Today, changing the insurance of your car or your home has become as simple as changing your telephone operator. The surge in comparison sites, with their increasingly competitive price offers, and more recently the evolution of legislation with the implementation of the Hamon law, offers consumers greater freedom in terminating and changing insurance contracts. Yet, winning a new customer remains costly and is energy and time consuming. It therefore seems more effective to detect and counter terminations in a timely manner.
Changing perspective: Moving the focus to prevention
For many years, players in the insurance, banking and telecommunications sectors had to content themselves with trying to retain the customer once his departure was almost certain. When the risk has materialized (a termination request online or by telephone), it is often already too late. Currently, the enormous amount of data generated by digitalization makes it possible to fight against attrition by going back up through the marketing tunnel to detect an initial intention. Thanks to predictive marketing, it is possible to do so before this intention is formalized by the customer. Big Data can highlight faint signals that indicate the likelihood of customers terminating a contract, and enables the calculation of scores to measure the inclination of customers to do so. It can also help identifying the causes in order to be able to implement concrete marketing actions.
The added value of big data
First and foremost the added value of big data is the cross-checking of heterogeneous and siloed data sources as well as their updates in real time:
- Rarely accessed ‘cool’ data (contracts, claims, customer information). Operational staff have few levers, but can assign a volatility or attrition score to the customer. These are the data used traditionally, often updated with too little frequency.
- Frequently accessed ‘hot’ data (internet navigation, telephone contacts at the call center, request for quotations). These data will help to enrich customer knowledge and identify “labels” specifying the signals detected and giving indications on how to retain the customer.
Cross-referencing the data is done on the individual level over a rolling year and can be done in real time during the industrialization phase.
Impact on the organisation: the real challenge
In case it is relatively easy to generate this knowledge, the challenge is to make it operational and actionable by the sales or marketing teams. Such a project requires complete cooperation between marketing, customer relations, IS, etc., to gather data, make calculated information available to all as well as to implement relevant actions with the customer. This process must be industrialized in order to be sustainable.
Thus, beyond a relevant score of customer volatility, the data will highlight insights that can be used to carry out actions focused on retaining customers, in order to capture them in the period between the beginning of the dissatisfaction and the actual act of the cancellation!
You’re interested in using data to detect warning signs of termination and thus to lessen your churn ? Contact our experts !
Flora Vidal, Consulting Manager