Profitable Use of Customer Data

The Union Bank of Norway (UBN) had 1 million customers and $25 billion in assets. During the last 20 years, as the bank had grown and become more automated, it had become clear that bank employees were not interacting directly with customers as often as they had been, which meant that the bank was losing touch with its customers’ needs. The bank was still conducting traditional direct marketing programs with some success, its highest response rate being about 10 percent.

Since it operated multiple computer platforms with data scattered among the 175 branches, the bank could not easily gather and use customer-specific information. Getting a full view of a group of customers took days. Bank management wanted to store and access customer-specific information in a responsive, easy-to-use format. The goal was to improve revenue and margins through cross-selling and increasing customer use of its most profitable products.

To solve these problems, the bank turned to NCR’s Teradata Division, installing a Teradata data warehouse in 1995 to capture and store all information about customers, including transactions going back 4 years and about 40 data elements from customer survey responses. In 1999, UBN added the Teradata CRM solution, a relationship optimization and campaign management application, for its events-based marketing initiative.

The Teradata system became the foundation for UBN’s loyalty program and event-based marketing initiative. It helped the bank determine whom to contact, as it prioritized customers, looking at each customer and evaluating whether that customer needed to be contacted.

Events were detected by a set of business rules that searched the database nightly or weekly to determine if there had been any significant changes in a customer’s life. For example, if a customer made a large deposit in a low-interest account, the system alerted a banker to contact the customer and let the customer know that he or she was losing money.

Before implementing its event-driven marketing approach, the bank was concerned that customers might be sensitive to the “Big Brother” effect. Customer feedback from the pilot program showed that the fears were unjustified and proved the relevancy of both the offer and the quality of the interactions.

Result of the New System

By using the data warehouse for direct marketing, UBN increased cross sales, measured against controls. The bank computed a payback period on the investment of less than 12 months. Compared to a previous 2 to 5 percent response rate, traditional campaigns using information in the data warehouse produced conversion rates of up to 40 percent. Loyalty programs gave UBN response rates of up to 70 percent. A pilot program for event-based marketing yielded conversion rates of up to 60 percent.

The 60 percent sales conversion rate includes the customer history picture provided by the new database. Some of those who were contacted did not express an immediate interest. Their response was recorded in the database. Later, the customer ended up purchasing the additional products from self-generated decisions or as a result of an automated follow-up. The new conversion rate represented an 1100 percent improvement over the bank’s previous average close rate for direct marketing campaigns.