How Home Shopping Retailer CHANNEL21 Made An Extra Six-Figure Profit With Predictive Models

Exploiting Inactive Customer Potential

CHANNEL21 is Germany’s third-largest home shopping station and can be received in 95 % of all TV households. Six million customers enjoy the retailer’s full range of products, including fashion, beauty, health, jewelry, household goods, and electronics.

The retailer had steadily been generating a large number of new customers via TV. However, of the one million new customers, only 100.000 to 200.000 became regular customers. Since customer acquisition through the operation of the TV station is very expensive, the CRM department of CHANNEL21 wanted to make better use of the inactive customer potential.

 

Reactivation: Identifying The Probability of Future Purchases

For its advertising activities, mostly via print mailings, CHANNEL21 previously selected large customer segments based on traditional RFM criteria. These campaigns generated high costs, which were offset by an uncertain return.

With the CrossEngage Model Builder, CHANNEL21 created predictive models for the purchase probabilities and contribution margins of all customers with little effort and without IT, and thus determined those who were really worth the advertising effort.

RFM Criteria vs. Predictive Modeling by CrossEngage

The results of the previous customer selection were to be directly compared with the new prediction methodology by CrossEngage. In the longer term, surprising results showed up:

Today, CHANNEL21 implements various use cases with CrossEngage and constantly develops and improves its customer relationship management. As part of this, CHANNEL21 is realizing the strategic importance of intelligent data analytics in order to profitably exploit customer potential and ensure long-term economic success.

 

In this case study, learn what steps the home shopping retailer has taken to reactivate valuable customers, retain them for the long term, and thus significantly increase profits.