Especially in saturated markets a growth of business through new customers is hardly to accomplish. Thus the central aim is to increase the customer’s loyalty to the company and to strengthen the competitiveness.

But how can this be achieved?

First of all, the following question must be answered:

What is the expected probability for each customer to churn in what time frame?

At this, customer data (master and transaction data, but also external micro- and socio-demographic data) and their analysis by Data Mining are very helpful.

With Data Mining the utility is capable e.g. of identifying fully automatic segments of customers which are most likely to churn.

In the second step within these segments affinity profiles can be extracted with respect to the payment and product behaviour.

The combination of the churn probabilities with these affinity profiles in the third step is used for marketing and call centre campaigns in order to proactively contact those customers which

■	are most likely to churn
■	but also promise an above-average prevention success

In this way, within the potential churners you let “sleeping dogs” lie and the offer of the utility within the scope of the prevention measure is just as high as the value of the customer (discounted cash-flow) for the company.

Products:
 prudsys DISCOVERER
 prudsys Preminer
 MERKUR MINER PLUS