Introduction

Your friend has sent you on a treasure hunt. She has given you clues about how to find the treasure, but you’ll be left to draw on your own treasure-hunting skills to put the clues to good use.

Who is this friend of yours? It’s your boss, the owner of the company for which you are the marketing manager. What is the treasure you seek? It’s a business advantage that will allow your company to allocate its marketing dollars optimally and come out ahead of the competition. Those clues? That’s data your company has gathered about the past behavior of customers. And what are your treasure-hunting skills? They are the tools you will find in this book—the techniques needed to analyze past marketing performance and discover unknowns that will allow you to predict the future.

The broad view of how this is done is the discipline of marketing analytics—the process of creating models helpful in understanding consumer behaviors. It is the systematic use of empirical data about customers, companies, their competition and collaborators, and industry context to inform strategic marketing decisions. The function of marketing analytics can range from reports on regular marketing activities—such as paid search advertising click-through rates—to allocating marketing resources to maximize future performance of a company’s digital presence.

You have a lot to learn, and there’s no time to waste. You’ve got treasure to find.

Dunia is not the only company that places a high value on customer data these days. As technology has allowed firms to link customer behaviors more closely with the drivers behind those behaviors, an increasing number of companies are becoming comfortable using marketing analytics to gain a business advantage.

A 2013 report in Forbes magazine covered a survey of 211 senior marketers that showed that most large companies have had success using big data to understand customer behaviors. More than half (60%) of organizations that used big data a majority of the time reportedly exceeded their goals, whereas companies that used such data only occasionally reported significantly less success. Almost three quarters of companies that used big data a majority of the time were able to understand the effects of multichannel campaigns, and 70% of that group of companies said they were able to target their marketing efforts optimally.

Consider the effect of advertising. In the past, when television and print advertisements were the predominant form of pushing a firm’s message, the relationship between the ads and customers’ willingness to purchase the item advertised was not entirely clear. The firm rarely knew whether a customer bought the item because he or she had seen a television advertisement or because he or she had heard about it through some other channel. Collecting data about the success of the advertisements was indeed difficult.

With the advent of e-mail and web-based advertising, all that has changed. Firms are now able to closely connect their inputs (for example, ad placements) and outputs (for example, whether the target of the advertisement made a purchase). This produces a large amount of behavioral data. This data, in turn, allows companies to model existing customer behaviors and predict future behaviors more precisely. (It is important, however, to note that with big data comes a big problem—namely, the risk of false positives, or seeing patterns among chance events.)²

To avoid making mistakes with big data, business intuition is critical. Intuition allows the savvy marketing manager to select the correct inputs and outputs for a model. Analytics allows a company to take this traditional static dashboard of metrics or measurables and turn it into a predictive and dynamic entity.

Marketing analytics is not a new field. It simply allows companies to move beyond reports about what is happening in their businesses—and alerts about what needs to be done in response—to actually understand why something is happening based on regressions, experiments, testing, prediction, and optimization.³ What is new is how skilled companies have become at using marketing analytics. The availability of granular customer data has transformed firms’ marketing-spending decisions. Sophisticated econometrics combined with rich customer and marketing-mix data allow firms to bring science into a field that has traditionally relied on managers’ intuition.⁴

After reading this book, you will be able to (1) understand the importance of marketing analytics for forward-looking and systematic allocation of marketing resources; (2) know how to use analytics to develop predictive marketing dashboards for an organization; (3) understand the biases inherent to analytics that derive from secondary data, the cost-benefit trade-offs in analytics, and the balance between analysis and intuition; and (4) learn how to conduct data analysis through linear regression, logistic regression, or cluster analysis to address strategic marketing challenges.

This text places a big emphasis on practical guidance and striking the right balance between technical sophistication and managerial relevance. This is accomplished by real-life cases and real-life data connected to the cases that allow you to take a hands-on approach to the analysis. The book emphasizes all three aspects of marketing analytics: statistical analysis, experiments, and managerial intuition. The website http://dmanalytics.org provides videos on implementing the analytics techniques discussed in this book using commonly available statistical analysis software.

This book emphasizes that (1) analytics needs to support broader strategy; (2) inferences are inherently biased by available data, information, and techniques; (3) managers constantly make cost-benefit trade-offs in analytics; and (4) not every strategic question is answered by analytics—smart managers know to balance analysis and intuition.

Resource allocation provides a strategic and unifying framework for the wide-ranging purposes of marketing analytics within an organization; we therefore build marketing analytics around the resource-allocation framework. You can view analytics as the engine that provides a forward-looking perspective for marketing dashboards. The chapters in this book are organized around primary marketing functions. Section II, “Product Analytics,” starts with analytics that relate to product management decisions, such as market segmentation and pricing. Section III, “Marketing-Mix Analytics,” then moves to media or marketing-mix management decisions where the focus is on obtaining reliable estimates for price and advertising elasticity. Customer lifetime value is then presented as an organizing framework for customer analytics in Section IV, “Customer Analytics.” Here you learn about tools to predict customer retention and profits. The emerging and popular field of analytics related to digital marketing is the focus of Section V, “Digital Analytics.” It introduces design of experiments, search engine marketing, and mobile marketing. The book concludes by revisiting resource allocation and ties the different analytics tools with a case study that deals with allocating marketing resources for cross-selling products. Section VI, “Resource Allocation Revisited,” then presents a forward-looking perspective on marketing analytics and provides an action plan for implementing marketing analytics in organizations and developing a learning organization that systematically includes insights gained from analytics in their strategic decisions.

2. Wes Nichols, “Advertising Analytics 2.0,” Harvard Business Review (March 2013).

3. Thomas Davenport, Competing on Analytics: The New Science of Winning (Boston, MA: Harvard Business School Press, 2007).

4. Nichols.