Tayko Software Reseller
Tayko Software is a software catalog firm that sells games and educational software. It started
out as a software manufacturer, and added third party titles to its offerings. It has recently put
together a revised collection of items in a new catalog, which it is preparing to roll out in a
mailing.
In addition to its own software titles, Tayko's customer list is a key asset. In an attempt to grow
its customer base, it has recently joined a consortium of catalog firms that specialize in computer
and software products.
The consortium affords members the opportunity to mail catalogs to names drawn from a pooled
list of customers. Members supply their own customer lists to the pool, and can "withdraw" an
equivalent number of names each quarter. Members are allowed to do predictive modeling on
the records in the pool so they can do a better job of selecting names from the pool.
Tayko has supplied its customer list of 200,000 names to the pool, which totals over 5,000,000
names, so it is now entitled to draw 200,000 names for a mailing. Tayko would like to select the
names that have the best chance of performing well, so it conducts a test – it draws 20,000 names
from the pool and does a test mailing of the new catalog to them.
This mailing yielded 1065 purchasers – a response rate of 0.05325. To optimize the performance
of the data mining techniques, it was decided to work with a stratified sample that contained
equal numbers of purchasers and non-purchasers. For ease of presentation, the data set for this
case includes just 1000 purchasers and 1000 non-purchasers, an apparent response rate of 0.5.
Therefore, after using the data set to predict who will be a purchaser, we must adjust the
purchase rate back down by multiplying each case’s "probability of purchase" by 0.05325/0.5 or
0.1065.
There are two response variables in this case: "purch" indicates whether or not a prospect
responded to the test mailing and purchased something, while "spend" indicates, for those who
made a purchase, how much they spent. The overall procedure in this case will be to develop
two models. One will be used to classify records as "purchase" or "no purchase." The other will
be used for those cases that are classified as "purchase," and will predict the amount they will
spend.
The following table provides a description of the variables available in this case. A partition
variable is used because we will be developing two different models in this case and we want to
preserve the same partition structure for assessing each model.
