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Modeling for Churn
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As the economy has grown, more companies have learned to use database marketing to retain customers. Competition in some products, particularly credit cards, cellular phones, long-distance service, and health care, has become intense. The attrition rate, or the rate at which people switch to another provider, is very high. At the same time, statistical modelers have found that regression analysis can help in predicting which customers are most likely to drop the service. The models use a combination of appended data and transaction history to pick out the variables that precede a customer s dropping the service. Then advanced database marketing techniques are used to concentrate on these potential defectors and
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T h e C u s t o m e r L o ya lt y S o l u t i o n
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Table 1-2 Risk/Revenue Matrix
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Probability of leaving soon LTV High Medium Low High priority A priority B priority C Medium priority B priority B priority C Low priority C priority C priority C
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Table 1-3 Price Offers
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Number tested 1000 1000 1000 Number renewing 800 900 950 Price offer $10 $ 9 $ 8 Revenue $8000 $8100 $7600
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get them to change their minds. Defection prediction plus lifetime value analysis further concentrates the communications on those whom it is most pro table to serve. The result is risk/revenue analysis (see Table 1-2). In this analysis, which is discussed later in this book, a concentration on those customers who are priority A or priority B reduces the task of lowering churn by 56 percent, saving resources and boosting pro ts. Bruce McDoniel of Summit Marketing used this system with a large regional bank to nd the right price for priority A customer renewals. The goal was to get customers to renew, but also to maximize revenue. Bruce tried three price offers that netted the bank from $8 to $10. Table 1-3 shows the result of the test. This simple test enabled the bank to x the offer price at $9.
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Use of Database Marketing for Acquisition
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Database marketing is also highly useful for acquiring new customers. The first step is to apply lifetime value analysis to existing customers to determine the characteristics of the most pro table and the least pro table customers. This analysis can be used to steer the sales force toward recruiting the right sort of people in the rst place. Frederick Reichheld, in his path-breaking book The Loyalty Effect, showed us that some people are inherently loyal, whereas others tend to be disloyal. If companies
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H o w D ata b a s e M a r k e t i n g W o r k s
concentrate on recruiting loyal customers to begin with, they will have a much easier time retaining their customers once they are acquired. In business-to-business customer acquisition, customers can be scored using Dun & Bradstreet data, such as Standard Industrial Classi cation (SIC) codes, annual sales, number of employees, and other such data. By using this type of appended data, enterprises can learn which types of companies are most likely to buy which of their products. Their acquisitions can be accurately targeted. In consumer acquisition, cluster coding has proved useful in many cases. Cluster coding systems, marketed by Claritas and other providers, segment the U.S. population into 62 different clusters based on lifestyle, with catchy names like Shotguns & Pickups, Money & Brains, Furs & Station Wagons, Pools & Patios, and Hard Scrabble. If you apply these clusters to your existing customer base, you may discover that your services appeal primarily to 30 percent or so of the 62 possible clusters. Marketing to the remaining 70 percent may be a losing proposition. By concentrating your acquisition resources on the prospects that are most likely to buy, you can increase your success and your pro ts. Cluster coding works for only a few products and services, but where it works, it is well worth the money.
Two Kinds of Databases
There are really two different kinds of databases in any company that is engaged in direct marketing of products and services. One is an operational database, and the other is a marketing database (see Figure 1-2). An operational database is used to process transactions and get out the monthly statements. For a cataloger, the operational database is used to process the orders, charge the credit cards, arrange shipment, and handle returns and credits. For a bank, the operational database processes checks and deposits, maintains balances, and creates the monthly statements. For a telephone company, the operational database keeps track of the telephone calls made and arranges the billing for them.
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