The Virtuous Cycle of Data Mining

In the first part of the nineteenth century, textile mills were the industrial success stories. These mills sprang up in the growing towns and cities along rivers in England and New England to harness hydropower. Water, running over water wheels, drove spinning, knitting, and weaving machines. For a century, the symbol of the industrial revolution was water driving textile machines.

The business world has changed. Old mill towns are now quaint historical curiosities. Long mill buildings alongside rivers are warehouses, shopping malls, artist studios and computer companies. Even manufacturing companies often provide more value in services than in goods. We were struck by an ad campaign by a leading international cement manufacturer, Cemex, that presented concrete as a service. Instead of focusing on the quality of cement, its price, or availability, the ad pictured a bridge over a river and sold the idea that cement is a service that connects people by building bridges between them. Concrete as a service? A very modern idea.

Access to electrical or mechanical power is no longer the criterion for success. For mass-market products, data about customer interactions is the new waterpower; knowledge drives the turbines of the service economy and, since the line between service and manufacturing is getting blurry, much of the manufacturing economy as well. Information from data focuses marketing efforts by segmenting customers, improves product designs by addressing real customer needs, and improves allocation of resources by understanding and predicting customer preferences.

Data is at the heart of most companies core business processes. It is generated by transactions in operational systems regardless of industry-retail, telecommunications, manufacturing, utilities, transportation, insurance, credit cards, and banking, for example. Adding to the deluge of internal data are external sources of demographic, lifestyle, and credit information on retail customers, and credit, financial, and marketing information on business customers. The promise of data mining is to find the interesting patterns lurking in all these billions and trillions of bytes. Merely finding patterns is not enough. You must respond to the patterns and act on them, ultimately turning data into information, information into action, and action into value. This is the virtuous cycle of data mining in a nutshell.

To achieve this promise, data mining needs to become an essential business process, incorporated into other processes including marketing, sales, customer support, product design, and inventory control. The virtuous cycle places data mining in the larger context of business, shifting the focus away from the discovery mechanism to the actions based on the discoveries. Throughout this chapter and this book, we will be talking about actionable results from data mining (and this usage of actionable should not be confused with its definition in the legal domain, where it means that some action has grounds for legal action). /? \(v

Marketing literature makes data mining seem so easy. Just apply the automated algorithms created by the best minds in academia, such as neural networks, decision trees, and genetic algorithms, and you are on your way to untold successes. Although algorithms are important, the data mining solution is more than just a set of powerful techniques and data structures. The techniques have to be applied in the right areas, on the right data. The virtuous cycle of data mining is an iterative learning process that builds on results over time. Success in using data will transform an organization from reactive to proactive. This is the virtuous cycle of data mining, used by the authors for extracting maximum benefit from the techniques described later in the book.

This chapter opens with a brief case history describing an actual example of the application of data mining techniques to a real business problem. The case study is used to introduce the virtuous cycle of data mining. Data mining is presented as an ongoing activity within the business with the results of one data mining project becoming inputs to the next. Each project goes through four major stages, which together form one trip around the virtuous cycle. Once these stages have been introduced, they are illustrated with additional case studies.

A Case Study in Business Data Mining

Once upon a time, there was a bank that had a business problem. One particular line of business, home equity lines of credit, was failing to attract good customers. There are several ways that a bank can attack this problem.

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The bank could, for instance, lower interest rates on home equity loans. This would bring in more customers and increase market share at the expense of lowered margins. Existing customers might switch to the lower rates, further depressing margins. Even worse, assuming that the initial rates were reasonably competitive, lowering the rates might bring in the worst customers-the disloyal. Competitors can easily lure them away with slightly better terms. The sidebar Making Money or Losing Money talks about the problems of retaining loyal customers.

In this example, Bank of America was anxious to expand its portfolio of home equity loans after several direct mail campaigns yielded disappointing results. The National Consumer Assets Group (NCAG) decided to use data mining to attack the problem, providing a good introduction to the virtuous cycle of data mining. (We would like to thank Larry Scroggins for allowing us to use material from a Bank of America Case Study he wrote. We also benefited from conversations with Bob Flynn, Lounette Dyer, and Jerry Modes, who at the time worked for Hyperparallel.)

Identifying the Business Challenge

BofA needed to do a better job of marketing home equity loans to customers. Using common sense and business consultants, they came up with these insights:

People with college-age children want to borrow against their home equity to pay tuition bills.

People with high but variable incomes want to use home equity to smooth out the peaks and valleys in their income.

MAKING MONEY OR LOSING MONEY?

Home equity loans generate revenue for banks from interest payments on the loans, but sometimes companies grapple with services that lose money. As an example, Fidelity Investments once put its bill-paying service on the chopping block because this service consistently lost money. Some last-minute analysis saved it, though, by showing that Fidelitys most loyal and most profitable customers used the bill paying service; although the bill paying service lost money, Fidelity made much more money on these customers other accounts. After all, customers that trust their financial institution to pay their bills have a very high level of trust in that institution.

Cutting such value-added services may inadvertently exacerbate the profitability problem by causing the best customers to look elsewhere for better service.