Creating Value with Data Analytics in Marketing

Mastering Data Science

Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa

• Publisher: Routledge
• Year: 2021
• ISBN:
  9781000465518
• Edition: 2
• Topic: Business
• Subtopic: Information Management
• Index: Business

This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organizations to leverage the information to create value in marketing.

Creating Value with Data Analytics in Marketing provides a nuanced view of big data developments and data science, arguing that big data is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors' extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. The second edition of this bestselling text has been fully updated in line with developments in the field and includes a selection of new, international cases and examples, exercises, techniques and methodologies.

Tying data and analytics to specific goals and processes for implementation makes this essential reading for advanced undergraduate and postgraduate students and specialists of data analytics, marketing research, marketing management and customer relationship management.

Online resources include chapter-by-chapter lecture slides and data sets and corresponding R code for selected chapters.

CHAPTER 1 Data science and big data

DOI: 10.4324/9781003011163-1

1.1 Introduction

One of the most important developments in the last decade is the increasing prevalence of data. This is frequently referred to as big data. One of the main underlying drivers of this explosion is the increasing digitalization of our society, business, and marketing. One can hardly imagine that consumers around the globe nowadays could live without smartphones, tablets, Facebook, Instagram, and Twitter. Marketing is probably one of the business disciplines most affected by new developments in technology. In recent decades, technological developments such as increasing data-storage capacity, increasing analytical capacity, the growth of online etc., have dramatically changed specific aspects of marketing. More specifically, we have seen the development of Customer Relationship Management (Kumar & Reinartz, 2005). This arrival of CRM posed challenges for marketing and raised issues on how to analyze and use all the available customer data to create loyal and valuable customers (Verhoef & Lemon, 2013). With the omnipresence of even more data and other types of data, such as text and unstructured data, firms consider this an even more important problem (Leeflang et al., 2014). The explosion of data has led to the current strong focus on data science and analytics in today’s business. Machine learning, algorithms and artificial intelligence have become important buzzwords. Investigations by the European Parliament show that the market value of (big) data analytics is around 116 billion Euros in the US and 54 million in the EU (see Figure 1.1) highlighting the great importance of big data for today’s economy. In addition around six million people are employed in this industry in the EU.

FIGURE 1.1 Big data employment and market value in the EU and other major economies

Source: https://epthinktank.eu/2016/09/29/economic-impact-of-big-data/big_data_employment

1.2 Explosion of data

Data have been around for decades. However, 30 to 40 years ago, these data were usually available on an aggregate level yearly or monthly. With developments such as scanning technologies, weekly data became the norm. In the 1990s, firms started to invest in large customer databases which produced records for millions of customers in which information on purchase behavior, marketing contacts, and other customer characteristics was stored (Rigby, Reichheld & Schefter, 2002). The arrivals of the Internet and, more recently, social media have led to a further explosion of data, and daily or even real-time data have become available for multiple firms. It is clear that getting value from these data is very important.

The Internet has become one of the most important marketplaces for transactions of goods and services. For example, online consumer spending across the globe has now reached around 3.53 Trillion USD (Statista, 2020).¹ Besides B2C and B2B-markets, online C2C markets have grown in importance, such as LuLu, eBay and YouTube. Amazon is now a dominant online platform and retailer, as is Alibaba in China, with very strong growth in market capitalization. Twitter users send half a million tweets every minute.² Companies are also increasingly investing in social media. In the USA firms have spent around 34 Billion USD on social media advertising, and this will continue to grow in the coming years (Hootsuite, 2020).³ Managers invest in social media to create brand fans, which tends to have positive effects on word-of-mouth recommendation and brand loyalty (De Vries, Gensler & Leeflang, 2012; Uptal & Durham, 2010). There are 3.5 billion searches on Google every day, growing by around 10% every year. The use of social media also creates a tremendous increase in customer insights, including how consumers interact with each other and the products and services they consume. Specifically, blogs, product reviews, discussion groups, product ratings, etc. are new and important sources of information (Mayzlin & Yoganarasimhan, 2012; Onishi & Manchanda, 2012). The increasing use of online media, including mobile technology, also allows firms to follow customers in their customer journeys (Lemon & Verhoef, 2016).

1.3 Data science becomes the norm

In 2020 it is estimated that there are around 40 trillion gigabytes of data. Big data has become the norm and firms understand that they might be able to compete more effectively in a digital environment by analyzing these data (e.g., Davenport & Harris, 2007; Verhoef et al., 2021). There are several popular examples of firms analyzing these data, such as IBM, Tesco, Capital One, Amazon, Google, Netflix, Zalando, etc. Data science is often used as an overall term that focuses on activities within a firm to extract value from data using analytics. In future years this can only become more important, given strong developments in artificial intelligence (e.g. Huang & Rust, 2018), in which data, analytics, and algorithms are very important.

However, there are also problems with data science. Many firms still struggle to implement an effective data science strategy. Moreover, the increasing presence of data and data science has stirred up heated discussion and public concern on privacy issues. These discussions and concerns have become even more prevalent as a consequence of Edward Snowden, who leaked documents that uncovered the existence of numerous global surveillance programs, many of them run by the NSA and Five Eyes with the cooperation of telecommunication companies and European governments.⁴ Firms continue to underestimate the privacy concerns of customers and societal organizations. For example, when Dutch based ING bank announced that they will use payment information to provide customers with personalized offers and advice, there were strong reactions on (social) media.

The problems with creating value from data science mainly arise due to a lack of knowledge and skills on how to analyze and use the (big) data. In addition, firms might overestimate the benefits of big data and data science (Meer, 2013). One important danger is that firms start too big and start thinking too ambitiously, while actually lacking good quality knowledge of the basics and challenges of good data analysis in regard to already existing data, such as CRM-data and survey data, and how this can contribute to business performance. Firms start up large-scale big data projects with complex data mining and computer science techniques and software programs, without a proper definition of the objectives of these projects and the underlying statistical techniques. As a consequence, firms invest heavily in data science but may well face a negative return on these investments.

1.4 Objectives

So, given the growing importance of data science, its economic value and the problems firms face in capitalizing on these opportunities, we believe there is an urgent need to provide greater knowledge and to develop data science skills. By writing this book we aim to provide readers with this guidance. We specifically have the following objectives.

The main objectives of the book are threefold. The first is to learn how data science can be used to create value. For that reason, we discuss the increasing presence of data and relevance of data science, and also important value concepts. We consider privacy and data security issues, which are essential for effective use of data science in a responsible manner. As a second objective, we aim to show how specific analytical approaches are required, how value can be extracted from these data and to develop new growth opportunities among new and existing customers. Our third objective is to discuss organizational solutions for development and organization of the marketing analytical function within firms that will create value from data science.

1.5 Our approach

We believe in the potential power of data science. With this book we aim to teach readers how to use data science and analytics to create value. Building on extensive academic and practical knowledge of multiple issues surrounding analytics, we have written a book that aims to provide managers and analysts with strategic directions, practical data- and analytical solutions on how to create value from existing and new big data. This book has two specific target groups. First, it is targeted at students who want to gain knowledge on data science and develop their skills in courses on data science. We include examples in the different chapters and also assignments at the end of each chapter. The chapters on data analytics provide examples based on available data sets and codes in R. This allows readers to actively carry out the analytical techniques discussed. The additional data are available at www.masteringdatascience.eu. Second, this book targets managers and data science users who are interested in how data science can be used in marketing to create value and in the analytical and other skills that are required to implement data science initiatives.

1.6 Overview of chapters

In Chapter 2 we discuss our main data science value creation model that will be used as a guidance for the following chapters. Next, we have a chapter on the value objectives and metrics that are important in measuring value creation (Chapter 3). Subsequently, we have three chapters on data. Specif...