In their book Verhoef, Kooge and Walk break through the mystique around Big Data. It shows that, fact based marketing can be very successful for organisations confronted with a lot of data. At the same time, it is stated that Big Data is not a revolution. Above all, it’s more data from more sources, especially from online channels. Although new marketing issues have arisen, it is shown that common statistical methods can be used for both traditional and big data analytics.
In essence the book leads the way in choosing the right metrics and analytics to get the right insights on different marketing issues and for improving marketing performance on the short term, but also in the long run. Because of the arisen availability and volume of data the possibilities are numerous and often real time effective. This is illustrated by some insightful cases. The intrinsic value of the book is in bridging the gap between Marketing Intelligence (MI) and other (marketing) specialisms. Furthermore, the book structure with numerous frameworks and examples is very pleasant. If preferred, one can also take a quick route through the book (without going through technical data-issues and statistical details).
Chapter 2 starts with an insightful overview of marketing metrics. First of all, a clear division is made by value for the customer (V2C) and value for the firm (V2F). V2C is the value of products and brands from a customer point of view (evaluation). V2F concentrates on the returns for the firm (€). V2C and V2F must be balanced. Secondly, the metrics are distinguished by market level, brand level and customer level. It is a great opportunity for firms to link all kinds of metrics or KPI’s (f.e. through dashboards) and bring together different (marketing) disciplines in order to create insights and improve overall marketing performance. One of the cases in chapter 6 illustrates this.
Chapter 3 focuses on specific data issues. It starts with a rather technical part, such as explaining the different sources and types of data and the integration of data. This part is especially useful for students and specialists. Privacy issues are also explained here and some useful privacy policies are stated.
At the heart of the book in chapter 4, traditional analytics and new big data analytics are explained in further detail. This is all done in a very understandable (not too much statistics) and inspiring way. We are also taught where real analytics differs from reports, monitors and dashboards. Although these last tools can have a substantial alert function, sophisticated analytics are used for more complicated business problems and aimed to be predictive and actionable. The summarizing frameworks, which relate specific marketing issues to different types of analytics including the applicable statistical methods, are very helpful in keeping track on all possibilities. Because Marketing and MI often have difficulties in understanding each other’s work approach the book gives a kind of manual how to overcome this problem by storytelling (clear formulation of the marketing problem) and visualization (by pictures and graphs).
It is preached, that the MI department should have a leading role in defining business challenges. A roadmap for the development of an influential MI-department is given in Chapter 5, including process, people, systems and organization. This chapter is very interesting for business managers.
Chapter 6 describes five cases, which prove how value can be created with big data analytics. Sometimes even simple algorithms can achieve great results. For example, one of the cases shows how behavioural targeting literally works and leads to higher conversion rates.
“Creating Data with Big Data Analytics” is an interesting, recognizable and accessible book for managers as well as for specialists like data scientists. Different marketing worlds are brought together, so that firms can actually create value from their big data.