Lack of Advertising Effectiveness due to Misdirected Selection
Budnikowsky Handels- und Service GmbH & Co. KG operates 185 drugstores with 1,950 employees in the Hamburg region. One million customers hold the Budni customer card – 40% of them for over 14 years. At the start of the partnership, customers with customer cards generated 50% of total sales but were considered to have little affinity for advertising. About 90 print campaigns sent out each year did not achieve a significant lift in purchasing behavior.
The previous marketing approach was based on a rough selection of customers who had bought certain products in the previous months. This approach required great operational effort and led to high circulation and advertising costs.
There was a desire to make the targeting of advertising measures more efficient, reduce the waste of advertising resources, and save on costs as a result. Since internal resources were missing to forecast precise customer values (CLV), the CrossEngage Customer Data and Prediction Platform became the solution of choice. With its help, predictive models based on Automated Machine Learning (AutoML) can be created with little operational effort.
Individual Customer Value Predictions (CLV)
Within a month, CrossEngage was integrated into the day-to-day business of Budni’s CRM manager. Using the no-code predictive model builder, she began by calculating the individual, predicted CLV of each Budni customer. The customer selection and approach could then be controlled in a customer value-oriented manner.
“CrossEngage is an insane time saver for me knowing: the quality of the results is always high.”
– Gabriele Schilling, Strategic CRM, IWAN BUDNIKOWSKY GmbH & Co. KG
In particular, the precise identification and reactivation of customers at risk of churn and the targeting of customers with a high affinity for advertising created an astonishing uplift.
– 30 % Costs
As a result of the realignment, advertising costs were reduced by 30 %.
+ 12,5 % Profits
The use of the CPP helped Budni to increase their profits by 12,5 %.
In this case study, you will learn which use cases Budni successfully implemented and which three long-term achievements the drugstore chain has made with the help of predictive analytics.