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Dec 17, 2012

BlackLotus and Product Assortment

It is reported in TechCrunch today that Home Depot acquired startup BlackLotus. BlackLotus applies data mining to two major problems facing retail stores: Pricing and Product Assortment. The Pricing solution got a lot of mention in this article. I want dive deeper on the second solution provided by BlackLotus: Product assortment.

Since the early days of data mining (which started in 1993, with the publication of Rakesh Agarsal's seminal paper on frequent pattern mining), researchers has been paying attention to product selection problem. The famous "diaper and beer" story illustrates how frequent pattern mining discovers young fathers who shop for diaper also shop for beer on Friday nights. The purchase pattern of users on an assortment of products give retailers hint on what to display together. It also gives retailers a tool to decide what to replenish when products are sold out.

Each retailer faces the problem of limited shelf space. Take Home Depot as an example. If they decide to replenish oriental rug, but find that this kind of rug is bought together with long dinning table. Then they should consider increasing the stock of those long dinning tables. Given the limited shelf space, they may have to decrease the stock of contemporary rugs if that rug does not sell well. Thus the decision has to be made between two product assortments: {oriental rug, long dinning table} and {contemporary rug}. This decision considers the frequency of purchase of each assortment, and the profit each sale brings.

The data mining approach to Frequent Pattern Mining gives us a fast and efficient way to calculate frequency of any product. The transaction table is recorded as in Table 1, where each transaction correspond to each row, and the columns are the products. One person can have multiple rows if that person comes back to shop again. In this table, we assume that are total 9 million purchases (in a month) in Home Depot.
Transaction ID
Oriental Rug
Contemporary
Rug
Dinning table
  1
1
0
  ...
1
  2
0
1
  ...
0
  3
1
0
  ...
0
  ...
 ...
  ...
  ...

  9,000,000
1
0
  ...
1
                             
                                    Table 1. Transaction table

The data mining field has invented 2 major algorithms to calculate the purchase frequency of any product set, or itemset (a set of product items): Apriori and FP-tree (Frequent-pattern tree). The first is a breadth first search method, and the second is essentially a depth-first search.

We will talk about the details of these algorithms in the future if time allows. 

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