Volume 6 (2020)

Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. Articles scheduled for Volume 6 are available to view on the 'Forthcoming content' page.

The Articles published in Volume 6 include:

Volume 6 Number 1

  • Editorial: Marketing science and epidemiology
    Dominique M. Hanssens, Editorial Board Member, Applied Marketing Analytics
  • Using event-based models for cross-device insights into the user journey
    Mai Alowaish, Digital Marketing Consultant and Delivery Team Lead, InfoTrust

    Gaining customer insight is the foundation of marketing science. Today’s customers interact with companies across multiple devices. Thus, to fully understand the customer’s journey, digital marketing analysts can no longer rely on measuring individual events on a single device; rather, they must gather and interpret data across devices. This requires making the user the unit of data measurement. The challenge is stitching together information into a single story and profile that lead to actionable and immediate insights. This paper discusses event-based models for digital marketing data and calls for marketing analytics leaders to execute the transformation from static session-based reporting to dynamic and powerful user-centric analytics.
    Keywords: digital analytics, web analytics, app analytics, event-data models, Internet-of-Things, cross-device

  • Case study: In-store display and visual merchandising analytics
    Gary Angel, Founder and Chief Executive Officer, Digital Mortar

    In this project, video and advanced machine learning were used to analyse shopper behaviour in a key display area at multiple locations of a multi-billion-dollar retailer. The goal was to understand the volume of shoppers using the area, engagement with the displays, and whether the displays generated product interactions and takeaways. In addition, the system was used to design and support an aggressive testing programme to optimise the display area. Measurement answered the usage questions and revealed obvious opportunities for improvement. Structured testing revealed that the geometry of the area heavily impacted usage and engagement, that shopper flow was strongly influenced by changing the density and alignment of the features, and that there were opportunities for improving product mix and layout. As the purpose of a store is to get shoppers’ eyes and hands directly on product, the ability of product displays to attract and engage shoppers is critical to retail success. Like many aspects of physical retail, however, merchants have little visibility into the success of any given display and insufficient measurement to drive testing and improvement programmes. This case study shows how measurement and testing in display has become possible using people-counting technologies.
    Keywords: retail analytics, store analytics, merchandising, display, endcap, display measurement, display performance, display engagement

  • Sentiment analysis and emotion recognition: Evolving the paradigm of communication within data classification
    Ted William Gross, AI Technologist and Data Theorist, Ituran

    The process of sentiment analysis and emotion recognition (SAER) entails using artificial intelligence components and algorithms to extract emotions and sentiments from online texts, such as tweets. The information extracted can then be used by marketing, customer support and public relations teams to foster positive consumer attitudes. Advances in this discipline, however, are being hindered by two significant obstacles. First, although ‘emotion’ and ‘sentiment’ are distinct entities that require distinct analysis, there is no agreed definition to distinguish between the two. Secondly, the nature of language within the electronic medium has evolved to include much more than textual statements, including (but not limited to) acronyms, emojis and other visuals, such as video (in its many forms). As visual communication lacks universal interpretation, this can lead to erroneous analysis and conclusions, even where there is a differentiation between emotion and sentiment. This paper uses examples and case studies to explain the theoretical basis of the problem. It also offers conceptual direction regarding how to make SAER more accurate.
    Keywords: sentiment analysis, emotion recognition, contextual analysis, communication, emoji symbolisation, data analytics

  • Facebook and Pandora’s box: How using Big Data and Artificial Intelligence in advertising resulted in housing discrimination
    Sarah Khatry, Data Scientist and Writer, DataRobot

    In 2019, the US Department of Housing and Development charged Facebook with violating the Fair Housing Act 1968. This followed an investigation into the use of ethnically targeted advertising practices on Facebook. To understand Facebook’s targeting methods and the cause of the problematic outcomes, this paper follows the journey of an advertisement through Facebook’s platform. In this way, Facebook’s regulatory missteps can serve as a case study to illustrate how Big Data analytics can, when informed by human and machine bias, cross the line into discriminatory practices. This case study underscores how it is vital — in advertising as in other industries — not to treat advanced analytics like artificial intelligence as black boxes. Indeed, to inform the design and use of advanced analytics, it is essential for companies to consistently develop a comprehensive understanding of their data, in addition to the legal and ethical implications of their operations.
    Keywords: housing discrimination, Facebook, Big Data, AI, targeted advertising

  • Building analytics teams for success
    Radhika V. Kulkarni, Former Vice President of Advanced Analytics R&D, SAS Institute

    Big Data, analytics, Artificial Intelligence, data science: all of these areas have given rise to the need for every industry to invest in building up analytics teams that can address complex business challenges. This paper looks at some aspects that are critical for the composition of successful teams in terms of technical background, skills and experience. It also discusses the challenges companies face in hiring the right individuals for these varied roles and the role educational institutions play in filling this talent gap.
    Keywords: analytics excellence, successful analytical groups, analytical skills gap, democratisation of analytics, marketing optimisation, data science, Artificial Intelligence

  • Four pivotal capabilities for marketers to support the growth mandate
    Laura Patterson, President, VisionEdge Marketing

    Whether times are uncertain or our sails are full of wind, there is always the pressure to grow. Every company must take a customer-centric approach to stay on track or to ramp up. This paper explores four pivotal capabilities that every company needs their marketing organisation to master in support of the growth mandate.
    Keywords: customer-centric, organisation, market growth, data-driven, analytics, marketing, strategy

  • Adapting the enterprise data lake architecture for marketing analytics
    Roger Kamena, Vice-President of Data Science and Technology, Adviso Conseil

    Data lakes have evolved over the last decade from highly complex IT infrastructures on-premise to simpler serverless cloud environments. This change makes data lakes much less difficult to maintain and operate, which provides an opportunity to democratise them beyond operational business intelligence teams to a wider audience, namely marketing analytics practitioners. At the same time, the availability of marketing data has also evolved over the last decade. Marketing technology platforms have proliferated the variety, velocity and volume of data for marketers to process. This paper considers how a data lake architecture could be adapted for marketers to help them go further with their data.
    Keywords: data lake, data management systems, Big Data analytics, cloud analytics, marketing data, data architecture

  • Interrelated factors driving the purchase of over-the-top television subscription services: A study using exploratory factor analysis and the decision-making trial and evaluation laboratory method
    Chand P. Saini, Assistant Professor, SGT University and Neha Gupta, Assistant Professor, Amity School of Business

    This study identifies the key factors motivating people to subscribe to over-the-top (OTT) services and examines the causal relationships between those factors. As it is a multiple criteria decision-making problem, the study embraces an integrated model of exploratory factor analysis and the decision-making trial and evaluation laboratory (DEMATEL) method. The latter technique is used to simplify the decision-making process and visualise the interrelationships between the motivation factors. The study identifies four key motivational factors: flexibility, content, variety and social status, of which content is the most crucial. The findings of this study will be of interest to marketing analysts and decision-makers seeking to measure, analyse and improve marketing performance in order to develop better strategies for marketing OTT services.
    Keywords: decision-making, OTT, live-streaming, flexibility, content, factor analysis, DEMATEL

  • Book review: The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster and More Profitable
    Reviewed by Denis Malin, Editorial Board Member
  • Book review: Fast Track Your Business: A Customer-Centric Approach to Accelerate Market Growth
    Reviewed by Seth Earley, Editorial Board Member