Challenge & Pain Points:
Attracting new customers is much more costly than keeping existing ones. Accurately predicting whether a customer is likely to cancel their policy, or not renew it, is thus a highly valuable capability for an insurance provider. If a crystal ball could show which customers are considering leaving, one could attempt to change their minds by offering promotions, or could do targeted surveys to determine common causes of strong dissatisfaction in order to improve products and services.
One common way to attack this problem is via expert analyses of past data, trying to find clues, with the benefit of hindsight. Which aspects of a customer’s history or demographics could be predictive of their nonrenewal of a policy? This kind of manual analysis is a difficult task that requires domain knowledge, and painstaking trial and error. Methods of traditional statistics like logistic regression take some steps towards automating this task, with the added limitation of strong assumptions on the way the likelihood of churn depends on the observed behavior and demographic data.
Insursight.ai Solution:
The Insursight.ai Churn Predictor product takes a machine learning approach to churn prediction, taking in all available data including policy characteristics, demographics, external factors such as seasonality, and crucially, various forms of recent customer engagement, to predict the likelihood of churn in a specified time period. Without strong mathematical assumptions such as those of logistic regression or GLM, our models are able to “listen to” what the data actually says, rather than trying to put the rich “square peg” of customer behavior into the “round hole” of a parametric model. In addition to making accurate predictions of churn, the model also gives insights into the factors resulting in churn via modern techniques of machine learning model interpretability.