Fleet Financial Banks on Data Warehouse
by Kimberly Rengle
Fleet Financial Group, a Boston-based financial services company with assets of more than $97 billion, is currently redesigning its customer service infrastructure, including a $38 million investment in a data warehouse and marketing automation software. To profit from this repository of information on more than 15 million customers, Fleet's analysts are using data-mining software, including Salford Systems' CART, to learn about their customers and to better target product promotions, such as home-equity lines of credit.
"The real key is implementing a disciplined business plan that enables us to sell the right product to the right customer," says Randall Grossman, senior VP and manager of Fleet's Customer Data Management and Analysis (CDMA) group. And to do that, Fleet needed to learn about customers' financial characteristics and buying habits in order to target the mailing list for the company's third-quarter home-equity product promotion. Victor Lo, a Fleet lead analytical consultant and VP, and his team were tasked with developing a model to estimate each prospect's probability of responding to the mailing, as well as estimating the expected profitability of respondents. Based on this expected profitability, the database would be segmented by scores that identify which prospects should receive one of several home-equity marketing pieces and who should not receive a mailing at all.
Previous home-equity product modeling had been conducted through third-party consultants who used a matrix, or a two-dimensional table, to determine which prospects should be mailed which promotional package. The mailings had been profitable, but Fleet's analysts knew that there was more to be learned about customers and prospects. During the first quarter of 1998's home-equity product promotion, Fleet became more involved in the modeling process by assigning prospect response scores and further targeting the mailing. The subsequent third-quarter home-equity product mailing list was handled completely in-house by combining CART and other data-mining and statistical techniques.