Abstract
This dissertation consists of two chapters. Chapter 1 provides a detailed account of customer, business and employee churn and how each of these churn types have been studied in extant literature. Consequently, a more comprehensive definition of churn that is grounded on the notion of various types of partnerships (B2C, B2B and B2E) is presented. Additionally, a churn framework is developed after analyzing leading research in marketing and non-marketing journals. This analysis reveals that churn can be attributed to a combination of both entity and non-entity characteristics, although entity characteristics appear to be more common. Methodologically, churn continues to be studied through various supervised learning methods in a variety of industries.
Finally, a proactive approach to churn management consisting of various propositions that link each of the churn types to firm profitability is outlined. It is my hope that future churn research will adopt this empirical based approach.
Chapter 2 specifically addresses the example of a customer character model as a determinant of customer auto loan churn among a unique population of subprime borrowers. The banking industry proxies for character include residential months, employment months, net worth, assets to age and others. Ultimately, the customer character model is compared to a full model consisting of the 4Cs of capacity, collateral, credit and character of churn prediction. The results reveal that there is a difference between the full model and the restricted model. Furthermore, the various supervised classification methods of logistic regression, linear discriminant analysis (LDA), decision trees (DTs) and random forests (RF) are applied and compared in terms of multiple predictive performance measures. The random forest classification measures report the strongest performance. Additionally, the customer character variables of residential months reveal importance when conducting logistic regression and net worth when conducting decision tree analysis. The random forest classification method details the most important character variables to be net worth, followed by assets to age ratio, employment months and finally residential months.
Keywords: churn, business churn, employee churn, auto loans, 4C’s, classification techniques