Cutting such value-added services may inadvertently exacerbate the profitability problem by causing the best customers to look elsewhere for better service.
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Marketing literature for the home equity line product reflected this view of the likely customer, as did the lists drawn up for telemarketing. These insights led to the disappointing results mentioned earlier.
Applying Data Mining
BofA worked with data mining consultants from Hyperparallel (then a data mining tool vendor that has since been absorbed into Yahoo!) to bring a range of data mining techniques to bear on the problem. There was no shortage of data. For many years, BofA had been storing data on its millions of retail customers in a large relational database on a powerful parallel computer from NCR/Teradata. Data from 42 systems of record was cleansed, transformed, aligned, and then fed into the corporate data warehouse. With this system, BofA could see all the relationships each customer maintained with the bank.
This historical database was truly worthy of the name—some records dating back to 1914! More recent customer records had about 250 fields, including demographic fields such as income, number of children, and type of home, as well as internal data. These customer attributes were combined into a customer signature, which was then analyzed using Hyperparallel’s data mining tools.
A decision tree derived rules to classify existing bank customers as likely or unlikely to respond to a home equity loan offer. The decision tree, trained on thousands of examples of customers who had obtained the product and thousands who had not, eventually learned rules to tell the difference between them. Once the rules were discovered, the resulting model was used to add yet another attribute to each prospect’s record. This attribute, the “good prospect”
flag, was generated by a data mining model.
Next, a sequential pattern-finding tool was used to determine when customers were most likely to want a loan of this type. The goal of this analysis was to discover a sequence of events that had frequently preceded successful solicitations in the past.
Finally, a clustering tool was used to automatically segment the customers into groups with similar attributes. At one point, the tool found 14 clusters of customers, many of which did not seem particularly interesting. One cluster, however, was very interesting indeed. This cluster had two intriguing properties:
■■ 39 percent of the people in the cluster had both business and personal accounts.
■■ This cluster accounted for over a quarter of the customers who had been classified by the decision tree as likely responders to a home equity loan offer.
This data suggested to inquisitive data miners that people might be using home equity loans to start businesses.
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Acting on the Results
With this new understanding, NCAG teamed with the Retail Banking Division and did what banks do in such circumstances: they sponsored market research to talk to customers. Now, the bank had one more question to ask: “Will the proceeds of the loan be used to start a business?” The results from the market research confirmed the suspicions aroused by data mining, so NCAG changed the message and targeting on their marketing of home equity loans.
Incidentally, market research and data mining are often used for similar ends—to gain a better understanding of customers. Although powerful, market research has some shortcomings:
■■ Responders may not be representative of the population as a whole.
That is, the set of responders may be biased, particularly by where past marketing efforts were focused, and hence form what is called an opportunistic sample.
■■ Customers (particularly dissatisfied customers and former customers) have little reason to be helpful or honest.
■■ For any given action, there may be an accumulation of reasons. For instance, banking customers may leave because a branch closed, the bank bounced a check, and they had to wait too long at ATMs. Market research may pick up only the proximate cause, although the sequence is more significant.
Despite these shortcomings, talking to customers and former customers provides insights that cannot be provided in any other way. This example with BofA shows that the two methods are compatible.
T I P When doing market research on existing customers, it is a good idea to use data mining to take into account what is already known about them.
Measuring the Effects
As a result of the new campaign, Bank of America saw the response rate for home equity campaigns jump from 0.7 percent to 7 percent. According to Dave McDonald, vice president of the group, the strategic implications of data mining are nothing short of the transformation of the retail side of the bank from a mass-marketing institution to a learning institution. “We want to get to the point where we are constantly executing marketing programs—not just quarterly mailings, but programs on a consistent basis.” He has a vision of a closed-loop marketing process where operational data feeds a rapid analysis process that leads to program creation for execution and testing, which in turn generates additional data to rejuvenate the process. In short, the virtuous cycle of data mining.
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What Is the Virtuous Cycle?
The BofA example shows the virtuous cycle of data mining in practice. Figure 2.1
shows the four stages:
1. Identifying the business problem.
2. Mining data to transform the data into actionable information.
3. Acting on the information.
4 . Measuring the results.
Transform data
into actionable information
using data mining techniques.
Identify
business opportunities
Act
where analyzing data
on the information.
can provide value.
1
2
3
4
5
6
7
8
9
10
Measure the results
of the efforts to complete
the learning cycle.
Figure 2.1 The virtuous cycle of data mining focuses on business results, rather than just exploiting advanced techniques.
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As these steps suggest, the key to success is incorporating data mining into business processes and being able to foster lines of communication between the technical data miners and the business users of the results.
Identify the Business Opportunity
The virtuous cycle of data mining starts with identifying the right business opportunities. Unfortunately, there are too many good statisticians and competent analysts whose work is essentially wasted because they are solving problems that don’t help the business. Good data miners want to avoid this situation.
Avoiding wasted analytic effort starts with a willingness to act on the results. Many normal business processes are good candidates for data mining:
■■
Planning for a new product introduction
■■
Planning direct marketing campaigns
■■
Understanding customer attrition/churn
■■
Evaluating results of a marketing test
These are examples of where data mining can enhance existing business efforts, by allowing business managers to make more informed decisions—by targeting a different group, by changing messaging, and so on.
To avoid wasting analytic effort, it is also important to measure the impact of whatever actions are taken in order to judge the value of the data mining effort itself. If we cannot measure the results of mining the data, then we cannot learn from the effort and there is no virtuous cycle.
Measurements of past efforts and ad hoc questions about the business also suggest data mining opportunities:
■■
What types of customers responded to the last campaign?
■■
Where do the best customers live?
■■
Are long waits at automated tellers a cause of customers’ attrition?
■■
Do profitable customers use customer support?
■■
What products should be promoted with Clorox bleach?
Interviewing business experts is another good way to get started. Because people on the business side may not be familiar with data mining, they may not understand how to act on the results. By explaining the value of data mining to an organization, such interviews provide a forum for two-way communication.
We once participated in a series of interviews at a telecommunications company to discuss the value of analyzing call detail records (records of completed calls made by each customer). During one interview, the participants were slow in understanding how this could be useful. Then, a colleague pointed out
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that lurking inside their data was information on which customers used fax machines at home (the details of this are discussed in Chapter 10 on Link Analysis). Click! Fax machine usage would be a good indicator of who was working from home. And to make use of that information, there was a specific product bundle for the work-at-home crowd. Without our prodding, this marketing group would never have considered searching through data to find this information. Joining the technical and the business highlighted a very valuable opportunity.
T I P When talking to business users about data mining opportunities, make sure they focus on the business problems and not technology and algorithms.
Let the technical experts focus on the technology and the business experts focus on the business.