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The day after any purchase, Harte-Hanks was able to identify the name and address of almost every person who had provided a phone number the day before. It was then able to print a targeted direct-mail certi cate and mail it so that it would arrive in the shopper s mailbox within 1 week of the shopper s visit to the store. The project started in November with the mailing of a holiday gift guide to all existing retail customers near each store. In certain key markets, there was some targeted prospecting as well. People who made a purchase based on the gift guide would receive a second standard postcard offer within 1 week. The Sports Authority says that approximately 5 percent of the shoppers who received the initial gift guide came to the stores and used the certi cates included in the guide. The follow-on mailing to anyone who responded to the rst holiday mailer had double-digit response rates, essentially tripling the industry norm for response rates and, most important, bringing those customers back to shop again before Christmas.
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Why did this program work Because of its recency and a solid offer. The customer who is most likely to respond to any promotion is the customer who has made a purchase most recently. The increased sales were so signi cant in comparison to the cost of the program that The Sports Authority made it a permanent part of its marketing program.
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Once you have captured the customer transaction data, you need to divide your customers into some sort of usable segments so that you can create a marketing program for each. How do you set up the segments This is a problem that separates the good marketers from the indifferent ones. There are dozens of ways to do this. For example: Senior citizens, college students, families with children, empty nesters High spenders, occasional shoppers, lapsed customers Pro tability segments RFM (recency, frequency, monetary analysis) segments SIC groups (for business-to-business marketing)
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The School of Continuing Studies at the University of Toronto is a selfsustaining academic division of the university that welcomes about 15,000 adult learners each year and offers courses in three program areas: business and professional studies, arts and humanities, and English as a second language. Recently, the school had to replace its existing registration system. This system had been in place for more than 12 years, and, although it could generate business reports and track registrations, the actual customer data were locked within its tables. The new system was Oracle-based. The school s director saw this as an opportunity for the
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school to take advantage of its customer data and employed Terri O Connor as the school s rst database marketer.
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Making Sense of the Data
Terri put the customer records from both the old and the new registration systems into a separate database. In order to make sense of the data, the records from the two systems were merged to create a unique customer record that provided a pro le of the customer. During this process, Terri used RFM to help summarize the customer data. The frequency indicator provided the school with its rst database surprise. The administrators had always known that they had a significant number of long-term customers, but they were very surprised to learn that some customers had purchased more than 50 courses in the previous 12 years. Terri termed these the high-value (HV) customers and started to track their retention rates and the types of courses they were purchasing. The breakdown is shown in Figure 8-2.
Recency and LTV
The school used several methods to distribute its course calendars. One of the most effective methods was to mail them directly to customers Figure 8-2 Currently Registered HV Customers by Start Date and Program Area
70 60 50 40 30 20 10 0 Writing Strategic leadership Science MNT Liberal studies Languages French ESL intensive Dispute resolution BPE DLP BPE
88 989 990 991 992 993 994 995 996 997 998 999 000 001 002 1 1 1 1 1 1 1 1 1 1 1 2 2 2
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