
In their book Verhoef,
Kooge and Walk break through the mystique around Big Data. It shows that, fact
based marketing can be very successful and is inescapable 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. New big data analytics are focused on online data sources. Next to
a number of traditional analytics, the following big data analytics are
explained: web analytics, customer journey analysis, attribution modelling (to
assess the effect of customer touchpoints on conversion and sales), dynamic
targeting (to realize real time offerings), big data integrated models, social
listening and social network analysis (for the identification of influencers).
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 also 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. For marketing managers who aren't afraid of
some statistical terminology, this book shows that big data analytics can have
a lot of added value in answering questions like for example: “How can we
improve customer satisfaction by specific features of the service experience?”,
“What is the optimal assortment to offer in a store?”, “Which customers should
we aim on in our marketing campaigns?”, “Through which channels should we
contact a customer in order to optimize customer value?”.
“Creating Data with Big Data Analytics” is an interesting, recognizable and
accessible book for managers as well as for specialists like data scientists.
Because of the integrating approach of data, different marketing specialisms
can be brought together, so that firms can actually create value from their big
data.
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