PhD Defence: Bridging Models and Business - Understanding Heterogeneity in Hidden Drivers of Customer Purchase Behaviour
Companies are often clueless about when their consumers will purchase again. In her PhD dissertation, ‘Understanding Heterogeneity in Hidden Drivers of Customer Purchase Behaviour’, Evşen Korkmaz will help businesses determine how much time and effort individual customers are worth investing in. Korkmaz reveals that by using existing customer base analysis models and extending their analytic capabilities, companies can predict the specific interests, and purchasing behaviour of customers at an early stage in order to develop individual marketing strategies.
Recent years have seen many advances in quantitative models in marketing literature. These advances enable model building for a better understanding of customer purchase behaviour and customer heterogeneity in a way for firms to develop optimal targeting and pricing strategies. Still, it has been observed that not many of the advanced models have found their way into business practice. With her dissertation, Evşen Korkmaz bridges the gap between advanced models and their business applications by systematically extending the use of models.
Sometimes a company hasn’t heard from a particular customer for a while, due to matters such as a changing family situation, holiday leave, or the loss of interest in your product. While a customer does not often share this information, companies should know whether that customer is still active and worth approaching. To map this out, businesses use probabilistic models that utilise customer data from the past to predict their future behaviour. However, in her dissertation, Korkmaz reveals that much more can be read using existing models.
Korkmaz focuses on probabilistic customer base analysis models that deal with understanding customer heterogeneity and predicting customer behaviour. These models specify a customer's transaction and defection processes under a non-contractual setting. Through this study, she shows that the timing of the next purchase for each customer can be predicted using these models. This is the type of information that allows a company to make relevant investment decisions in customer acquisition/retention.
Building two new models, Korkmaz interprets customer heterogeneity in a more flexible and insightful way. As a result, managers can obtain a refined segmentation. Customers are no longer analysed as one heterogeneous group, but rather as several heterogeneous groups, so-called customer segments, so that one can analyse within and across heterogeneity, for whom both individual and segment-specific marketing techniques can be determined.
Businesses can learn from this research how to get a hold of more – and more accurate – information on their customer’s behaviour by using data they already have.
Evşen defended her dissertation in the Senate Hall at Erasmus University Rotterdam on Friday, 12 September 2014 at 11:30. Her supervisors were Professor Steef van de Velde and Professor Dennis Fok and her co-supervisor was Dr Roelof Kuik . Other members of the Doctoral Committee were Professor Leo Kroon (ERIM), Professor Gerrit van Bruggen (ERIM), and Professor Florian von Wangenheim (ETH Zurich).
About Evşen Korkmaz
Evşen Korkmaz received a BSc in Industrial Engineering (2005) and an MSc in Industrial Engineering with a specialization in Operations Research (2008) from Istanbul Technical University with distinction. In 2009, she started her PhD research at the Rotterdam School of Management, Erasmus University Rotterdam. Her main research interest lies on the interface of Marketing Modelling and Operations Research. Her research has been presented at several international conferences such as INFORMS Annual Meeting, Production and Operations Management Conference and ISMS Marketing Science Conferences.
Abstract of Bridging Models and Business: Understanding heterogeneity in hidden drivers of customer purchase behaviour
Recent years have seen many advances in quantitative models in the marketing literature. Even though these advances enable model building for a better understanding of customer purchase behavior and customer heterogeneity such that firms develop optimal targeting and pricing strategies, it has been observed that not many of the advanced models have found their way into business practice.
This thesis aims to bridge the gap between advanced models and their business applications by systematically extending the use of models. We first focus on probabilistic customer base analysis models that deal with understanding customer heterogeneity and predicting customer behavior. These models specify a customer's transaction and defection processes under a non-contractual setting. Through this study, we show that the timing of the next purchase for each customer can be predicted using these models. We also extend them by modeling customer heterogeneity in a more flexible and insightful way. As a result, managers can obtain a refined segmentation. Based on the customer heterogeneity insights, we then focus on pricing strategies for online retailers who derive their revenues from delivery fees and sales. In order to come up with optimal pricing strategies for delivery fees, we use ideas from the two-part tariff literature.
Given the time and costs associated with implementing advanced models/theories in managerial practice, the marketing executives need to be convinced by clearly demonstrating the contributions of such models. Our study serves as a step toward bridging advanced models and business practice by empirically demonstrating their extended contributions.
Photos: Chris Gorzeman / Capital Images
