Customer Selections For Over 20 Million Print Catalogs
The Walz Group, hidden champion in the European mail-order business with 244 million Euros in sales and over 1,200 employees, operates the brands Baby-Walz, Die moderne Hausfrau and Walzvital. Since 2000, the company has been acting online with its own shops.
With its multi-channel approach, the brand Die moderne Hausfrau generates annual revenue of 100 million Euros, making it the market leader in the segment for innovative household and garden products. In sales, the focus lies on the print channel. More than 20 million catalogs with over 200 pages in A4 format and over 1 million mailings are sent out every year.
Before the Walz Group started working with CrossEngage, RFM models were used for the selections of catalogs and mailings, which were set up once according to fixed rules and applied until further notice. The desire came up to form further and more targeted customer clusters, so that in the next step the print trigger chains could be controlled in a more segment-specific way and to get closer to the goal of customer-centricity. However, the personalized targeting of advertising materials required a considerable amount of time. There was a lack of capacity for the creation of the necessary statistical models as well as the required specific knowledge in the field of data science. From this problem and the desire for effective customer scoring, the cooperation with CrossEngage was started.
Optimize Customer Scoring With CrossEngage
The initial results using predictive models from the CrossEngage Customer Prediction Platform (CPP) were more than promising.
+ 29 % Revenue
Compared to the previous benchmark, the use of the CPPmodels generated additional revenue of 29 % for this campaign.
+ 16 % Conversion
The catalog conversion in the customer group evaluated and selected by the CPP-models was approximately 16 % higher.
+ €500.000 to €600.000
The initial results of the monthly catalog send-outs indicated an additional revenue of €500,000 to €600,000 per year.
Today, Walz uses predictive models from the CPP for all customer situations and 20 million print mailings a year, and has fundamentally optimized its CRM approach with the help of scalable customer ratings.
“The tool is very dynamic.
We can now build, test, and evaluate scoring models on our own. We use the advantages of data science without having in-depth data mining knowledge ourselves.”
– Tim Steffen, Campaign Management Specialist, Walz
Read our case study to learn about the steps required to achieve these successful outcomes, the use cases the mail-order company is optimizing, and the long-term benefits of the CrossEngage CPP.