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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.

Overall, the S&P 500 data series appears to be squeaky-clean This was expected: In our experience, Pinnacle Data Corporation (the source of the data) supplies data of very high quality As an example of how bad data quality can get, and the kinds of errors that can be expected when dealing with low-quality data, another data set was analyzed with the same data-checking utility This data, obtained from an acquaintance, was for Apple Computer (AAPL) The data-checking results appear in Table l-2 In this data set, unlike in the previous one, 2 bars were flagged for having outright logical inconsistencies One logically invalid data point had an opening price of zero, which was also lower than the low, while the other bar had a high price that was lower than the closing price.

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)

Another data point was detected as having an excessive range, which may or may not be a data error, In addition, several bars evidenced extreme closing price deviance, perhaps reflecting uncorrected stock splits There were no duplicate or out-of-order dates, but quite a few data points were missing In this instance, the missing data points were holidays and, therefore, only reflect differences in data handling: for a variety of reasons, we usually fill holidays with data from previous bars Considering that the data series extended only from l/2/97 through 1 l/6/98 (in contrast to the S&P 500, which ran from l/3/83 to 5/21/98), it is distressing that several serious errors, including logical violations, were detected by a rather simple scan The implication of this exercise is that data should be purchased only from a.

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.

reputable vendor who takes data quality seriously; this will save time and ensure reliable, error-free data for system development, testing, and trading, In addition, all data should be scanned for errors to avoid disturbing surprises. For an in-depth discussion of data quality, which includes coverage of how data is produced, transmitted, received, and stored, see Jurik (1999).

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.

Today there are a great many sowces from which data may be acquired Data may be purchased from value-added vendors, downloaded from any of several exchanges, and extracted from a wide variety of databases accessible over the Internet and on compact discs Value-added vendors, such as Tick Data and Pinnacle, whose data have been used extensively in this work, can supply the trader with relatively clean data in easy-to-use form They also provide convenient update services and, at least in the case of Pinnacle, error corrections that are handled automatically by the downloading software, which makes the task of maintaining a reliable, up-to-date database very straightforward Popular suppliers of end-of-day commodities data include Pinnacle Data Corporation (800-724-4903), Prophet Financial Systems (650-322-4183) Commodities Systems Incorporated (CSI, 8002744727), and Technical Tools (800-231-8005).