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Use the database to identify moms-to-be through their purchase of maternity apparel and other prebirth items. Be the rst to market before the baby is born, through databasetargeted direct-mail communications. Integrate with other departments as a cross-functional team to change merchandise assortments and the arrangement of merchandise in the stores so that the items purchased by mothersto-be are within easy reach. Actual shopping behavior plus multiple regressions were used to determine which customers really were mothers-to-be (as opposed to grandmothers or friends shopping for a baby shower). The data explored the cross shopping of each individual, her market basket, her known age, and her previous shopping history. Armed with this information about the moms-to-be, Kmart developed test groups that got the special promotions and control groups that did not. Comparison of the purchasing behavior of the two groups clearly showed the advantage of the special targeting.

Out-of-sample, the genetic models continued to be profitable, the nets were better than chance (although there was significant shrinkage in their performance due to curve-fitting), and the breakouts deteriorated to chance (optimization cannot be a factor in this case)..

The program increased store sales by over 15 percent while reducing mailing costs by 35 percent. In Hispanic markets, store sales of baby products increased by over 25 percent, while sales in adjacent highly cross-shopped departments increased by 35 percent over the prior year. On a customer level, over 21 percent of the top-decile moms-to-be shopped at Kmart during the mail event, increasing their spending more than 40 percent over the prior year. Targeted customers outperformed the control groups by 4 to 1 and spent over 20 percent more per visit during the promotional event. As a result of this experiment, the use of the database to drive promotions was expanded into many other departments at Kmart. According to Tom Lemke, vice president of database marketing at Kmart, Our mission is to Mail Less and Sell More. To accomplish this, we use the intelligence of customer data to create improved response rates and lowered mail volumes.

The next best performers in-sample were the support/resistance moving average models ($300 loss per trade) and the seasonality models ($671 loss per trade) Further down the ladder of best-to-worst performers were the lunar and solar models, which lost $1,076 and $1,067, respectively Losses in the $1,300 to $1,700 range were observed for the moving average models The oscillator and cycle models exhibited losses of over $2,000 per trade; when taken as a whole, these models were no better than chance It is interesting that the genetic model and the small neural network models were the ones that held up out of sample Such models offer great opportunities for curve-fitting and tend to fail in out-of-sample tests and real trading Seasonality, which is only rarely the topic of articles, also exhibited potential On the other hand, the most popular methods (eg.

Everyone who tries to create customer segments runs into the problem of numbers. By using recency, frequency, monetary (RFM) analysis, it is possible to come up with hundreds of different RFM cells. By using SIC codes, you can create hundreds of types of customers that you can market to. By using pro tability, you can subdivide your customers into deciles, for example, from the most pro table to the least pro table. How many segments make up the right number To get the answer, you need to consider the imagination and creative genius available in your marketing organization. Every new marketing idea requires time and resources for its execution. Few organizations can develop and support as many as ve or six simultaneous marketing efforts. Therefore, the sector marketing strategy must end up creating and marketing to just a few sectors.

, moving average, oscillator, and cycle models) performed the worst, trading badly both in- and out-of-sample The breakout models are noteworthy in the sense that, taken as a group, they worked well in the past; but, due to increased market efficiency, have since deteriorated to the point where they currently perform no better than chance Table C-l contains the annualized return-on-account (the first line of each modelorder combination) and average dollars-per-trade (the second line of each model-order combination) data for all the tests (all model and order combinations) conducted for entry models using the standard exit strategy The data presented arc for portfolio performance as a whole The model descriptions (leftmost column) are the same as used elsewhere in this book The last six lines of the table contain data that can be used as a baseline against which the various entry models can be compared.