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Introduction to Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands On Learning

The authors of Cutting Edge Marketing Analytics discuss the techniques needed to analyze past marketing performance and discover unknowns that will allow you to predict the future.
This chapter is from the book

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.

Why Marketing Analytics?

Dunia Finance LLC is a midsized financial services firm that operates in a unique financial market. Unlike similar institutions in the Western world, the Abu Dhabi–based company does not have the benefit of a reliable credit bureau to provide information on consumers’ risk scores. Still, the company believes such scores are necessary to help it quantify decisions on product offerings. For example, risk scores indicate the interest rate Dunia should charge for a personal loan, as well as whether a personal loan customer is a good target for cross-selling credit cards. So instead of operating in the dark, the company has developed an internal system of tracking customer behavior and stores its data in a data warehouse. (For more information on Dunia Finance LLC, see Chapter 2, “Dunia Finance LLC.”)1

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

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