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“Lift” due to

Data Mining

Random

selection response

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% of Total Cases

Figure 1-5

Cumulative positive targets representing model “lift.”

DMWHIZZ takes a 1 percent sample of customers for a trial

campaign. Reviewing the data from the 10,000 customers they

received their expected 2 percent, or 200 responses. Using the data

mining model and a held-aside or test set of customers, we find that

the model can return 80 percent of the likely responders contacting

only 40 percent of the customers, or 70 percent of likely responders

contacting 30 percent of the customers, as illustrated in Figure 1-5.
6

Armed with this information, DMWHIZZ data miners build a

classification model using customer demographic and other data in

their customer and sales databases. The resulting model is used to

predict the likelihood of response for each of the remaining 990,000

customers. Now, let's add up the results.

The cost of the trial campaign was $15,000 (10,000

$1.50). Using

the data mining model, DMWHIZZ scores the 990,000 customers and

takes the top 40 percent as likely to respond (400,000). Recall that this

model provides 80 percent of the likely responders within that

40 percent. Since we expect 20,000 responses out of a million custom-

ers, we should get 80 percent of these, or 16,000—less the 200 we

already received in the trial, or 15,800. At an average profit of $50 per

6

Note that predicting a customer's response is one aspect of the solution. Another

aspect is often profitability. This scenario can be augmented to predict profit per

customer and multiply by the probability of response, thereby producing an

expected profit per customer. Customers can be ordered based on this expected

profit.

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