In the case of retailers with stores, market basket information enables the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept).
The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases.
The challenge has been how to leverage this data to produce business value. Most have already figured out a way to consolidate and aggregate their data to understand the basics of the business: what are they selling, how many units are moving and the sales amount. However, few have ventured far enough to analyze the information at its lowest level of granularity: the market basket transaction.
At this level of detail, the information is very useful as it provides the business users with direct visibility into the market basket of each of the customers who shopped at their store. The data becomes a window into the events as they happened, understanding not only the quantity of the items that were purchased in that particular basket, but how these items were bought in conjunction with each other. In turn, this capability enables advanced analytics such as:
- Item affinity: Defines the likelihood of two (or more) items being purchased together.
- Identification of driver items: Enables the identification of the items that drive people to us that always need to be in stock.
- Trip classification: Analyzes the content of the basket and classifies the shopping trip into a category: weekly grocery trip, special occasion, etc.
- Store-to-store comparison: Understanding the number of baskets allows any metric to be divided by the total number of baskets, effectively creating a convenient and easy way to compare stores with different characteristics (units sold per customer, revenue per transaction, number of items per basket, etc.).