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Until 10 years ago, few companies used their databases to compute customer lifetime value. Banks began the process. They were able to determine the pro tability of every account on a monthly basis, and to roll that up to compute household pro tability. The results were used to segment customers in order to learn which ones were useful to the bank and which ones were reducing the bank s pro ts. I like to use the chart shown in Figure 1-1. It comes from a major bank in the South that ranked all its customers by pro tability. It shows that 80 percent of the bank s pro ts came from the top 5 percent of its customers. The bottom 28 percent lost 22 percent of its pro ts. This is very powerful information that was just not available before database marketing came along. What was even more powerful was what the bank was able to do with this information. Clearly the top 5 percent of customers had to be retained. The bank s whole future rested on these people. In addition, something had to change in the bank s marketing strategy for the bottom 28 percent. Why try to retain people who are costing the bank money Not just banks, but insurance companies, business-to-business marketers, and scores of other enterprises began to compute numbers like these and to develop strategies that made pro table use of them. Many companies went on to determine potential lifetime value by using computer models to determine all the other products that each customer Figure 1-1
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80.00% 60.00% 40.00% 20.00% 0.00% 20.00% 40.00% 5% 11% 28% 28% 28% 21.83% 24.82% 15.83% 1.52%
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was likely to buy from the company in the future. Each such product had three attributes: The potential pro tability to the enterprise if the customer were to buy the product (based on the customer s assets and income) The probability that the customer would buy the product if it were offered (based on the purchases of other customers with similar lifestyles) The cost to the company of selling the customer the additional product From this analysis came the determination of the next best product for each customer (in terms of pro t to the enterprise and likelihood of response). This product was identi ed in every customer s database record and shown on the screens used by all personnel who had customer contact. This has proved to be a highly successful and signi cant use of database marketing.
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The most important number in any lifetime value table is the retention rate. This is the percentage of newly acquired customers in each segment who will still be buying from your company 1 year later. Before database marketing, few companies knew what this number was or what to do about it. I worked with an insurance company whose retention rate in the rst year was only 54 percent. During this year, the company made absolutely no effort to make contact with its customers or to build a relationship with them. Lifetime value computation showed that an increase of 5 percent in this rst-year retention rate would increase pro ts by $14 million (see Table 1-1). The cost of getting this additional 5 percent increase was estimated at $36 per customer, or a total of $3 million, for customer communications. The beauty of using a database to do lifetime value analysis is that you focus on things that you can change by marketing strategy and that you can prove the value of each strategy in nite dollar terms.
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Table 1-1 Effect of 5 Percent Increase in the Retention Rate
New customer Retention rate Customers Average revenue Total revenue Acquisition/retention Commission Total Total cost Net revenue per customer Net revenue in total 130,000 $2,900 $377,000,000 $192 $959 $1,151 $149,630,000 $1,749 $227,370,000 Second year 54% 70,200 $2,900 $203,580,000 $ $769 $769 $99,970,000 $1,476 $103,610,000 Second year 59% 76,700 $2,900 $222,430,000 $36 $769 $805 $104,650,000 $1,536 $117,780,000
Using database marketing, the company would not have to commit the $3 million in the hope of getting this increased return. It could use the database to set up test and control groups to see if the communications would produce the desired results. It could select 5000 customers to receive the communications (at a cost of $180,000) to see if their retention rate could be increased by 5 percent. If the strategy worked, then the next year the company could apply the successful methods to all new acquisitions, spend the whole $3 million, and pocket the $14 million. (Table 1-1 and all the other tables in this book can be downloaded free from www.dbmarketing.com.)
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