Leveraging data rich environments using marketing analytics

Niels Holtrop

Research output: ThesisThesis fully internal (DIV)

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With the onset of what is popularly known as “big data”, increased attention is being paid to creating value from these data rich environments. Within the field of marketing, the analysis of customer and market data supported by models is known as marketing analytics. The goal of these analyses is to enhance managerial decision making regarding marketing problems. However, before these data rich environments can be used to guide managerial decision making, firms need to grasp the process of doing so. Therefore, in this thesis we explore two opportunities and one challenge that firms are faced with in this process.
The first opportunity we identify is the possibility to get enhanced insights on own and competitors’ market behavior. Here, the difference in reactions to competing strategic and tactical marketing actions is investigated, in order to improve future decision making in the face of competitive response.
The second opportunity identified is the possibility to engage in real-time marketing. Using ideas from statistical quality control, a control chart method to track customer purchase behavior is developed. Using this approach, firms can decide what the best time to approach a customer with a marketing action is, increasing the relevance and effectiveness of such actions.
The challenge investigated is that of maintaining customer privacy when using marketing analytics. In the setting of customer churn prediction, it is shown that firms can still perform effective analyses of customer churn while maintaining customer privacy. The method developed in this chapter assures this.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Groningen
  • Wieringa, Jaap, Supervisor
  • Gijsenberg, Maarten, Co-supervisor
Award date30-Mar-2017
Place of Publication[Groningen]
Print ISBNs978-90-367-9523-4
Electronic ISBNs978-90-367-9522-7
Publication statusPublished - 2017

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