Volume 7 (2021-22)

Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. 

The Articles published in Volume 7 include:

Volume 7 Number 4

  • Editorial: What problem are you trying to solve?
    Denis Malin, Independent Consultant
  • Practice papers
    Revenue operations: A systems approach for turning analytics into growth
    Stephen Diorio, Managing Director, Chris Hummel, Managing Director and Bruce Rogers, Managing Director, Revenue Enablement Institute

    Selling has become more data-driven, capital intensive and aligned around customer lifetime value. As part of their research for a book (Revenue Operations: A New Way to Align Sales & Marketing, Monetize Data, and Ignite Growth, Wiley 2022), the authors have conducted interviews with 120 senior growth leaders to uncover the systems and structures they are using to generate higher returns from their commercial data assets, which are among the largest financial assets in the business. This paper will draw on original commercial research as well as research from the leading academics in the field of marketing analytics to answer these critical management questions: how can organisations turn customer engagement data into actionable customer insights?; how can organisations use those insights to support decisions, actions and resource allocation?; how can organisations monetise data assets, enable their revenue teams and support the entire revenue cycle?; what are the most financially viable ways organisations can monetise their customer engagement data assets?
    Keywords: marketing, analytics, sales, data, growth, revenue, intelligence, customer

  • How much does digital advertising accelerate new product success?
    Giacomo Bertozzi, Senior Analytics and Media Manager, Amazon Advertising, et al.

    Many new products are launched in e-commerce. While advertising is believed to enhance new product success, managers often lack the numbers to quantify this benefit to the company. Retail websites offer specific success benchmarks, such as pre-purchase product views, purchase conversion and post-purchase reviews. This paper’s main thesis is that while new products can succeed with or without advertising, digital advertising can help products achieve success faster. Across five categories, this research shows that digital advertising on Amazon.com can cut the time needed to reach success levels by more than half, compared to products that reach these same benchmarks without such advertising.
    Keywords: advertising, digital, new products, take-off, page views, purchase conversion, online reviews

  • Recommendations and personalisation: Three strategies for activating customer behaviour analytics insights
    Seth Earley, CEO, Earley Information Science

    This paper describes three approaches to personalisation and explains the methods for implementing each one. The reasons why many organisations have difficulty achieving effective personalisation are discussed, including lack of maturity in the required processes, architecture and data quality. Differences and similarities between personalisation and recommendations are reviewed, as well as the methods described for segmenting customers to present them with products and information tailored to their interests and needs. The paper identifies four dimensions that can be used for personalisation; preferences, location, topics and products/solutions. Methods required to carry out personalisation at scale are explained, and suggestions made regarding the development of the attributes needed for personalisation. The author highlights the importance of correctly modelling the customer journey in order to present the right information or products in response to signals from the customer.
    Keywords: personalisation, customer journey, customer data, data models, customer behavioural analytics, product recommendations, artificial intelligence, customer attributes

  • From captive to captivating: The new customer journey model for companies
    Sophia Tannir, Data Scientist, C Space, et al.

    From purely transactional to personalised and unique, customer experiences can extend far beyond the exchange of goods. Companies often focus on functional and transactional experiences because they are the easiest to understand. A large part of purchase behaviour is, however, subjective and occurs in the context of the emotional relationship between companies and their customers. Together, Wharton Customer Analytics (WCA) and C Space explored and quantified the strength of this customer–company relationship while identifying the commercial benefits of customer-centricity (Customer Experience Code). Using the simple clustering method, k-means, WCA led the process of identifying discrete levels of customer-centricity and C Space augmented the analysis with new data on 160 companies. Together, WCA and C Space observed a significant relationship between the measure of a company’s customer-centricity and its performance. In addition, this study proposed a novel company journey framework, in which companies evolve from having a highly transactional customer relationship to one that is emotionally relevant and, crucially, provides the levers for improving customer-centricity. The framework also links this journey to the more typical customer satisfaction metric, net promoter score (NPS), and other measures of word-of-mouth (eg earned advocacy score [EAS]). A recent illustration of this journey is the surprising transformation of the logistics industry, which saw a large gain in emotional connections between 2019 and 2020. UPS, specifically, positively traversed all emotional categories and grew from the bottom to the top in less than a year. The data reveals that UPS’s response to the COVID-19 pandemic illustrated to their customers they are much more than a parcel delivery service.
    Keywords: customer experience, customer-centricity, NPS, CX measurement, customer journey, maturity model, CRM

  • Customer-centric framework: The missing link between data and business value
    Jenna Martinez, Professional Services Consultant, Adobe

    As enterprises continue to evolve their data-driven strategies, many decision makers are required to draw conclusions based off data and advanced statistical analyses. Despite organisations’ increased access to valuable data and use of sophisticated data models for analysis, many decision makers feel they are not informed enough to make critical business decisions. The complexity and intricacy of consumer data and analytics represents a major challenge in implementing customer-centric data strategies across an organisation. By shifting towards analytical-based actions and a customer-centric framework, an organisation can deliver more responsive and personalised customer experiences that drive greater business value.
    Keywords: actionable insights, data-driven, data, management, analytics, innovation, data-driven decision making

  • Customer insights that matter
    Debbie MacInnis, Professor, University of Southern California

    Marketers realise significant benefits such as greater sales, reduced marketing costs, higher profits, more lucrative salaries and enhanced influence within their organisation when they understand customers. To reap these benefits, marketers need customer insights: how customers think, how they feel, how they behave and how they are changing in response to evolving marketplaces. Marketing’s role in research is imperative, since marketing represents the voice of the consumer within the organisation. The best research-derived insights are realised when marketers have thought through the following questions before data is collected: 1) is there a clear and specific research question?; 2) which customers are most relevant to the research question?; 3) has this question been answered before (in the academic literature, industry/consulting reports, within my own company)?; if not, 4) which research methods and measures should be used; 5) where should one start in the research process?; and 6) what is optimal? This paper addresses each question.
    Keywords: customer insights, customer research, research questions, data sources, multi-method research

  • Why domain knowledge is essential for data scientists in marketing
    Andrea Ahlemeyer-Stubbe, Director Strategic Analytics and Agnes Müller, Senior Analytical Consultant, servicepro GmbH

    What good is the most scientifically valuable analysis if it piles up in marketers’ inboxes but does not give them the necessary foundation for their decisions? Such a situation is no use to data scientists and certainly no use to the marketing team. The root of the issue is that two worlds meet here that speak completely different languages. Only if data scientists can ‘translate’ their results into marketing language will their work be successful. Marketing teams do not need as much information as possible; rather, they require just the right information, preferably with recommendations for action that can guide their decisions. To select the information that is truly useful for marketing and communicate it in an understandable way, data scientists must have more than expertise in analytics methods and tools (which is assumed and therefore not discussed in detail here); they also need to know about marketing objectives and have a comprehensive contextual understanding of their company’s industry and sector, including competitors. Knowledge of the general situation in the world as well as the legal, political and religious particularities of the countries in which the company operates is also required. In short, analytics results that truly drive marketing can only be delivered by data scientists with domain knowledge. Using a case study from the field, this paper shows how data scientists can gain the domain knowledge they need to be successful in marketing and in which aspects of their work it helps them perform more effectively.
    Keywords: domain knowledge in data science, marketing analytics, success factors, data scientist, predictive modelling, statistics, computer science

  • Research papers
    Assessing the statistical significance of repeated A/B tests with meta-analysis
    David M. Harman, Assistant Professor, University of St. Thomas

    This paper presents the steps to conduct a meta-analysis of a set of repeated A/B tests. Repeated A/B testing is common in professional marketing practice. Each test, however, can only be individually evaluated for statistical significance when using basic statistical analysis. A meta-analysis of the same A/B tests provides three useful analytics outputs: an overall treatment effect across all tests, a confidence interval for that effect to assess statistical significance, and a measure of heterogeneity between tests to assess the context variation between campaigns. Meta-analysis is a useful addition to marketing analytics practice.
    Keywords: meta-analysis, A/B testing, significance testing, marketing campaign analytics

  • Analysing perceptions towards electric cars using text mining and sentiment analysis: A case study of the newly introduced TOGG in Turkey
    Dilek Penpece Demirer, Associate Professor and Ahmet Büyükeke, Research Assistant, Adana Alparslan Türkes Science and Technology University

    The electric car market is growing steadily around the world and, accordingly, has become an attractive research area. It is important to understand the consumer perspective on newly introduced electric cars, such as those of Turkey’s Automobile Joint Venture Group Inc. (TOGG). Thus, the purpose of this study is to provide a better understanding of consumers’ perceptions related to the newly introduced TOGG, which may create a competitive advantage. Social media is an abundant source of textual data that allows for very reliable analysis and understanding of consumer opinions. In this study, Twitter comments on TOGG were collected and studied. Text mining, sentiment analysis and topic model analysis were then conducted. The results show that TOGG is a popular product with the public: there are many more positive Twitter comments related to TOGG than negative ones. The topics identified in social media are price expectancy, production facility, design and features. The most frequent topic for both positive and negative comments is price expectancy.
    Keywords: sustainable competitive advantage, electric car, text mining, sentiment analysis, topic model analysis

Volume 7 Number 3

  • Editorial: Drawing from data to paint bigger pictures
    James Wycherley, Chief Executive, Insight Management Academy
  • Practice papers
    Effective first-party data collection in a privacy-first world
    Lucas Long, Product Manager and Privacy Specialist for Tag Inspector, InfoTrust

    The analytics industry is facing an unprecedented change in the methods and requirements for data collection. These changes are a result of increasing consumer expectations regarding the privacy of personal information and shifts in the regulatory and technological methods used to meet these expectations. In today’s privacy-first world, first-party data collection becomes more important than ever — while at the same time more difficult. This paper outlines core principles for first-party data collection in a privacy-focused world and offers tactical suggestions for future-proofing. These suggestions include methods to collect privacy-safe first-party and anonymous data, strategies to enable downstream integration, and ways to enforce data taxonomies, as well as compliance via a server-side data distribution approach.
    Keywords: data architecture, data minimisation, privacy, data collection models, cookieless data, data governance

  • Thriving in the age of privacy regulation: A first-party data strategy
    Lawrence Latvala, Americas Financial Services Industry Practice Leader and Jeff Horn, Americas Financial Services Industry Consultant, Teradata and Bill Bruno, Chief Executive Officer, D4t4 Solutions Plc

    This paper reviews the recent legislative and policy changes in the area of consumer data privacy and their impact on the effectiveness of the most popular tool for tracking customers — the third-party cookie. With third-party tracking rendered mostly ineffective, the authors present the case for marketers to implement a stronger first-party data strategy to fill the gaps left by these changes. First-party data strategies provide significant advantages over third-party techniques, including broader controls for customer data capture and the ability to generate a deeper understanding of customer behaviour and intent; they are also easier to integrate into the marketing analytic and operational process. Additionally, the authors outline the advantages of a first-party data strategy for implementing real-time identification and tracking of customers — something that third-party strategies cannot deliver. All of this can be achieved while staying in compliance with changing regulations that control the timing and use of customer data. Increasingly, the future of customer digital marketing is private, respectful, in real time and first-party.
    Keywords: data, regulation, privacy, digital, marketing, strategy, customer management

  • High-impact testing that inspires action
    Valerie Kroll, Optimisation Director and Julie Shallman Hoyer, Senior Analytics Associate, Search Discovery

    This paper outlines a simple framework for data storytelling that communicates experimentation programme test results focused on stakeholder actionability rather than test performance. This framework can be applied both as a narrative and a visualisation guide within a company’s existing analytics tool. Its benefits for a business mirror the true returns from investing in an experimentation programme — not the percentage lift of a single test treatment, but the application of test results to how the business might de-risk decision-making, optimise marketing efforts, or prioritise its product roadmap. This approach supports experimentation maturity by (1) giving stakeholders access to test learnings, (2) sharing how stakeholders can use learnings, (3) building institutional intuition of how to talk to different customer audiences or develop new products/features, (4) enabling insights to guide next right actions, and (5) helping others understand the potential application of experimentation and the benefits of testing.
    Keywords: A/B testing, analysis, experimentation, impact, data storytelling, digital marketing

  • Relevant, impactful and trusted data analysis: A framework for driving the efficient adoption of results
    Mike Johnson, Senior Data Scientist, St. Charles Health System and James Cousins, Analyst Manager, Rapid Insight

    Data analysis and soundly implemented analytics are critical aspects of marketing. Sound implementation, however, is not a guaranteed follow-on from data analysis projects. Strategically planning ahead for implementation is essential. In other words, the planning, methods and communication of results must integrate seamlessly into business requirements and objectives. Typically, the burden falls on executives and leadership to seek insights from analysts, but analysts can further their impact by proactively optimising their analysis for the implementation phase. This paper details a framework for guiding strategies that drive relevance, trust and adoption during all stages of analysis and the decision-making process.
    Keywords: marketing analytics, business intelligence, communication in business, strategic analytics, project design, stakeholder engagement, predictive analytics

  • Research papers
    Benefits, challenges and future developments in digital analytics in German-speaking countries: An empirical analysis
    Darius Zumstein, Lecturer and Senior Researcher, ZHAW School of Management and Law, Claudia Brauer, Professor, Management Center Innsbruck and Andrea Zelic, Digital Media Consultant, Attackera GmbH

    This paper presents the results of a survey, conducted in 2020, to gauge the current standing of digital analytics in Germany, Switzerland and Austria. The findings highlight the increasing maturity level, benefits, challenges and future developments in the field. The research confirms that digital analytics supports the analysis and optimisation of digital marketing campaigns, user experience, search engine marketing and data-driven decisions. For many of the companies analysed, the most important challenges were reported to be data quality, lack of skills and data culture; however, maturity level, capabilities, agility and professionalism in digital analytics are steadily increasing, and artificial intelligence is enjoying many new and different applications within the fields of sales and marketing. These findings suggest that companies in German-speaking countries should focus on improving data quality and data culture.
    Keywords: digital analytics, web analytics, marketing analytics, business analytics, Big Data, maturity level, artificial intelligence

  • The effect of brand image, price, service, product quality and promotion on consumer buying decisions for car purchases: A case study of Bosowa Berlian Motor Inc. in Makassar
    Muhammad Yusuf, Lecturer and Miah Said, Lecturer, Bosowa University, Nurhilalia, Lecturer, State Polytechnic of Ujung Pandang and Yulia Yunita Yusuf, Lecturer, Fadjar University

    This paper aims to analyse how brand image, price, service quality, product quality and promotion of cars at Bosowa Berlian Motor Inc. (part of the Mitsubishi brand that sells cars in South Sulawesi province) affect consumer purchase decisions. This study used path analysis to analyse the pattern of relationships between the independent variables (exogenous) and the dependent variable (endogenous) to determine their direct or indirect effects. The results showed that the brand image, price, service quality, product quality and promotional activities had a significant positive influence by encouraging an increase in consumer interest.
    Keywords: brand image, car, price, product quality, promotion, service quality

  • Understanding the consumer: A comparison of buying behaviour among consumers of Hindustan Unilever and Patanjali products
    Anshul Dubey, MBA Student, Symbiosis Institute of Business Management

    With consumer tastes and preferences evolving at an ever-increasing speed, the fast-moving consumer goods industry is facing an unprecedented level of uncertainty. This paper explores how upcoming brands associated with organic and natural products, such as Patanjali, are competing with established giants like Hindustan Unilever across various product lines. The research uses a questionnaire to obtain a sample of consumer perceptions, and analyses the results to identify how consumer demographics relate to brand perception and, in turn, buying behaviour.
    Keywords: Patanjali, Hindustan Unilever, FMCG, Ayurvedic products, organic products

  • A machine-learning approach for classifying Indian internet shoppers
    Ritanjali Majhi, Associate Professor, National Institute of Technology Karnataka and Renu Prasad Sugasi, Data Analytics and Business Consultant, Thorogood Associates

    This paper identifies the key factors that influence Indian consumers to shop online. The study uses data collected via questionnaire survey to segment consumers with shared behaviours into groups, with the results of this clustering used to train radial basis function neural networks, decision trees and random forest models. The performance of these classification models is then assessed and compared with the conventional statistical-based naïve Bayes method and logistic regression. The study finds that the random forest method provides the greatest accuracy for the segmentation of online consumers, followed by naïve Bayes and decision trees methods. The behavioural patterns identified in this study may be leveraged in real-world situations.
    Keywords: classification, consumer behaviour, online shoppers, random forest, decision tree, RBFNN, logistic regression, naive Bayes model

Volume 7 Number 2

  • Editorial: Approaches for a thriving marketing analytics function: Now and in the future
    Dan Mooney, Founder, Strategic Digital Insights LLC
  • Opinion piece
    The social responsibility of data visualisation in a time of pandemic
    Dona Wong, Senior Vice President of Digital Strategy and Communications, Federal Reserve Bank of New York

    In a time of pandemic, media platforms and outlets have a social responsibility to make complex data more accessible, understandable and usable. To this end, health organisations and government officials have made extensive use of data visualisations to manage the global COVID-19 crisis. Motivating people to make the necessary behavioural changes, however, requires data to be presented in an appropriately engaging manner. This paper describes effective ways to present data and how to turn complex raw data into actionable insights.
    Keywords: data visualisation, storytelling with data, actionable insights, Big Data, interactive graphics, information graphics, strategic communications

  • Practice papers
    The care and feeding of digital analysts
    Jim Sterne, Online Marketing Analytics & Business Scaling Consultant

    Achieving excellence in digital analytics requires a dedicated balance of people, process and technology. This paper focuses on the people side of this equation, providing tips, opinions and observations, rather than rigorous research or structured analysis. The paper offers advice for analytics team leaders and those who would like to become one, recognising that analytics is a unique occupation in that it offers guidance rather than an end product. The paper discusses special management considerations, including the ongoing need to explain and convince the rest of the organisation of the value of analytics; hiring the smartest people and getting out of their way; supervising a diverse mix of personality types that are not necessarily well suited to team efforts; keeping the best and brightest engaged; recognising leaders and developing their skills; helping people get the recognition they deserve; maintaining core, organisational values; and staying human in an industrialised environment.
    Keywords: analytics, management, advice, unique, value, recruit, retention

  • The four big forces conspiring to ruin one’s analytics
    Jeff Greenhouse, Vice President of Subscriber Growth, AMC Networks

    Despite major advances in tracking, attribution and data augmentation over the past decade, or perhaps because of them, several forces now threaten to degrade the ability of marketers to measure their campaigns and understand their audience. A growing privacy movement is resulting in government regulation and technical restrictions from ‘big tech’. Walled gardens are growing larger and more numerous, forcing marketers to rely on limited data without third-party verification. Consumers are becoming increasingly fragmented across a sea of connected devices, and the minimum viable product development philosophy is creating a legacy of ‘data debt’ for many companies. This paper explores these four threats and discusses strategies that marketing and analytics teams can use to attempt to counter them.
    Keywords: privacy, attribution, tracking, compliance, retargeting, data quality, analytics strategy

  • Moving towards inferential attribution modelling in a world without third-party cookies
    Roger Kamena, VP, Analytics and Data Science, Adviso Conseil Inc.

    With the gradual disappearance of third-party cookies and Identifier for Advertisers (IDFA) tracking, marketers are becoming more restricted in their capacity to measure the performance of their marketing initiatives. Standard attribution models are currently based on user-level data to establish a one-to-one relationship between customer interactions and conversion goals. However, with user-level data about to become more difficult to access, marketers will need to embrace alternative ways to measure the effectiveness of their marketing efforts. This paper proposes inferential attribution modelling techniques as a potential alternative or complementary approach to user-level attribution techniques, and revisits older marketing techniques, such as media mix models, to address the upcoming changes in the marketing data ecosystem.
    Keywords: user data, marketing data, digital advertising, user privacy, attribution, data collection

  • Assessing privacy protected cohort-based market segmentation
    Martin P. Block, Professor, Integrated Marketing Communications, Medill School of Journalism, Northwestern University

    Marketers need an efficient way to be able to identify their customers and prospects without contravening any of the various laws or regulations relating to data privacy. There are two solutions to address this issue: reliance on consumer characteristics that cannot be used to identify a particular individual, and the segmentation of individuals into groups or cohorts. With respect to the former, the challenge is to select the appropriate consumer characteristics from the many variables, such as demographic and psychological variables, leisure interests, and behavioural variables such as purchase history or online activities. On the other hand, the size of the cohort or group is also an issue, to ensure that individual consumers cannot be identified. Using fashion brands as an example, this paper demonstrates the efficacy of segmentation variables in the context of leisure activities and online activities. The study finds that although cohort-based segmentation appears to be a reasonable solution to the privacy problem, the utility of the cohort appears to be informed by its size. The findings also point to significant issues regarding the cluster methodology necessary to create the cohorts, the sizes of the cohorts, and perhaps most importantly, the heterogeneity of the cohorts.
    Keywords: privacy, cohorts, activities, segmentation

  • Optimising marketing strategies by customer segments and lifetime values, with A/B testing
    Paromita Guha, Co-founder and Data Scientist, Axiomatic Data, Christina Echagarruga, Data Scientist, Facebook and Eva Qi Tian, Data Scientist, Vanguard Group

    Every customer has different needs and purchasing behaviour. This paper shows how data science tools such as machine learning, artificial intelligence and A/B testing enable marketers to segment their target market, identify the most loyal high-value customers and their purchasing patterns, and calculate the lifetime value of these customer segments to optimise marketing strategies and campaigns. The paper also argues that A/B testing helps marketers make unbiased data-driven decisions, making it the gold standard for identifying the best marketing strategy.
    Keywords: predictive analytics, customer, segmentation, lifetime value (LTV), experimentation, A/B testing

  • Measuring the value of artificial intelligence in improving search and chatbot outcomes
    Jeff Larche, Director, Analytics and Personalization and Josip Lazarevski, Senior Product and Data Science Architect, TA Digital

    This paper recommends that the best way to measure the success of artificial intelligence used in internal search or chatbots is not to use the data driving these improvements, but rather to tune a standard user behaviour measurement system to the metrics that matter and use carefully constructed experiments to assess the return on investment. The paper also maintains that digital marketers can learn a great deal about their customers and products from these enhancements, including what products need the most online marketing help. Through this process, the marketer can learn as much about the system as the system is learning from its users.
    Keywords: artificial intelligence, machine learning, return on investment, technological domestication, Adobe Analytics, Google Analytics, data visualisation, brownie charts

  • Whither in-store analytics: How in-store behavioural analytics has changed and where it is heading
    Gary Angel, Chief Executive Officer, Digital Mortar

    As organisations evolve, they want a more comprehensive view of customer behaviour. While digital channels are easily measured and have been studied extensively, for retailers with a physical presence, the store is somewhat of a black box: retailers know how many customers they have going in and what sales are coming out the other side, but what happens in between has long been a mystery. As this paper shows, however, improvements in both technology and analytics are shining fresh light on in-store activity. This paper describes recent advances in technology for monitoring in-store journeys and how such advances have enabled people-movement data to be used in more sophisticated ways, despite privacy limitations.
    Keywords: shopper analytics, store analytics, full-journey measurement, shopper measurement, store optimisation, customer experience

  • Emanating confluence: The symbiotic relationship between artificial intelligence and data
    Ted W. Gross, Founder, Asanatae

    This paper seeks to explain significant constructs within artificial intelligence (AI), including but not limited to: the impact of ‘information theory’; entropy, especially in terms of ‘information entropy’; and language theory (linguistics), dealing with all communication methods and the ‘meaning’ in that communication. The paper discusses the progression from ‘chaos theory’ to ‘complexity theory’ to ‘emergence’ and finally the ‘technological singularity’. It examines such questions as: How do chaos and complexity lead to emergent systems which will inevitably lead to a technological singularity? How does the endless loop of AI progress as it emanates outward, comes to confluence, and emanates yet again? What does each phase entail? How does our advance towards exponential growth in data affect the progression of AI? What are the dangers of ‘bias’? What are the risks as we move towards emergence? Finally, why must we exercise extreme caution as we come closer to the technological singularity?
    Keywords: artificial intelligence, entropy, chaos, complexity, emergence, singularity, superintelligence

Volume 7 Number 1

  • Editorial: Shedding the pounds by quitting the cookies 
    Brendan J. Keegan, Senior Lecturer in Digital Marketing, Manchester Metropolitan University
  • Practice papers
    Planning for a cookie-less future: How browser and mobile privacy changes will impact marketing, targeting and analytics
    Ian Thomas, Independent Consultant

    Recent and impending changes to the way that browsers and mobile platforms handle third-party cookies and ad IDs will have a profound impact on the digital advertising ecosystem. This paper examines these changes in the context of the development of the ad-tech and digital media industry, and concludes that while these developments may benefit users by protecting them from intrusive third-party tracking and targeting, they risk further consolidating power with the three dominant companies in the sector, namely Google, Facebook and Amazon, and advertisers and marketers will have to work hard to ensure they do not become over-dependent on these suppliers. At the same time, the changes offer an opportunity to move back to a better equilibrium between advertising and the content that it appears alongside, driving value for both advertisers and consumers.
    Keywords: privacy, cookies, ad-tech, Google, Facebook, Apple

  • Ethics and data governance in marketing analytics and artificial intelligence 
    Haniyeh Mahmoudian, Global AI Ethicist, DataRobot

    Recently, marketers have seized the opportunity to leverage the power of Big Data analytics, machine learning and artificial intelligence in their work. However, greater use of data is accompanied by increasing concerns and ethical challenges regarding aspects of data collection, data security and privacy. Implementing a data governance framework and standardising the data life cycle can help analytics-based marketing departments work more effectively, and to proactively address the concerns inherent in their operations. This paper discusses some of the current challenges and how data governance provides principles that organisations can use in their quest for a more robust approach to analytics-based marketing.
    Keywords: Big Data, analytics, data governance, artificial intelligence, machine learning, data ethics

  • Analytics redefined: How privacy is reshaping the industry 
    Cory Underwood, Analytics Engineer, Search Discovery

    This paper explores the recent impact of privacy on the analytics industry. It examines this impact from multiple perspectives, including analytics testing (eg effects on retention reports and cohort analysis, audience segmentation intelligence and device identification reports) and marketing (eg effects on campaign performance, remarketing and mobile apps). These impacts inform a basis for projecting what is to come in the future, what organisations need to know, and what they may need to do, including planning responses to developments in legal regulations, intelligent tracking prevention, network blocking and design; reconsidering teams and workflows; and prioritising training and education.
    Keywords: privacy, legal, development, analytics, measurement

  • Ethical and efficient consent management Capacity planning in marketing
    Doug Hall, Senior Director of Analytics, MightyHive

    The need for consent management on websites does not need to be a barrier to success. This paper discusses how ‘cookie banners’ offer an opportunity to start a conversation, and build a mutually valuable relationship with users. Collecting analytics data requires a responsible attitude with a human touch. By understanding technology, regulation and users’ needs, site owners can use consent management as a competitive advantage rather than a hindrance.
    Keywords: privacy, digital, marketing, data, analytics

  • The impact of evolving digital behaviours on the diffusion of marketing technology 
    Andy Betts, Marketing Consultant and Adviser

    As the transformation to digital has accelerated due to the global pandemic, companies are finding both challenges and opportunities in understanding and adapting to dynamically changing consumer behaviour. This paper discusses how organisations must adopt the right digital technology to better understand their customers and measure success.
    Keywords: digital behaviour, marketing technology, customer experience, digital interaction, COVID-19, analytics, measurement

  • How analytics is used in forecasting 
    Barry Keating, Professor Emeritus of Finance, University of Notre Dame

    Over the last decade, the science of forecasting has adopted the tools of the data scientist. Prediction today combines traditional demand planning models with the standard tools of machine learning. The result is much improved accuracy over the short term and an enhanced ability to account for the effects of major changes in the economic environment. On the flipside, researchers must now sort through much greater volumes of data in order to identify what might be useful to produce accurate forecasts. The application of machine learning solves what could be a major stumbling block here. So-called ‘data consolidators’ are now emerging to support forecasters by providing access to previously unknown data as well as the tools for using such data creatively. This paper will demonstrate how data from data consolidators may be used by analytics algorithms to improve the accuracy of forecasts.
    Keywords: analytics, forecasting, classification, prediction, supply chain, demand planning

  • Data and decisioning: It takes two to tango in customer experience
    Lisa Loftis, Principal, SAS Customer Intelligence

    The COVID-19 pandemic has induced a massive increase in digital activity, and consumers are demanding a new engagement model with both digital and physical aspects. To provide the best customer experience, this paper posits that brands will have to reach their customers with personalised interactions in real time. As this paper will discuss, this will require them to adapt their technology models to become more agile and more reliant on automation (decisioning) and analytics.
    Keywords: customer data platform, intelligent decisioning, artificial intelligence, marketing analytics, real-time analytics, Experience 2030

  • From marketing to neuromarketing: Ethical considerations
    Caterina Garofalo, President and Francesco Gallucci, Vice President, Italian Association of Neuromarketing

    This paper discusses the emerging field of neuroethics — broadly speaking, the ethical and social issues raised by advances in neuroscience — in the context of neuromarketing. The paper explains how the field is strongly influenced by current events and cultural consciousness. The authors build on the opinions proposed by some scholars, highlighting key considerations, starting with respect for privacy, transparency and, most urgently, the need to place the consumer at the centre of any business strategy and any project development.
    Keywords: neuromarketing, neuroethics, marketing, moral philosophy

  • Research papers
    Screening for self-directedness: A method for recruiting savvy analysts in a dynamic business environment 
    Jennifer L. Dapko, Assistant Professor of Marketing and Gregory J. Snyder, Adjunct Professor, Florida Southern College

    Analytics is changing at the speed of thought. If analysts are not capable of and motivated by self-directed learning, they will be left behind and their organisations will be left scrambling to keep up with their competitors. Even the best company-directed learning programmes cannot always keep pace with this change, and most analysts will find company-directed technical training falling short of their needs. Self-directed learning enables analysts to stay relevant and motivated in a quickly changing business world, and self-directedness is a key employee characteristic in creating adaptable and flexible organisations. This paper discusses ways in which hiring managers can screen analysts for self-directedness during the interview process. As important as it is for hiring managers to recruit a workforce with self-directedness, it is equally important to set up an environment where a self-directed learner can thrive and utilise those skills. Therefore, this paper will also discuss ways in which organisations can nurture self-directed learners once recruited.
    Keywords: self-directed learning, self-directedness, business analytics, analytics training, hiring analytics employees, employee motivation, employee learning

  • The importance of local culture in the marketing mix during low season in Bali
    Nyoman Gde Dewa Rucika, PhD candidate, I. Wayan Ardika, Lecturer, A.A.P. Agung Suryawan Wiranatha, Head of the Centre of Excellence in Tourism and Made Budiarsa, Head of Tourism Doctoral Study Programme, Udayana University

    Tourism in Bali has suffered greatly from the impacts of COVID-19. To address this problem, this paper proposes strategies to reinvigorate the sector. Specifically, it aims to determine the appropriate marketing mix for tourism during the low season. Research for the study was conducted with expert focus groups from April 2020 to October 2020. Following analytical hierarchy process analysis, the study finds that the promotion of local culture should play a greater part in the marketing mix. The authors therefore recommend that the regional government reschedule cultural events, such as the Bali Arts Festival, to the low season, as this should attract tourists interested in Bali’s unique local culture. The suggestion that local culture should play a greater role in the marketing mix may be extended to tourism destinations worldwide, as leveraging unique selling points is more cost-effective than strategies based primarily on discounts.
    Keywords: local culture, marketing mix, Bali, tourism, low season, analytical hierarchy process