Acquisition – cross-selling – customer retention
Efficient acquisition and realisation of customer potentials

Customer-profile models form the basis of our acquisition programmes. These data mining-supported processes promote efficient customer acquisition by identifying potentially receptive target groups in the marketplace and therefore avoiding wastage.
Furthermore, we promote the realisation of customer value through cross- and up-selling. Our processes enable us to forecast product-specific turnover expectations (customer value and turnover profile) for individual customers – a popular feature for strategic marketing planning.
Customer lifecycle management
- Targeting
- Acquisition
- Customer value prediction
- Churn management
- Marketing automation
Our own specially-developed Churn Predictor forecasts the likelihood of subscription cancellation / churn. This process enables the early localisation of potential cancellation candidates, so that effective reactivation activities can be applied.
Efficient marketing and sales activities require professional tools:
Our customer value, churn prediction and cross-selling forecasts are based on our own specially developed data mining processes. Telemarketing agents use our data mining-aided marketing platforms. Our lead management systems supply sales leads to sales units. In addition to filling the sales pipeline, our lead management systems allow optimised sales controlling and therefore ensure perfect lead management.
Our existing campaign management and database marketing systems serve to automate marketing processes. They enable the planning, control and implementation of complex, multi-channel sales promotions.
Business intelligence solutions combine the relevant marketing, sales and customer information and present these on intuitive marketing dashboards. The basis for campaign management is derived from our data warehouse approach, which generates a marketing-relevant customer profile out of randomly distributed customer information.
Akquisition Cross Selling Churn Management

