Research Officer (emeritus), Hypatia Research Group
Practice papers
ChatGPT and search engine optimisation: The future is here
Kelly Cutler, Lecturer, Northwestern University

The new chatbot from artificial intelligence company OpenAI, called ChatGPT, has grown aggressively since its launch in late 2022. ChatGPT provides information back to users who populate questions in a clear and easy to understand structure. This technology can be used for different purposes, including writing code, creating business proposals, writing stories and answering complex questions. Because this technology is considered a breakthrough in terms of its ability to process natural language, it stands to reason that it could have future effects on the fundamental digital marketing tactic known as search engine optimisation (SEO), a process by which websites are developed and updated with the goal of increasing natural ranking, traffic and customers from search engines like Google and Bing. For the past 20 years, people have turned to search engines for information, news articles, images, videos and answers to questions both mundane and complex. This technological advance with chatbots could indicate a significant general shift for marketers, specifically related to web searches and how marketers think about SEO. In fact, Microsoft's decision to invest in ChatGPT and include it within their search engine, Bing, has already created waves in the search engine ecosystems. In this paper, ChatGPT will be examined, its capabilities and how they could affect search marketing and SEO.
Keywords: ChatGPT; SEO; chat bots; digital marketing; search engine marketing; search engine optimisation; search marketing
Data democratisation requires literacy and fluency for proficiency
Jim Sterne, Business Scaling Consultant, Online Marketing Analytics

This paper discusses the challenges of data democratisation for non-digital-native organisations and the importance of data literacy in the process. Data democratisation refers to providing access to data to everyone to enable easy and informed decision-making without gatekeepers. Without a firm understanding of the basics and an educated grasp of the nuances, exploring data can be intriguing but not constructive. To be successful, organisations must get everybody, including those who are less knowledgeable, to have the same understanding of data and speak the same language in a collaborative manner. A common understanding of data is supported by data dictionaries, catalogues and legacy repositories. A common language allows teams to better choose which projects have the highest likelihood of success. The paper emphasises the need for data fluency across an organisation.
Keywords: collaboration; data democratisation; data fluency; data literacy; data proficiency; data-driven; language
Qualifying control data with propensity score matching
Dakota Crisp, Data Science Manager, RXA, et al.

The Fourth Industrial Revolution has brought with it a proliferation of data and an environment with ever-increasing complexity. While experimental design is the gold standard in assessing direct causal impact, the need for frequent business pivots and the abundance of pre-existing data makes quasi-experimental design a notable contender. Propensity score matching is one such quasi-experimental design tool that enables retrospective hypothesis testing, enabling businesses to use previously unviable data. This paper provides a case study of how this technique helps process nonrandomised data into viable analyses.
Keywords: automotive; control group; design of experiments; lift analysis; propensity score matching; quasi-experimental design
What is the right set of technologies and techniques to effectively analyse marketing effectiveness?
Pranav Patil, Customer Analytics Manager, Nextdoor

The evolving nature of the digital marketing ecosystem in a world where consumer privacy comes first requires marketing organisations to rethink how they measure campaign effectiveness and allocate budgets. A broad consensus exists within the ecosystem that methods and strategies that worked a few years ago do not give a clear picture of campaign effectiveness now. With signal loss, organisations need to be agile and think about how they can measure effectively and invest each dollar wisely. This paper describes, through the lens of a practitioner, the right set of measurement technologies that can help marketers effectively analyse campaign effectiveness. There is no single technical solution that provides an all-encompassing picture of the campaign's effectiveness; rather, it is a collection of solutions.
Keywords: cross-channel reporting; data analytics; marketing; marketing effectiveness; marketing technology; measurement strategy; media mix modelling; multi-touch attribution
Which key performance indicators should be used to establish a lead scoring strategy for customer relationship management?
Sergio Suárez, PhD Student and Ana Reyes-Menendez, Assistant Professor, Rey Juan Carlos University

Customer data has always been a company's most valuable asset, but now it increasingly represents the cornerstone of any company's marketing strategy. Therefore, this data must be optimised and leveraged effectively. This research aims to identify which Key Performance Indicators (KPIs) are essential for enabling personalised treatment of customers through the organisation's marketing strategy, using a lead scoring strategy to provide a different experience to each customer segment depending on their life cycle stage (top of the funnel awareness, middle of the funnel selection, bottom of the funnel ready to purchase). A survey was carried out of leaders in the advertising sector to understand their opinions on, and practices with, the different KPIs that this type of strategy should include.
Keywords: CRM; KPI; Key Performance Indicators; big data; customer data platform; lead scoring; marketing automation; personalisation; targeting; user profiling
Research papers
Using MASEM to explore the psychological mechanisms linking salespeople’s job satisfaction and performance
Chien-Chung Chen, Associate Professor of Marketing, Hong Chen, Assistant Professor of Informatics and Yan Liu, Assistant Professor of Management, Indiana University East

Satisfied and involved salespeople not only benefit themselves but also contribute to the profitability of their sales organisation. However, no sales research focuses on salespeople's psychological states driving salesperson involvement to sales performance. This study classified salespeople's psychological states influencing sales performance by the objects related to salespeople and the motivational states. The three psychological states are salespeople's motivations for the salespeople themselves (intrinsic motivation), their organisation (organisational commitment) and their customers (customer orientation). In addition, no meta-analysis or empirical sales study has yet identified and clarified the interactions between the salespeople's three psychological states or shown how they mediate the link between salespeople's job satisfaction and performance. This study involved the collection of 141 empirical sales articles (1971–2022) from 26 journals and coded 275 effect sizes with 72,668 survey responses. Meta-analytic structural equational modelling (MASEM) was used to analyse the data and test the conceptual model. The results support the hypotheses: salespeople's job satisfaction is positively related to intrinsic motivation and organisational commitment; salespeople's intrinsic motivation is positively related to job performance, organisational commitment and customer orientation; salespeople's organisational commitment is positively related to job performance; salespeople's customer orientation is positively related to job performance. However, two are not supported: the hypothesis proposing a relationship between salespeople's job satisfaction and customer orientation, and that proposing a link between salespeople's organisational commitment and customer orientation. Based on the findings, a map of the salespeople's three psychological states is proposed, illustrating the link between salespeople's job satisfaction and performance, revealing the dominant role of salespeople's intrinsic motivation in driving the other two psychological states, and identifying the two-way causal relationship between salespeople's intrinsic motivation and job satisfaction. This study also offers marketing analytic professionals a clear roadmap for what the crucial variables are and marketing managers practical advice on how to promote and control the sales force.
Keywords: MASEM; customer orientation; intrinsic motivation; meta-analytic structural equation modelling; organisational commitment; sales force management
Lengthen your attribution window: Which digital ads have most long-term impact?
Vivian Qin, Senior Data Scientist, Amazon Ads

Brands usually invest in a portfolio of digital ad products for brand consideration and conversion, and their performance is commonly evaluated with ad-attributed metrics. However, these metrics limit the measurement of advertising effectiveness within a short time window, typically of two weeks. Therefore, they could underestimate the total effect if some ad products' efficacy lasts beyond the measurement period. In particular, this could understate the impact from ad products aimed at awareness and consideration. In addition, this bias could manifest in product categories where shoppers' involvement is high because they are making deliberate purchase decisions. To solve these problems, the Vector Autoregressive Moving Average with Exogenous variables (VARMAX) model is applied, which allows flexibility in the length of the advertising measurement window, and thus can empirically quantify how long the effect of each ad lasts without a priori restrictions. For 15 US brands across three verticals (Hardlines, Softlines and Consumables) on Amazon, it was found that within the two-week attribution window, upper/middle-funnel ad products only materialise 30–50 per cent of the total effects, compared to lower-funnel at 60–90 per cent. Based on these results, it is recommended that advertisers and publishers lengthen the attribution window, and especially track their upper and middle-funnel ad products for at least a month to capture their longer-term effects.
Keywords: ROAS; attribution window; digital ads; e-commerce; long-term effects; performance metrics
Gen Z versus Millennials, purchase intentions: A comparative study based on social media marketing strategies in India
M. Thirumagal Vijaya, Associate Professor, PSG College of Arts & Science, et al.

The ubiquity of mobile phones and the Internet has affected every individual and their lifestyle. The Internet is used a great deal by organisations and companies to examine and analyse data and also for marketing. Millennials are now active participants in the Internet, having surpassed Generation Z, particularly in usage of social media, blogs, forums, wikis and other interactive online activities; voicing their opinions and preferences based on their experiences in online marketing and purchasing, especially during the pandemic. This study analyses the purchase intention of Generation Z and Millennials in India as a variable against online marketing strategies, with moderators based on age, income and gender. The four Ps are analysed against the purchase intention. Methodologies used for analyses are regression and Hayes process-macro with SPSS. The research finds that among the four Ps, place has no impact on purchase intention and gender does not moderate the relationship of the variables. Thus, the study concludes that price, promotion and product are significant factors for respondents who intend to purchase online, and age and income also play a vital role.
Keywords: Gen Z; Millennial; marketing strategies; online marketing; online purchase; purchase intentions