Cross Selling and predicting quantity sold?
- From: "anonymous_user@xxxxxxxxxxxxxxxxxxxxxxx" <anonymous_user@xxxxxxxxxxxxxxxxxxxxxxx>
- Date: Thu, 22 Jun 2006 19:20:07 GMT
I've done a lot of web research and read a book on analysis services but still haven't found an example of how to develop a cross selling algorithm that can predict not only what is sold, but how much is predicted to be sold.
For instance,
If I know customer A has bought 5 units of item 1, 10 units of item 2, and 20 units of item 3, I may know there is a 50% chance they will also want to but item 4.
Is there any way to determine how much of item 4 they might buy?
For instnace, say Item 4 is batteries and Item1 is a clock radio. We know that if they buy one clock radio they are likley to buy 2 batteries, but if they buy 2 clock radios they are likley to buy 4 batteries, not 2. In each case, there is the same likleyhood that they buy at least some batteries.
If my data set looks like this (in my sales table)
Customer / Item / Quantity
How do I design the data mining model to account for quantity? I tried several approaches to this and can't seem to get it to work correctly when taking quantity into account.
The method I used takes the sale table and makes it a case table and nested table, treats the Customer from the cast table as a key, and the item from the nested table as a key, input, and predict. This is how the books I've read explain how to create a cross selling model. I don't understand how I make it take quantity of each item into account.
Would it be better to do this in a 2nd mining model and not use it in the cross selling model?
Thanks
Tom
.
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