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Creating Value with Big Data Analytics: Making Smarter Marketing Decisions
Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself 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. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.
338 pages, Paperback
First published March 8, 2016
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Displaying 1 - 2 of 2 reviews
January 26, 2026
Read like a dense scientific paper with common vernacular like "Nowadays" being used which was off putting considering that this is written by university professors. Really liked Chapter 4.3 on data visualization. Overall, it was hard to understand what yo take away from the read but worth taking time to explore the seminal literature around data and analytics.
June 2, 2016
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.
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.
Displaying 1 - 2 of 2 reviews