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Among the most useful fields that can be collected for future data mining are the initial purchase date, initial acquisition channel, offer responded to, initial product, initial credit score, time to respond, and geographic location. We have found these fields to be predictive a wide range of outcomes of interest such as expected duration of the relationship, bad debt, and additional purchases. These initial values should be maintained as is, rather than being overwritten with new values as the customer relationship develops.
Acquisition-Time Variables Can Predict Future Outcomes
By recording everything that was known about a customer at the time of acquisition and then tracking customers over time, businesses can use data mining to relate acquisition-time variables to future outcomes such as customer longevity, customer value, and default risk. This information can then be used to guide marketing efforts by focusing on the channels and messages that produce the best results. For example, the survival analysis techniques described in Chapter 12 can be used to establish the mean customer lifetime for each channel. It is not uncommon to discover that some channels yield customers that last twice as long as the customers from other channels. Assuming that a customer’s value per month can be estimated, this translates into an actual dollar figure for how much more valuable a typical channel A customer is than a typical channel B customer—a figure that is as valuable as the cost-per-response measures often used to rate channels.
Data Mining for Customer Relationship
Management
Customer relationship management naturally focuses on established customers. Happily, established customers are the richest source of data for mining. Best of all, the data generated by established customers reflects their actual individual behavior. Does the customer pay bills on time? Check or credit card? When was the last purchase? What product was purchased? How much did it cost? How many times has the customer called customer service?
How many times have we called the customer? What shipping method does the customer use most often? How many times has the customer returned a purchase? This kind of behavioral data can be used to evaluate customers’
potential value, assess the risk that they will end the relationship, assess the risk that they will stop paying their bills, and anticipate their future needs.
Matching Campaigns to Customers
The same response model scores that are used to optimize the budget for a mailing to prospects are even more useful with existing customers where they
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can be used to tailor the mix of marketing messages that a company directs to its existing customers. Marketing does not stop once customers have been acquired. There are cross-sell campaigns, up-sell campaigns, usage stimulation campaigns, loyalty programs, and so on. These campaigns can be thought of as competing for access to customers.
When each campaign is considered in isolation, and all customers are given response scores for every campaign, what typically happens is that a similar group of customers gets high scores for many of the campaigns. Some customers are just more responsive than others, a fact that is reflected in the model scores. This approach leads to poor customer relationship management. The high-scoring group is bombarded with messages and becomes irritated and unresponsive. Meanwhile, other customers never hear from the company and so are not encouraged to expand their relationships.
An alternative is to send a limited number of messages to each customer, using the scores to decide which messages are most appropriate for each one.
Even a customer with low scores for every offer has higher scores for some then others. In Mastering Data Mining (Wiley, 1999), we describe how this system has been used to personalize a banking Web site by highlighting the products and services most likely to be of interest to each customer based on their banking behavior.
Segmenting the Customer Base
Customer segmentation is a popular application of data mining with established customers. The purpose of segmentation is to tailor products, services, and marketing messages to each segment. Customer segments have traditionally been based on market research and demographics. There might be a
“young and single” segment or a “loyal entrenched segment.” The problem with segments based on market research is that it is hard to know how to apply them to all the customers who were not part of the survey. The problem with customer segments based on demographics is that not all “young and singles” or “empty nesters” actually have the tastes and product affinities ascribed to their segment. The data mining approach is to identify behavioral segments.
Finding Behavioral Segments
One way to find behavioral segments is to use the undirected clustering techniques described in Chapter 11. This method leads to clusters of similar customers but it may be hard to understand how these clusters relate to the business. In Chapter 2, there is an example of a bank successfully using automatic cluster detection to identify a segment of small business customers that were good prospects for home equity credit lines. However, that was only one of 14 clusters found and others did not have obvious marketing uses.
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More typically, a business would like to perform a segmentation that places every customer into some easily described segment. Often, these segments are built with respect to a marketing goal such as subscription renewal or high spending levels. Decision tree techniques described in Chapter 6 are ideal for this sort of segmentation.
Another common case is when there are preexisting segment definition that are based on customer behavior and the data mining challenge is to identify patterns in the data that correspond to the segments. A good example is the grouping of credit card customers into segments such as “high balance revolvers” or “high volume transactors.”
One very interesting application of data mining to the task of finding patterns corresponding to predefined customer segments is the system that AT&T
Long Distance uses to decide whether a phone is likely to be used for business purposes.
AT&T views anyone in the United States who has a phone and is not already a customer as a potential customer. For marketing purposes, they have long maintained a list of phone numbers called the Universe List. This is as complete as possible a list of U.S. phone numbers for both AT&T and non-AT&T
customers flagged as either business or residence. The original method of obtaining non-AT&T customers was to buy directories from local phone companies, and search for numbers that were not on the AT&T customer list. This TEAMFLY
was both costly and unreliable and likely to become more so as the companies supplying the directories competed more and more directly with AT&T. The original way of determining whether a number was a home or business was to call and ask.
In 1995, Corina Cortes and Daryl Pregibon, researchers at Bell Labs (then a part of AT&T) came up with a better way. AT&T, like other phone companies, collects call detail data on every call that traverses its network (they are legally mandated to keep this information for a certain period of time). Many of these calls are either made or received by noncustomers. The telephone numbers of non-customers appear in the call detail data when they dial AT&T 800 numbers and when they receive calls from AT&T customers. These records can be analyzed and scored for likelihood to be businesses based on a statistical model of businesslike behavior derived from data generated by known businesses. This score, which AT&T calls “bizocity,” is used to determine which services should be marketed to the prospects.
Every telephone number is scored every day. AT&T’s switches process several hundred million calls each day, representing about 65 million distinct phone numbers. Over the course of a month, they see over 300 million distinct phone numbers. Each of those numbers is given a small profile that includes the number of days since the number was last seen, the average daily minutes of use, the average time between appearances of the number on the network, and the bizocity score.
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The bizocity score is generated by a regression model that takes into account the length of calls made and received by the number, the time of day that calling peaks, and the proportion of calls the number makes to known businesses.
Each day’s new data adjusts the score. In practice, the score is a weighted average over time with the most recent data counting the most.
Bizocity can be combined with other information in order to address particular business segments. One segment of particular interest in the past is home businesses. These are often not recognized as businesses even by the local phone company that issued the number. A phone number with high bizocity that is at a residential address or one that has been flagged as residential by the local phone company is a good candidate for services aimed at people who work at home.