In the retail and restaurant businesses, market basket analysis (MBA) is a set of statistical affinity calculations that help managers better understand – and ultimately serve – their customers by highlighting purchasing patterns. In simplest terms, MBA shows what combinations of products most frequently occur together in orders. These relationships can be used to increase profitability through cross-selling, recommendations, promotions, or even the placement of items on a menu or in a store.
The approach is based on the theory that customers who buy a certain item (or group of items) are more likely to buy another specific item (or group of items). Association Rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.
An example of Association Rules
- Assume there are 100 customers
- 10 of them bought milk, 8 bought butter and 6 bought both of them.
- bought milk => bought butter
- support = P(Milk & Butter) = 6/100 = 0.06
- confidence = support/P(Butter) = 0.06/0.08 = 0.75
- lift = confidence/P(Milk) = 0.75/0.10 = 7.5
Note: this example is extremely small. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.
The correlation becomes more valuable if it is shown that there are other products to be stronger than that between milk and butter.
MBA can be used to recommend a purchase based on the absence of a common pairing, such as when a customer orders only one item. They might be more likely to buy something else or a second item than someone who bought a large item. Staff trained to recognize these situations can offer their customers the additional items, possibly with a discount to make the option more attractive.
Applied more deeply, MBA allows companies to identify the keystone products, those that differentiate them in the market and could potentially hurt business if they were unavailable or more expensive. Gourmet or other specialty items in a grocery store might have limited appeal, but the customers they attract (and their subsequent spending) could justify high-visibility placement. Customers ordering through the company’s app could be interested in items or combinations that offer extra loyalty points.
Generating reliable insights from MBA typically requires large volumes of transactional data. Large data sets are difficult to process without highly-scalable storage and compute resources. Modern cloud-based architectures allow for more agile analytics with the capability to test a number of theories of customer behavior or to examine the effectiveness of a recent marketing campaign. Many of our clients are using our in-house technology as a data lake with BI tools, integrated with specialized Python statistical packages, to solve these problems.