Volume 8 (2022-23)

Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. Articles scheduled for Volume 8 are available to view on the 'Forthcoming content' page.

The Articles published in Volume 8 include:

Volume 8 Number 2

Special Issue: Innovative methods to measure digital marketing analytics: Part 1

Guest Editors: Ana Reyes-Menendez and Nuria Ruiz-Lacaci, Rey Juan Carlos University, and Pedro Palos-Sanchez, University of Seville

  • Editorial: Innovative methods to measure digital marketing analytics: Part 1
    Ana Reyes-Menendez and Nuria Ruiz-Lacaci, Rey Juan Carlos University, and Pedro Palos-Sanchez, University of Seville
  • Practice papers
    How marketers can use the power of an AI/ML model to identify and predict customers
    Shashi Bellamkonda, Adjunct Professor, Georgetown University

    Marketers are increasingly faced with a lot of data and need to identify the best prospects. Sales teams are often sceptical of the data provided by marketing and possibly blame the quality of the prospect data provided by marketing. Enter a neutral party, a neural network, an AI/ML model that can analyse the current customers and provide a mechanism for identifying co-relations and similarities in larger prospect data and increase the efficiency of sales teams.
    Keywords: marketing analytics, artificial intelligence, machine learning, data, modelling

  • How businesses of any size can use AI in a digital marketing strategy
    Martin Broadhurst, Director, Broadhurst Digital Limited

    In recent years it has been said that data is the new oil and marketing departments have never had much data to work with. At the same time, big tech companies are investing huge sums into artificial intelligence and machine learning (AI/ML) while making access to these tools easily available through cloud-computing services. This convergence of data abundance and easy access to AI/ML technology has created a huge opportunity for marketers to become more efficient and more effective through the use of AI. However, marketers are still struggling to take advantage of the opportunities presented by marketing AI technology due to a lack of knowledge within the profession. Organisations are educating marketing professionals through frameworks such as the 5Ps of Marketing AI to help identify use cases. The AI/ML marketing deployment matrix helps marketing professionals self-assess their organisation to better identify ways to utilise AI for their situation.
    Keywords: artificial intelligence, AI/ML, marketing AI, AI framework

  • Web3 and the future of marketing
    Kelly Cutler, Program Director and Faculty, Medill School, Northwestern University

    The rise of Web3 and related regulations have brought data privacy concerns to the forefront for brands and marketers. With the rise in legislation, the deprecation of third-party cookies and an eye towards transparency, Web3 promises a more open, permissionless online environment. This has implications, from ad targeting to the storage and collection of user data. New concepts including zero-party data, micro-experiences and non-fungible tokens (NFTs) represent trends that are emerging with Web3, but the future is still to be defined. Marketers must understand the implications of the changing data privacy landscape and discover new and creative ways of sourcing and collecting data that follow the new guidelines that are being established by legislators, as well as by walled gardens such as Apple, Meta and Google. To do so, they will have to experiment and innovate. Here, we will explore the evolution of the web from its earliest version into the future. We will also review the different ways marketers currently collect and use customer data. Finally, we will explore new ways for brands and marketers to stay relevant in this evolving digital ecosystem with zero-party data, integrated marketing technology, customer-centric governance and NFTs.
    Keywords: Web3, NFTs, blockchain, zero-party data, data privacy, walled gardens, digital marketing

  • C-3DP: A cross-cluster analysis model to identify latent categorical customer attributes
    Roger Kamena, Lead Data Scientist and Head of Innovation and R&D, Adviso Conseil Inc.

    Using two-dimensional clustering methods to segment a customer database is a popular practice. The advantage of two-dimensional clustering is the ability to map customers according to a well-defined business logic. For example, how many segments can be identified based on the customers’ age group and RFM score? Such an approach also has the advantage of reducing the dimensionality of datasets and a model’s training time. Conversely, the trade-off of clustering on two dimensions is to ignore all the other dimensions potentially available in a CRM or web analytics platform. As such, the qualitative traits analysis of each segment from available customer dimensions can be challenging, especially for categorical dimensions with higher cardinality. In order to maximise the customer insights derived from cluster analysis, the paper proposes a qualitative trait prevalence scoring system: the C-3DP index (categorical density, dominance and diversity prevalence index). This technique maps a subset of dominant qualitative segment traits using a simple algorithm, as opposed to relying solely on traditional descriptive analytics approaches.
    Keywords: marketing segmentation, cluster analysis, machine learning, consumer insights

  • Attribution done right: How to prove the real value of marketing
    Moni Oloyede, Director of Marketing Infrastructure, Fidelis Cybersecurity

    Marketing attribution models are used by businesses to keep the marketing department accountable for the expenditure and the resources they use, as marketing is often seen as a cost centre within many organisations. Marketers implement marketing attribution models for two purposes. Firstly, to justify the money spent on marketing campaigns and activities, and secondly, to identify which marketing channels and tactics produce the desired results or outcomes. However, marketing departments continually struggle to use marketing attribution models to justify expenditure or show results. The most common reasons many marketers struggle to implement attribution models are issues with data integrity, lack of knowledge around how to effectively use marketing attribution tools and the sheer complexity of the buyer’s journey. In this paper, it is argued that the actual problem lies in how marketers perceive the use of attribution reporting. Furthermore, it is posited that those reasons are just symptoms of a larger problem with marketers’ approach to marketing attribution. If marketers want to use attribution successfully then they need to change the purpose of their attribution models to focus on customer metrics and not business outcomes. Nearly all marketing attribution focuses on return on investment as a desired outcome; however ROI is a business outcome, not a customer metric. Focusing on customer metrics such as brand awareness, brand engagement and churn rate will provide marketers with the informed outcomes they desire. The ability to gain insight from an attribution model comes from establishing the goal of the attribution model and then aligning the proper marketing metrics to the desired outcome.
    Keywords: marketing technology, marketing automation, business analytics, marketing attribution, dashboards and reporting, omni channel marketing, digital marketing

  • Net searcher sentiment: A web-search based replacement for net promoter score
    Isaac Gerber, Director, Commercial Insights and Analytics, Captify®

    In 2003, Bain consultant Frederick Reichheld published an article in Harvard Business Review arguing that the best predictor of top-line growth can be captured in a single survey question: ‘Would you recommend this company to a friend?’ In the almost 20 years since, Reichheld’s question, referred to as net promoter score (NPS), has become a standard metric for many organisations, complementing and even replacing more comprehensive customer satisfaction surveys. Yet, just as Reichheld argued in 2003 that companies were measuring the wrong thing, NPS itself may be the wrong measure. As documented in recent literature, NPS captures self-reported attitudes versus behaviour, is prone to sampling bias and runs the risk of non-participation bias. This paper explores the creation of a new metric, net searcher sentiment (NSS), that replaces NPS’s reliance on survey data with aggregated search data. This paper lays out the benefits of NPS, its shortcomings as documented in the relevant literature, the methodology behind NSS, how it addresses the shortcomings of NPS, and provides two examples of NSS in action.
    Keywords: net promoter score, brand measurement, growth, brand loyalty, search data, SEO, marketing

  • Empirical measurement of marketing analytics orientation: Quantifying the factors that create highly analytical marketing practices
    Anthony F. Branda, Chief Data and Analytics Officer, ASB Bank Limited, Mark Weber, Research and Analytics Consultant, WellSpring Consulting, and Eduardo Lucio, Chapter Lead for Data Science, ASB Bank Limited

    Marketing analytics involves use of data tools to quantify and monitor marketing performance (MP) to both optimise investments in marketing programmes and maximise customer interaction. Built on market orientation (MO) theories, this study establishes a construct and measure of marketing analytics orientation (MAO) to assess factors that predict MAO. A novel measure that provides empirical evidence of MAO and its relationship to MP is presented using data collected from a survey of company leadership and marketing analytics professionals in real-world businesses. The results demonstrate that people effectiveness and analytic skill sets mediate the relationship between MAO and MP. These findings describe a new tool that may help data scientists and business owners better understand the drivers of marketing analytics maturity, including the importance of professional analytics skills, top management support, data governance, the credibility of the analytics organisation, and the adoption of analytics within corporate culture.
    Keywords: Marketing analytics orientation, marketing performance, marketing analytics, market orientation, data analytics governance, analytics adoption

  • Research paper
    The impact of digital marketing on the performance of firms in Tunisia
    Bechir Fridhi, Assistant Professor, College of Business Administration, Majmaah University

    The objective of this study is to examine digital marketing in Tunisia and its benefits as a business model used by Tunisian businesses and to determine the influence of digital transformation on the performance of firms in Tunisia. In addition, the objective of our empirical survey is to define the capacities and skills essential to the success of a digital transformation. The study focused on: (1) establishing the extent to which digital marketing has been adopted by firms in Tunisia and (2) determining the influence of digital transformation on the open innovation performance of Tunisian firms. The result of the integration of technological power into the management of enterprises gives rise to a digitalisation that disrupts marketing strategies, significantly contributing to the creation of new business models. The results revealed that the use of digital marketing is significantly linked to three open innovation performance metrics: increased revenue, acquisition of new customers and retention of existing customers. This open innovation performance also has a positive and significant relationship with the use of digital channels by customers, showing that customers using digital channels in their purchasing process will tend to buy, consume and be more loyal than customers using the classic channels.
    Keywords: digital marketing, digital transformation, partial least square (PLS) approach, open innovation performance

  • An explorative study of salient usability attributes affecting m-commerce consumer behaviour in a Nordic context
    Lasse Baungård Løber, Assistant Professor, UCL University College, Denmark and Simon Svendsen, Analytics Translator, Danfoss A/S

    M-commerce, or mobile commerce, refers to smartphone shopping and plays an increasingly large role within e-commerce. Past research in the field of information systems (IS) success has primarily consisted of quantitative studies. Little research has been conducted in an m-commerce context. Furthermore, few qualitative works on the intersection of success factors from firms’ perspective and m-commerce exist. In fact, there is a knowledge gap regarding how business practitioners can increase their website usability in an m-commerce setting. Based on the mapping of usability attributes by Kuan et al., which elaborates on DeLone and McLean’s IS Success Model, this paper aims to identify the most important website usability attributes by generating new insights about mobile consumers’ behaviour. This is obtained via a qualitative approach, using data about consumer behaviour on four Nordic m-commerce webshops within the baby products industry. Eight respondents completed a smartphone task while verbalising their thoughts. This data was supplemented with eye-tracking information. The theoretical model was operationalised via creating 19 codes corresponding to 19 usability attributes. As such, this paper took a deductive approach, coding the data in NVivo by tagging labels to categorical labels. Based on the number of references for each categorical label, three salient attributes were identified to increase m-commerce website usability: ‘ease of navigation’, ‘relevance’ and ‘effectiveness of product search and comparison’. The managerial implications include recommendations for these three attributes, including the use of categorisation and filtering systems, avoiding uncertainty and minimising the potential negative consequences related to the purchase intention and enabling on-page product and consumer product experience comparisons. While this study is based on a small data sample, and represents a minor exploration into a niche market, it nonetheless provides a starting point for business practitioners who have the goal of increasing usability in an m-commerce context.
    Keywords: m-commerce, usability, information systems success, e-commerce, online shopping, user experience

Volume 8 Number 1

  • Editorial: Towards a theory of analytics and guidelines for practitioners
    Marco Vriens, CEO, Kwantum Analytics
  • Practice papers
    Unlocking the full potential of social listening platforms through prescriptive-based intelligence
    Mike McGuirk, Lecturer, Babson College

    Today’s business leaders are demanding access to information that will help them make more informed decisions. The widespread shift to customer-centric business practices, which require a much deeper understanding of consumer needs, emotions, and behaviours, is a primary driver of this heightened thirst for more data. This data obsession has triggered an unprecedented level of innovation in marketing technology. Particularly, innovation in platforms that can ingest and analyse new sources of consumer data. Social listening platforms are an important contributor to this new data collection and analytics ecosystem. This paper explores the adoption of social listening platforms and social analytics practices and details the distinct and highly valuable benefits these solutions can provide marketers and customer experience management executives.
    Keywords: social listening, social analytics, customer analytics, marketing analytics, market research, marketing, customer experience

  • Customer lifetime value: How to find the right calculation and prediction approach
    Shirley Coleman, Technical Director at NU Solve, Newcastle University, et al.

    In this age of abundant data, there are special opportunities for companies to measure the value of their customers. Such analytical action can help inform business and marketing decisions. This article gives an overview of what is meant by customer lifetime value and describes four approaches to calculating its value. We compare the pros and cons of each approach and show how engaging with the measurement activity can be beneficial for your business. Finally, the article gives guidelines so that managers can decide which approach best fits their current situation.
    Keywords: data science, statistical models, marketing decisions, data scientist, predictive modelling, machine learning, small business, SME

  • The three stages of workforce optimisation: Moving beyond the industry standard
    Dakota Crisp, Data Scientist, RXA, et al.

    Erlang-C has long been the industry standard for call centre staffing. As call centres evolve and staffing concerns move to other industries, the standard methods don’t always work as expected. While traditional scheduling methods focused on translating historic demand into staffing needs, the estimation of demand and the logistics of scheduling employees have not always been equally considered. Through the analysis of multiple use cases from distinct industries, this approach evaluates all three stages of workforce optimisation and explores the gap between theory and reality.
    Keywords: optimisation, staffing, call centres, ensemble learning, Erlang-C, automotive, forecasting

  • The intuition behind machine learning in marketing: Linear TV attribution
    Mario Vinasco, Director BI and Analytics, Credit Sesame

    Linear or broadcast TV continues to be an important channel for lead generation and user acquisition but precise attribution to an ad is not possible; attribution methodologies include time-based windows, keyword search within those windows, pixels that fire within the same Wi-Fi, panels, etc. This paper describes a forecasting methodology that uses machine learning to analyse recent and historical time series of new user registrations as well as additional factors and variables that affect new user acquisition. We then use this forecast to construct a baseline of expected new user volumes and how we attribute new users above that baseline to TV.
    Keywords: Marketing analytics, forecasting, linear TV

  • Should you change your ad messaging or execution? It depends on brand age
    Koen Pauwels, Distinguished Professor of Marketing, Northeastern University, Adjunct Professor, BI Norwegian Business School, et al.

    Should advertisers change their message (what is said), just as they do the execution (how it is said) to reflect changing consumer preferences? This paper is the first to quantify how these ad components and their interplay affect brand sales. The authors define the concepts of market consistency and changes in ad executions and show how they interact with each other and with the brand’s age in their sales outcome. The empirical analysis confirms the hypotheses in the US minivan market. As a brand matures, executional variations become increasingly beneficial, but changing advertising messaging to remain consistent with customer preferences becomes less effective. For older brands with little executional variation, changing the ad message even reduces sales. The authors thus uncover important boundary conditions for the opposing theories that brands should ‘stick with their message’ versus ‘change with the times’ and advise how to manage advertising as the brand matures.
    Keywords: advertising, advertising messages and execution, brand management, brand maturity, Hausman-Taylor regression

  • The culture and leadership style combination that cultivates a best-in-class marketing organisation
    Laura Patterson, President, VisionEdge Marketing, Rita Egeland, Adjunct Marketing Professor and Kathleen Wong, Graduate Student, University of Texas at Dallas

    The 2021-2022 Marketing Organisation Value and Performance Management (MPM) Benchmark Study is the continuation of a longitudinal study that began in 2001. The purpose of the study is to gain insights into which high performing best-in-class (BIC) marketing organisations do better and differently from their peers to earn high marks from a randomly selected group of C-Suite executives. These executives assessed Marketing on its ability to measure and communicate its value, impact and contribution to the business. The 2021-2022 study focused on gaining insight into whether organisational culture and/or leadership style are factors that contribute to the emergence and success of BIC marketing organisations. The study addressed three C-Level questions: 1) Is there any relationship between organisational culture and/or leadership style and marketing organisations who excel at performance management? 2) If so, is there an optimum culture or leadership style combination? 3) What do marketing organisations, classified as Value Creators, do differently in terms of turning data into insights, performance measurement and reporting, and processes? Based on the findings, the report identifies what business and marketing leaders can do to cultivate a Value Creator marketing organisation.
    Keywords: Marketing performance management, performance management, marketing effectiveness, marketing accountability, marketing measurement, marketing metrics, MPM, marketing analytics, organisational culture, leadership styles

  • Why multi-disciplinary, cross-silo teams are best at analysing and actioning data collected along the customer journey
    Victoria Jenner, Head of Customer Insights & Analytics, Harrods, et al.

    Enter 2021, 90 years since the famous McElroy ‘Brand Men’ memo also marked the start of the shift of ‘brand activities’ from advertising to full ownership of everything needed to produce business results: ‘Where brand development is heavy and where it is progressing, examine carefully the combination of effort that seems to be clicking and try to apply this same treatment to other territories that are comparable.’ The explosion of sales and marketing channels since has spawned a plethora of marketing specialisms and seen businesses come almost full circle, with marketing departments focusing solely on incremental growth and campaign ROI, sometimes at risk of losing control of core activities. The pandemic of the last two years has accelerated the growth and importance of digital media and sales channels – another typically siloed team – in how customers research, consider and buy. Change is not only taking place in more obvious consumer markets, but also in B2B, where both customers and suppliers have been forced to move more of sales and service processes online. This is delivering surprisingly positive outcomes in many cases, with McKinsey’s research showing that buyers and sellers alike prefer the new digital reality. As the need to connect ‘base nurturing’ activities (which tend to be owned by sales departments) and ‘growth activities’ (usually led by marketers) becomes clearer and as the digitalisation of both sale and marketing becomes more mainstream, we are seeing the re-birth of the ownership of ‘line of sight to results’ across functional silos. This is happening with the creation of roles such as Chief Growth Officer, Chief Customer Officer and new-style CMOs or CSOs who take a data-first approach and tend to look further than their own discipline along the entire value chain for the customer. This hybrid position bridges traditional departmental silos such as business development, sales, marketing, operations, customer support and information technology. While those organisations are striving to understand their customers’ omni-channel journeys and add value all the way across this complex ecosystem of interactions, some historical models of analysis may no longer be able to cope with today’s cross-silo, omni-channel reality that requires end-to-end insights for many teams across the business. In this article we examine various useful, relevant, data science based approaches on which modern executives can rely to understand this new interconnected reality, allowing them to understand, nurture and grow their customer relationships, even in times of uncertainty and rapid change. We use examples of how traditionally marketing department-led activities such as customer segmentation and marketing investment analysis can evolve into a more cross-functional approach delivering better insights and, importantly, more value to the business.
    Keywords: data-science approaches, customer relationships, customer segmentation, marketing

  • Research paper
    How to choose the right influencer for a marketing strategy
    Aslı Diyadin Lenger, Assistant Professor of Business Administration, Istanbul Gelisim University

    This paper attempts to build a new model for deciding which social media influencers (SMIs) are suitable for the marketing strategies of businesses. How to choose social media influencers is an unanswered question in the literature, and this study attempts to fill this gap. The study employs a Multiple-Criteria Decision-Making (MCDM) method, the fuzzy decision-making trial and evaluation laboratory model (DEMATEL), to determine the relevance of criteria constructed. It observed SMIs on Instagram and reviewed insights from the literature. Five criteria are proposed for selecting the correct influencers: 1) positioning; 2) target; 3) budget; 4) past campaigns; and 5) the number of followers. Businesses were asked to evaluate these criteria according to their experience with using SMIs. The results suggest that the most important criterion is ‘target’, whereas ‘past campaigns’ is the least influential.
    Keywords: Influencer, marketing, social media influencers (SMIs), fuzzy DEMATEL, fuzzy logic