A robust, central database drives GreenPoint’s profitability in the banking sector
Pareto’s Principle holds that 80 percent of a company’s business is generated by 20 percent of its customers. In banking, the ratio is more like 90/10 or even 95/5, points out Bob Lord, vice president of strategic marketing services at Harte-Hanks. This makes identifying, retaining and maximizing the profit potential of this top performing tier of customers essential to a bank’s overall profitability.
Banking is a highly competitive financial services sector that has witnessed a great deal of merger and acquisition activity in the past 10 years. This flurry of activity was in part responsible for GreenPoint Bank’s decision to consolidate its client information. It enlisted the help of Harte-Hanks and its multichannel integration system, Allink Financial, to gather customer information scattered across disparate operations, and find a way to house it in a central, client/server environment, accessible by multiple users across the financial institution.
GreenPoint Bank currently serves more than 475,000 households through 90 branches in eight New York counties. By consolidating its data—formerly housed in multiple departments and databases—the bank can take advantage of data analytics and segmentation models to identify its best customers, says GreenPoint’s Vice President Leo Khmelniker. He explains that in doing so, GreenPoint has developed more targeted offers for its best performing and potentially best-performing customer segments, and has gained a greater share of its customers’ wallets by offering additional financial products.This has resulted in a 5-percent to 10-percent lift in response to its cross-sell and upsell campaigns.
According to Khmelniker, the success of GreenPoint’s marketing programs is very much dependent on the data integration within the marketing database and the bank’s ability to analyze customers over various periods of time. GreenPoint’s marketing customer information file currently has 35 months of
customer history with transactional data available. After overlaying customer information with additional demographic data, GreenPoint models its customer data to find look-alikes on outside rented lists that would be prime for prospecting.