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Using Data Warehouses To Calculate Customer Profitability
Aug 13, 2004
Mark Rittman

One of the first data warehousing projects I worked on was for a bank that wanted to calculate customer profitability. We had millions of customers, with a range of different account usage patterns, balances and accounts held, other products bought through our life and pensions arms, and length of time with the bank. Our project built a data warehouse that brought all the customer and account information together, and calculated the annual profit (or loss) the bank made from each customer.

The point of all this was to identify those customers who cost the bank money, or made very little profit contribution, and to either make them profitable, or to encourage them to use lower cost channels (ATMs). In addition, those customers who made a high contribution to profit (and in fact a small proportion of customers made a disproportionate contribution to profit) were then actively focused on, given their own personal account managers, and identified as customers we had to keep. Building a customer profitability model allowed us to segment our customers into distinct groups, encourage the lower contributing ones to either incur less costs for us or move up in to a higher contributing segment, and letting us focus more attention on those customers who made the most money for us.

On this theme, (via Slashdot) are running an article titled "The customer is always right? Not anymore" which looks at how BestBuy is handling customer profitability. According to the introduction:

"So much for the customer always being right.

Some retailers are deciding that the customer can be very, very wrong -- as in unprofitable. And some, including Best Buy Co. Inc., are discriminating between profitable customers and shoppers they lose money on.

Like a customer who ties up a salesworker but never buys anything, or who buys only during big sales. Or one who files for a rebate, then returns the item.

"That would be directly equivalent to somebody going to an ATM and getting money out without putting any in," Brad Anderson, Best Buy's chief executive, said in a recent interview. "Those customers, they're smart, and they're costing us money."

Anderson said Best Buy was tightening its rebate policies in the case of customers who abuse the privilege, but declined to say what else his company was doing to discourage its most costly customers.

"What we're trying to do is not eliminate those customers, but just diminish the number of offers we make to them," Anderson said."

Interestingly, BestBuy don't ever 'fire' these customers, instead aiming to make them profitable instead. The article does go into examples though where 'firing' customers does happen, with one consultant for another company quoted as saying:

""Then there are those customers that are just evil customers ... fundamentally they're out to cheat us," Selden said in a telephone interview. "It's not a large number of customers, but they can have a material impact on a business."

Take a look if you get a chance. It's a good article that illustrates one of the real-world applications for data warehousing and data mining, and one that applies to banking as well as retailing.




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