Tayko Software Cataloger Case Study - Business Management Assignment Help

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Background

Tayko is a software catalog firm that sells games and educational software. It started out as a software manufacturer and later 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 expand 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

The Mailing Experiment

Tayko has supplied its customer list of 200,000 names to the pool, which totals over 5 million 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 first conducts a test prior to withdrawing its full allotment of names – it draws 20,000 names from the pool and does a test mailing of the new catalog.

This mailing yielded 1,065 purchasers, a response rate of 0.05325, or 5.325%. Average spending was $102.56 per purchaser (i.e. only those who bought), or $5.46 per catalog mailed (i.e. all 20,000 names whether they purchased or not). 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 nonpurchasers. For ease of presentation, the dataset for this case includes just 1,000 purchasers and 1,000 nonpurchasers, an apparent response rate of 0.5, or 50%. Therefore, after using the dataset to predict who will be a purchaser, we must adjust the purchase rate back down by multiplying each case's “probability of purchase”

 

 

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