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 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, USA Adjunct Professor, BI Norwegian Business School, Norway

    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