A leading insurance was facing customer churn rates of 30% and more across all of its divisions. With the reasons for termination unclear it was difficult to face leaving customers with the right arguments to stay. Reaching out to the entire customer base would be ineffective and very costly. Neither was it possible to predict who would be leaving next and prevent it or to eradicate reasons for termination overall before they would show their effect. The insurance reached out to KIANA to look for a solution in their data.
KIANA gathered internal customer data from across all divisions in a central system and fed in further external micro-geographical data to include customers' financial health and other useful information. A decision tree was developed in close cooperation with the client's marketing department to identify clear customer segments that exhibited striking patterns. It became evident that leavers had profiles and account histories clearly different from other customer segments. XL customers with several insurance contracts who had not been compensated for damages recently for example exhibited termination rates of 90% and more. These customers were identified across all divisions and could now be targeted confidently with tailored customer retention efforts. Knowing the patterns that would lead to customers terminating contracts a strong basis for customer loss prevention was established.
The insurance reached over 90% of customers with high-risk to leave by contacting no more than 30% of its overall customer base and with targeted retention strategies. Together with the implementation of preventive strategic measures the rate of leaving customers could be sustainably reduced by 50% throughout all divisions. Analysing customer segments by gathering all vital internal data and enriching that with further external sources provides a powerful basis for customer retention but also cross- and up-selling measures in any industry.