Mining Data
Data mining, the focus of this book, transforms data into actionable results.
Success is about making business sense of the data, not using particular algorithms or tools. Numerous pitfalls interfere with the ability to use the results of data mining:
■■ Bad data formats, such as not including the zip code in the customer address in the results
■■ Confusing data fields, such as a delivery date that means “planned delivery date” in one system and “actual delivery date” in another system
■■
Lack of functionality, such as a call-center application that does not allow annotations on a per-customer basis
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Legal ramifications, such as having to provide a legal reason when rejecting a loan (and “my neural network told me so” is not acceptable)
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Organizational factors, since some operational groups are reluctant to change their operations, particularly without incentives
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Lack of timeliness, since results that come too late may no longer be actionable
Data comes in many forms, in many formats, and from multiple systems, as shown in Figure 2.2. Identifying the right data sources and bringing them together are critical success factors. Every data mining project has data issues: inconsistent systems, table keys that don’t match across databases, records overwritten every few months, and so on. Complaints about data are the number one excuse for not doing anything. The real question is “What can be done with available data?” This is where the algorithms described later in this book come in.
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External sources of
demographic,
lifestyle, and credit
summarizations,
information
aggregations,
Σ views
Historical
Data whose
format and
content change
Transaction
over time
Data with
missing and
incomplete
fields
Data from multiple
competing sources
Data Mart
Marketing Summaries
Operational System
Figure 2.2 Data is never clean. It comes in many forms, from many sources both internal and external.
A wireless telecommunications company once wanted to put together a data mining group after they had already acquired a powerful server and a data mining software package. At this late stage, they contacted Data Miners to help them investigate data mining opportunities. In the process, we learned that a key factor for churn was overcalls: new customers making too many calls during their first month. Customers would learn about the excess usage when the first bill arrived, sometime during the middle of the second month.
By that time, the customers had run up more large bills and were even more unhappy. Unfortunately, the customer service group also had to wait for the same billing cycle to detect the excess usage. There was no lead time to be proactive.
However, the nascent data mining group had resources and had identified appropriate data feeds. With some relatively simple programming, it was
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possible to identify these customers within days of their first overcall. With this information, the customer service center could contact at-risk customers and move them onto appropriate billing plans even before the first bill went out. This simple system was a big win for data mining, simply because having a data mining group—with the skills, hardware, software, and access—was the enabling factor for putting together this triggering system.
Take Action
Taking action is the purpose of the virtuous cycle of data mining. As already mentioned, action can take many forms. Data mining makes business decisions more informed. Over time, we expect that better-informed decisions lead to better results.
Actions are usually going to be in line with what the business is doing anyway:
■■ Sending messages to customers and prospects via direct mail, email, telemarketing, and so on; with data mining, different messages may go to different people
■■ Prioritizing customer service
■■ Adjusting inventory levels
■■ And so on
The results of data mining need to feed into business processes that touch customers and affect the customer relationship.
Measuring Results
The importance of measuring results has already been highlighted. Despite its importance, it is the stage in the virtuous cycle most likely to be overlooked.
Even though the value of measurement and continuous improvement is widely acknowledged, it is usually given less attention than it deserves. How many business cases are implemented, with no one going back to see how well reality matched the plans? Individuals improve their own efforts by comparing and learning, by asking questions about why plans match or do not match what really happened, by being willing to learn that earlier assumptions were wrong. What works for individuals also works for organizations.
The time to start thinking about measurement is at the beginning when identifying the business problem. How can results be measured? A company that sends out coupons to encourage sales of their products will no doubt measure the coupon redemption rate. However, coupon-redeemers may have purchased the product anyway. Another appropriate measure is increased sales in
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particular stores or regions, increases that can be tied to the particular marketing effort. Such measurements may be difficult to make, because they require more detailed sales information. However, if the goal is to increase sales, there needs to be a way to measure this directly. Otherwise, marketing efforts may be all “sound and fury, signifying nothing.”
Standard reports, which may arrive months after interventions have occurred, contain summaries. Marketing managers may not have the technical skills to glean important findings from such reports, even if the information is there.
Understanding the impact on customer retention, means tracking old marketing efforts for even longer periods of time. Well-designed Online Analytic Processing (OLAP) applications, discussed in Chapter 15, can be a big help for marketing groups and marketing analysts. However, for some questions, the most detailed level is needed.
It is a good idea to think of every data mining effort as a small business case.
Comparing expectations to actual results makes it possible to recognize promising opportunities to exploit on the next round of the virtuous cycle. We are often too busy tackling the next problem to devote energy to measuring the success of current efforts. This is a mistake. Every data mining effort, whether successful or not, has lessons that can be applied to future efforts. The question is what to measure and how to approach the measurement so it provides the best input for future use.
As an example, let’s start with what to measure for a targeted acquisition campaign. The canonical measurement is the response rate: How many people targeted by the campaign actually responded? This leaves a lot of information lying on the table. For an acquisition effort, some examples of questions that have future value are:
■■ Did this campaign reach and bring in profitable customers?
■■ Were these customers retained as well as would be expected?
■■ What are the characteristics of the most loyal customers reached by this campaign? Demographic profiles of known customers can be applied to future prospective customers. In some circumstances, such profiles should be limited to those characteristics that can be provided by an external source so the results from the data mining analysis can be applied purchased lists.
■■ Do these customers purchase additional products? Can the different systems in an organization detect if one customer purchases multiple products?
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Did some messages or offers work better than others?
■■
Did customers reached by the campaign respond through alternate
channels?
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All of these measurements provide information for making more informed decisions in the future. Data mining is about connecting the past—through learning—to future actions.
One particular measurement is lifetime customer value. As its name implies, this is an estimate of the value of a customer during the entire course of his or her relationship. In some industries, quite complicated models have been developed to estimate lifetime customer value. Even without sophisticated models, shorter-term estimates, such as value after 1 month, 6 months, and 1 year, can prove to be quite useful. Customer value is discussed in more detail in Chapter 4.
Data Mining in the Context of the Virtuous Cycle
A typical large regional telephone company in the United States has millions of customers. It owns hundreds or thousands of switches located in central offices, which are typically in several states in multiple time zones. Each switch can handle thousands of calls simultaneously—including advanced features such as call waiting, conference calling, call-forwarding, voice mail, and digital services. Switches, among the most complex computing devices yet developed, are available from a handful of manufacturers. A typical telephone company has multiple versions of several switches from each of the TEAMFLY
vendors. Each of these switches provides volumes of data in its own format on every call and attempted call—volumes measured in tens of gigabytes each day. In addition, each state has its own regulations affecting the industry, not to mention federal laws and regulations that are subject to rather frequent changes. And, to add to the confusion, the company offers thousands of different billing plans to its customers, which range from occasional residential users to Fortune 100 corporations.