ACTIVE CAPITAL Marketing de la Relation Client (Prospection et Fidélisation Clients)

One of the leading financial services companies, Credit Suisse Group provides banking and insurance solutions for private clients, companies and institutions.

The Group primarily focuses on investment banking and managing customer assets.

Challenge
Competition in the financial services industry is intense, and obtaining new customers is an expensive proposition.

The Big Idea
In order to maximize profitability, Credit Suisse focuses on three areas :
- Identifying profitable current customers
- Managing customer relationships to ensure longevity
- Retaining profitable customer accounts.

Strategic Solution

Credit Suisse started a loyalty based program, with the primary goal of retaining profitable customers. They invested in a six-member data mining team to analyze a robust data warehouse of its 2.5 million customers, each with more than 400 attributes. The analysis was used to identify potential leads among Credit Suisse's customers and intelligently market to them based on their individual preferences and histories.

Measures of Success
Generated targeted customer leads for consultants
Customer leads based on data mining analysis were mainly generated for product-related database marketing. Consultants had very limited resources and depended on information that enabled them to use these resources as efficiently as possible. The consultants' services were also costly and their time and services had to be used effectively. As a result of the success of the project, Credit Suisse consultants begin to see data mining's benefits and started to use it to sell specific customers targeted services. Credit Suisse can now identify customers, typically the top one percent, who are extremely likely to buy a service, thus increasing the opportunities for cross-selling and retaining customers.

Tailored marketing programs to segmented customers
Detailed segmentation of its vast customer base allows Credit Suisse to develop targeted solutions for its customers. This segmentation is executed inductively using the cluster algorithm and the dimensions are tailored directly to the customer requirements. Each cluster serves as a starting point for individual marketing campaigns. This hierarchical system is advantageous because the customer database is continually researched and monitored. As a result, changes in the cluster structure are quickly identified and appropriate responses are triggered.

Increased efficiency of direct marketing campaigns
It's not enough to know whether customers are interested in a product. Will they actually follow though and purchase it? We therefore analyzed situations where customer interest in a service did not correlate with a purchase. Many times, customers did not have good enough credit and were subsequently refused the service. Improved models factored in credit as a criterion. As a result, the percentage of customers interested in purchasing a service but who were refused due to bad credit was reduced by almost half in subsequent campaigns. The reductions have allowed substantial cost savings.