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The business objective of company DMWhizz is to increase the
response rate for its latest campaign for a new product, Gizmos .
DMWhizz has run many such campaigns, so from an operational
standpoint, there is little risk associated with this project. However,
this is the first time DMWhizz is employing data mining to try
to increase the response rate over previous efforts. Historically,
DMWhizz has obtained a 3 percent response rate from such cam-
paigns, which they viewed as better than the norm in the retail
industry. DMWhizz will be satisfied with anything over a 4 percent
response rate, a 33 percent increase. They typically send a campaign
to 400,000 of their customers, chosen at random. As such, they get
about 12,000 responses. With the introduction of data mining,
DMWhizz expects at least 16,000 responses if sent to 400,000
customers, a 33 percent increase.
The business has a base of 1 million existing customers to poten-
tially send an offer to. Although Gizmos is a new product, it is
related to several other less-featured products, so DMWhizz can use
historical sales information of customers who have bought these
other products.
DMWhizz knows that this data mining solution requires known
outcomes to build predictive models, in this case, which customers
actually purchased Gizmos. As such they factor into their plan
conducting a small-scale, trial campaign to collect data on which
customers actually purchased Gizmos. From this trial campaign,
data mining models can be built to predict which of the remaining
customers and prospects are likely to purchase Gizmos.
Specifically, DMWhizz takes a 2 percent random sample of the 1
million potential customers for Gizmos, totaling 20,000. They mail
the offer to these customers and record which customers purchased
the item. Based on previous campaigns, they expect a 3 percent
response rate, or 600 customers purchasing Gizmos. This data, with
known outcomes both positive and negative, serves as the basis for
the modeling process.
Technically speaking, this is a classification problem in data
mining. DMWhizz is as yet unfamiliar with the quality and charac-
ter of the data; they expect to try several types of classification
algorithms and to select the one that provides the best lift . Lift
essentially indicates how well the model performs at predicting a
particular outcome instead of randomly selecting cases, in this
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