Case Study Basler Kantonalbank

Marketing-Response-Quoten increase significantly





„The campaigns are now more targeted and efficient.  It is quality before quantity.“

Pascal Freudiger, Data Scientist, Basler Kantonalbank





Basler Kantonalbank relies on intelligent analysis solutions


When it comes to marketing, it's the same with banks as with any other company: they need to be in constant contact with their customers to meet their needs.  They need to know what their customers' lifestyle and habits look like.  Thereafter, the marketing campaigns of the banks should be aligned in order to offer customers appropriate products and services for their current life situation. 


The Basler Kantonalbank (BKB) also endeavors to implement personalized and targeted campaigns.  Pascal Freudiger, Data Scientist at BKB, explains: "In the past, we have clients based on common attributes such as gender or age group selected.  And because we manually created the data selection, preparing the campaign took a long time.  That was not always efficient. "To counteract this and to intensify marketing activities, a new plan had to be created.  After evaluating several solutions, BKB chose IBM® Customer Insight for Banking.  This was perceived by the BKB as user-friendly, efficient and easy to maintain.  It was implemented by iRIX Software Engineering AG.


Customer Relationship Management


Considerably higher success rates due to the analysis solution


Jörg Koch, expert in analysis solutions for the financial industry at iRIX, explains the approach: "We participated in a proof of concept with predictive analytics tools to help BKB select their customers for specific campaigns."  The A / B test selected customers using the traditional method, while the other group used the leads generated by the IBM Customer Insight models.  Pascal Freudiger confirms: "With the traditional approach, we achieved a success rate of 7 percent - with the Customer Insight models, we were able to achieve a success rate of 35 percent."


Instead of selecting customers manually as before, BKB can now provide data on its customer behavior from the  Drag the central data warehouse to create a master record of 650 fields.  These fields can be selected, for example, for account values ​​or sales.  These descriptive data are used to create specific statistical models for specific use cases.  These use cases can then be used to optimize the marketing campaigns.  So say these models how customers respond to advertising or how product-aware they are.  Finally, the customer advertising can be specifically tailored to it.  Pascal Freudiger explains, "We use the models over and over again to rate and classify customers based on more than 600 key attributes, such as customer master data, business volume or aggregated transaction volume."



Increased customer responses


Thanks to the use of predictive models, the Bank achieved a 5 times higher response rate within the "proof of concept" than with the manually selected control group.  Since then, machine learning methods have been used successfully within marketing campaigns.  The analysis services are also used by the sales department for spontaneous sales campaigns. The manual selection of customers had previously taken an enormous amount of time.  This is no longer necessary with the new solution and the previous effort could thus be significantly reduced. 


In addition, the sales department achieved efficiencies by improving sales processes through customer behavioral analytics and scores. It can better track customer relationships while avoiding repeated contacts.  Pascal Freudiger is pleased with the result: "Although we now contact fewer customers in a single campaign, revenue per campaign has not decreased significantly as campaigns are now more targeted and efficient.  It's quality before quantity." 



Basler Kantonalbank


Headquarter in Basel, founded 1899

49 branches, 150,000 private and corporate clients

800 employees, total assets CHF 22.8 billion




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