Download Now! Free 30 Day Trial of Salford System's Predictive Modeling Suite

Upcoming Tradeshows

View full calendar
Home Resources Case Studies
Fleet Uses CART® Data Mining Technology to Understand Customer Characteristics and Habits
Hybrid Analysis Methods Target Q3 Home Equity Product Promotion Mailing

Computer 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 valuable information on more than 15 million customers, Fleet's analysts are using data mining software, including Salford Systems' (San Diego, Calif.) 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. To do that, Fleet needed to learn about customers' financial characteristics and buying habits so as to target the mailing list for the company's third quarter home equity product promotion. Victor Lo, a Fleet lead analytic 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 to estimate 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 which 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's (1998), 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.

Building the Foundation

"The goal of the mailing project for our home equity line of credit was to identify characteristics of would-be customers and to create a predictive model that could score new prospects," says Lo. "We chose to employ CART because it is an advanced, non-parametric data analysis technique that can efficiently handle missing data values. By hybridizing CART and logistic regression techniques, we were able to use each methodology's strength to complement the other's. CART, in particular, brings with it the unique advantage of helping analysts understand people's behavioral patterns, and it provides excellent predictive accuracy with a proven methodological track record."

The first step in the modeling process was to gather the historical data on which to create the model. The team selected a sample of approximately 20,000 customers for which Fleet had a record of responses; included were 100 percent of past profitable respondents, as well as two percent of past non-respondents. The customer records were "massaged" into a data set and output as a text file that could be fed into different modeling tools.

Mating Methods

The data set was then transferred into CART to display the interaction of the data. The resulting effects were subsequently incorporated into a logistic regression model that illustrated the overall and local landscape of the data. When the data were fed into CART, the software automatically generated a decision tree whose branches, or nodes, showed the hierarchy of binary data splits and displayed the data set's myriad variables and their interactions. This hierarchy distilled nearly 100 predictor variables into a more manageable amount of approximately 25. In addition, the CART nodes provided probability ratios that were used to understand why one segment would be more responsive than another.

"The CART analysis enables an intuitive understanding about the variables and the interactions among them", says Lo. "In Fleet's marketing of this product promotion, this is an essential piece of the puzzle - CART helps provide a human understanding of why certain segments respond better than others, as well as what their needs are and what types of offers will provoke a response."

Understanding Customers

Opening door The CART model illustrated certain characteristics of "best" respondents by predicting the expected balance they would carry on the credit line, as well as how much they might transfer from another line. In addition, the CART results painted a portrait of the principal characteristics of the least responsive customers. These prospects would either not likely respond to a Fleet product offer because they do not have a need for a large line of credit, or - equally of concern - they would respond but their subsequent credit line usage and/or likely losses would not be profitable for the bank. "Within a predictive model, accuracy is very important, but it is also important to obtain a true understanding of one's customers," says Lo. "The more we can learn about why a product does or doesn't suit a certain segment, the better we can manage the business and our profitability in the long term; for this project, CART gave us that understanding."

This home equity product mailing might bring an increase in revenues compared with past product mailings. More importantly, it is expected to have a much higher profitability due to more efficient targeting and lower marketing costs, which would give Fleet a higher return on investment. Lo's team is cautiously taking into consideration other factors, such as the mailing's time of year and the number of other financial product offerings customers have received recently.

"Test and control groups are needed to validate the efficiency of our targeting with this predictive model," says Lo. "We are, however, very confident that Fleet will achieve a high response rate with this mailing. Our customers have many more dimensions than the previous mailing model could encapsulate for predictions. Creating a hybrid model using CART and our other data mining and statistical tools was a more sophisticated approach that painted a very descriptive portrait of our prospects, enabling us to increase the probability of their response."

Broadening Applications

The third quarter home equity promotion is Fleet's first CART modeling application. The company is currently evaluating and applying many more tools to manage its customer information and data warehouses; in the meantime, future plans for CART include applying it to various neural network algorithms and other projects. Once Fleet has received results from this home equity mailing, for example, the responses will be analyzed again in CART to validate the robustness of the original model and to determine other successes, such as whether or not the probability scores are accurately reflected in the response rate. Then CART will be used to construct a new model for the next home equity product mailing based on this mailing's response rates. Says Lo, "This test and learn approach is on-going, and the demand for better models and sophisticated techniques will continually grow."

Fleet will continue to use CART to gain a deeper understanding of its customers so that the information can be applied to classification and segmentation applications among Fleet's other product lines. "Fleet's product managers are anxious to have us determine customer characteristics, identify cross-selling opportunities for products - such as certificates of deposit, money markets and mutual funds - and build predictive models for their promotions,"says Lo. "CART's insight into our customers will help us better support our marketing departments, and it will help Fleet harvest an impressive return on our data warehouse and customer information investments."