Browsing by Subject "logit model"

This thesis examines underlying causes of customer churn in the Finnish insurance market. Using individual data on moving insurance customers, econometric modeling is conducted to find significant relations between observed customer characteristics and behavior, and the probability to churn. A subscription-based business gains revenue not only from new sales but more importantly from automatic renewals of existing customers, i.e. retention. Significant drops in retention are important to understand for the insurer in order to not lose profit. Churn is an antonym for retention. A change of address – or moving homes – is an event around which churn rates spike, as it is a time when all address-specific subscriptions (electricity, internet, etc.) need to be proactively renewed by the consumer. There were one million moving individuals in 2016, as reported by Posti. This means that a significant share of an insurer’s customers are at a heightened risk to churn, with an address change being the common denominator. This thesis asks which customer characteristics and experiences significantly either increase or decrease the probability of a customer either changing their home insurance or churning completely around the time of their move. Insurance literature such as Hillson & Murray-Webster (2007) and Vaughan (1996) are reviewed to present the nature of risk, the insurance mechanism and the modern insurance business model. An annual report by Finance Finland (2017) provides accounting data via which the Finnish market situation is presented, while data and reports by Posti (2016; 2017a; 2017b) provide the numbers and facts regarding Finnish movers. Churn modeling is based on 20th century discrete choice theory, literature of which is reviewed, most notably by Nobel-laureate Daniel McFadden (1974; 2000). Also presented are modern applications of choice theory into churn problems, such as Madden et al (1999). The empirical section of the thesis consists of data presentation, model construction and evaluation and finally discussion of the results. The final sample of customer data consists of 24 230 observations with 21 variables. Following Madden et al (1999) and with help from Cox (1958) and McFadden (1974), binomial logistic regression models are constructed to relate the probability of churning with the specified variables. It is found that customer data can be used to predict churn among movers. Significant weights are found for variables denoting the size of a customer’s insurance portfolio as well as customer age and the duration of customership. Also the presence of personal insurance products and contact with one’s insurer notably affect retention positively. Younger segments and customers with implications of lower income (with fewer insurance products, more payment installments) exhibit a significantly increased probability of churning.