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Unleashing Branch Power at Fleet

For the last few years, banks have been exploring the potential for customer relationship marketing with very productive results. The process goes this way:

  • Creating a Marketing Customer Information File. This is the first step that many banks explored in the 1980’s. Accounts for many of the bank’s different products – many of them housed on different computers in different formats – were brought together once a month, reformatted, and householded, to produce a single consolidated statement for each customer. Many used that statement to produce a consolidated monthly report to the customer, although most did not.
  • Computing customer profitability. This was the next logical step which many banks have been exploring in the 1990’s. For each product owned by each customer, the bank figured out the revenue and the costs involved. For banks the computations were quite complicated, involving interest revenue and costs, the cost of capital, transaction fees and costs, maintenance costs, product sales costs, provision for losses, etc. The software is extensive. Each variable (interest rates, capital hurdle rates, etc) is subject to monthly changes. The output of the process is a monthly new profit (or loss) figure for each customer in the bank.
  • Segmenting customers by profitability. Once banks learn the profitability of each customer each month, they are often profoundly shocked. They discovered, as one large bank did recently, that half of their customers are unprofitable. In addition, they found that bottom 20% of their customers are very unprofitable – often involving sizeable losses for the bank. To get a grip on this situation, banks have begun to segment their customers into profitability quintiles. In these segments, typically 100% or more of the bank’s profits come from the top 20% of the customers. The other 80% represent a little more than break even or a dead loss.
  • Managing customers based on their profitability segment. Once you know that a customer is losing you money every month, if you want the bank to prosper, something has to change. You can reprice the unprofitable customer’s products, try to change her costly habits, encourage her to buy additional profitable products, or find a way to gently ease her towards the door. Branch personnel are furnished with information about the profitability segment of each customer, and are trained to look for ways to change the situation. They also learn who the Gold customers are, and are trained to treat them well.
  • Computing lifetime value and potential lifetime profitability. It is not enough to know the current profitability of a customer. You need to be able to predict the future by making accurate forecasts of each customer’s potential lifetime profitability. This is especially critical for the “middle profitability” segments – those for which the bank breaks even or loses a small amount of money today. Half of those “marginal” customers have the potential to become profitable. The question is, which half? To forecast the future, you determine for each customer, the likelihood of your being able to sell them additional profitable products, the expected net revenue from usage of those products minus the promotional expense involved in the sale of the products. This forecast is added to current profitability to create a reasonably reliable lifetime profitability forecast which can drive bank marketing strategy and tactics.
This is the story of how Fleet Bank went through these steps, what they learned, and how they are going about providing their branch personnel with the information about their customers and the training necessary to improve customer profitability. The process was directed by Randall Grossman, Senior Vice President and Director of Customer Data Management and Analysis of the Fleet Financial Group,
Fleet had developed a bank-wide MCIF in the early 1990’s, maintained externally on a Harte-Hanks database. In January 1996, they took their first measure of customer value – by simply adding up all non-mortgage deposit and loan balances for each customer. By the end of 1996, they had created the software necessary to determine the Net Income after deduction of the Cost of Capital (NIACC) for all retail customers. For a typical customer, it looked something like this:
Check & Savings Home Equity Credit Card Mutual Funds Total
Net Interest $210 $248 -$280 $0 $178
Fees $18 $18 $396 $1,500 $1,932
Total Revenue $228 $266 $116 $1,500 $2,110
Amort. Sales Cost $20 $120 $67 $75 $282
Acc. Maint. $30 $75 $40 $900 $1,045
Transact. Cost $193 $21 $0 $30 $244
Allocated Overhead $0 $0 $0 $0 $0
Total Expenses $243 $216 $107 $1,005 $1,571
Loss Provision $0 $23 $160 $0 $183
Net Income -$15 $27 -$151 $495 $356
Taxes -$6 $11 -$60 $198 $143
NI After Taxes -$9 $16 -$91 $297 $213
Cost of Capital $1 $40 $32 $68 $150
NIACC -$10 -$33 -$123 $229 $63
Annual Revenue and Costs for typical bank customers having these products.
In this annual calculation for a typical customer, her net profitability is $63, due principally to the fact that she invested in mutual funds. Without this investment, she would have represented a loss of $166 to the bank.
By the end of 1997, Fleet had extended this system to its commercial customers. They were using industry benchmark costs for computing profitability. By the end of 1998, the bank had created an in-house data warehouse which enabled them to keep all of this data current, and to use actual Fleet activity based costs. The system looked something like this:
At their PC workstations using Windows NT and 95, power users, marketing and business analysts can access the data on each customer, can do analytical work and modeling, and develop marketing initiatives. The analysis server, for example, provides for statistical analysis, neural networks, ad hoc query and analysis, and geodemographic analysis. The data mart provides summarized, pre-formatted data for promotion design, tracking and analysis, and enables the users to do point & click, drill down analysis. The management reporting server provides on-demand parameterized reports.
Preliminary Lessons Learned
The new system enabled bank management, for the first time, to really understand their customer profitability, and to do something about it. The data showed that:
  • Half of all customers were unprofitable
  • 20% were very unprofitable
  • The balances that people maintained were only loosely correlated with profitability
  • Demographics were even more poorly correlated with profitability
  • Half of the new accounts being currently sold would never be profitable
  • The bank staff was working hard every day to retain customers who destroy customer value
Even though half of all customers were considered unprofitable, Grossman realized that Fleet could not simply walk away from half of their customer base. Further analysis showed Grossman that:
  • It was almost impossible profitably to cross sell the most profitable customers. Most of these sales cannibalized existing profitable products. For these top customers, marketing should focus on retention, not cross or up selling.
  • Some customer profits and losses were temporary, not permanent
  • Some low profit customers had great potential, sometimes because their assets were elsewhere, and they were, in fact, high profit customers at another bank
  • Some unprofitable customers could be nudged into profitability if they were offered the right products at the right prices
  • There were, however, many customers for whom there was very little potential for profit.
Faced with these sobering facts, the marketing staff decided to figure out ways to use the database that they had created to turn the situation around. The key to Grossman’s strategy was to develop three measures of customer value:
  • Lifetime Profitability
  • Potential Profitability
  • Potential Customer Value
Lifetime profitability is the net present value of the expected future stream of net income after the cost of capital, discounted at the corporate hurdle rate. It is calculated based on the current products that the customer is now using, including planned re-pricing. As calculated by Fleet, customer profitability differs from organizational profitability for several reasons:
  • Many customer’s business with the bank cuts across business lines. Organizational profitability is computed by adding up the profits from each line of business.
  • Some costs, such as overhead, are not allocated to each customer, since the methods for doing this involve arbitrary decisions which may distort the real profitability of the customer to the bank.
  • For those costs that are allocated to the customer, the allocation had, of necessity, to use standard cost factors (such as the cost of a branch visit, telephone call, or product sale) for which it was not cost effective to determine accurately based on each specific event.
  • Customer profitability does not “roll up”. If two customers share the same account, the bank gave full credit to both (which one is the decision maker?). Organizationally, of course, each account is only counted once, not twice.
Given these qualifications, lifetime customer profitability at Fleet is calculated for each bank customer and stored in the customer’s database record each month. Customers can then be ranked and segmented. It is possible to pick out the top customers, those just below the top, the average customers and the unprofitable customers. They can be flagged in the customer record so that marketers and branch personnel can recognize their value to the bank, and develop appropriate strategies and tactics.
Potential Profitability carries the analysis one step further. A typical customer has a limited number of bank products. There are usually many other bank products that the customer could be using. The probability of a given customer purchasing an additional product can be determined using CHAID analysis. For example, if a customer owns a home with a mortgage of $W, has a checking account with an average balance of $X , a savings account with an average balance of $Y, and a monthly credit card usage rate of $Z, an age of 44 years, two children in college, CHAID is used to predict the likelihood of him purchasing:
An auto loan 12%
A home equity loan 16%
A personal loan 12%
Mutual Funds 21%
A Certificate of Deposit 3%
CHAID can also predict the average balance that he will maintain on each of the possible additional products. Logistic regressions are then used to determine the expected Net Income after the Cost of Capital (NIACC) that Fleet will realize from the possible sale of each of the products to the customer. In each case, an estimate is made of the promotional expense involved in getting him to purchase the product. The potential profitability, then is calculated for each product as the:
Probability of purchase x Expected NIACC from usage – Promotional expense
The software then adds up each of the products for this customer with a positive NIACC to get the potential profit.
Potential Customer Value is then determined for each customer by adding together the lifetime profitability (with current products) and the potential profitability (from possible new products). This value is stored in every customer’s database record and used to select the most likely candidates for promotion for each product in direct mail promotions. It is also used to suggest the next best product when branch personnel are talking to the customer, or customer service has them on the line.
Mobilizing Branch Personnel
Knowing potential customer value and keeping it in a database is useless unless the data can be put to work by customer contact personnel. The central marketing staff can use the data:
  • For retention programs and preferred customer efforts
  • For product design analysis and decisions
  • For channel reconfiguration and service introductions
  • For product pricing
Beyond that, however, the bank seeks to mobilize its branch personnel to use the new customer data.
The bank has 1,200 branches, many of which have more than one officer that is interested and skilled at identifying targets for bank programs, and determining the appropriate tactics for each particular case. Here is where branch empowerment becomes important. Too many marketing staffs assume that they know what is best. Furthermore, most direct marketing professionals like to speak in terms of mailings of hundreds of thousands. They use statistical programs on computers to determine what should be done. Branch personnel, on the other hand, who know their customers, and see them once a week or more often, can identify those customers who they spot as being obvious candidates for particular products. They can dream up creative ways of suggesting these products to their customers. They are comfortable with programs that identify the ten or twenty best customers for action in a given week. By mobilizing their imaginations, and entrepreneurial skills, the bank can put their calculations of potential customer value to work.
Banks still have a long way to go in training their staffs to use potential lifetime profitability in their work. But, at Fleet, the data are now available. They are poised for a big boost in profitability by knowing their customers, and training and empowering their customer contact personnel to use that knowledge.
Arthur Middleton Hughes is Vice President of The Database Marketing Institute that does research and consulting for e-mail and database marketing companies. He would love to hear about your problems. Perhaps he could help.  He can be reached at or 954 767 4558. His new book Strategic Database Marketing 4th Edition is due out from McGraw-Hill in 2011.

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Arthur Middleton Hughes has published over 200 articles on Database and E-mail Marketing. Click Here to read them.

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