Market Basket Analysis
Market basket analysis (MBA) is an example of an analytics technique employed by retailers to understand customer purchase behaviors. It is used to determine what items are frequently bought together or placed in the same basket by customers. It uses this purchase information to leverage effectiveness of sales and marketing. MBA looks for combinations of products that frequently occur in purchases and has been prolifically used since the introduction of electronic point of sale systems that have allowed the collection of immense amounts of data.
Market basket analysis only uses transactions with more than one item, as no associations can be made with single purchases. Item association does not necessarily suggest a cause and effect, but simply a measure of co-occurrence. It does not mean that since energy drinks and video games are frequently bought together, one is the cause for the purchase of the other, but it can be construed from the information that this purchase is most probably made by (or for) a gamer. Such rules or hypothesis must be tested and should not be taken as truth unless item sales say otherwise.
There are two main types of MBA:
- Predictive MBA is used to classify cliques of item purchases, events and services that largely occur in sequence.
- Differential MBA removes a high volume of insignificant results and can lead to very in-depth results. It compares information between different stores, demographics, seasons of the year, days of the week and other factors.
MBA is commonly used by online retailers to make purchase suggestions to consumers. For example, when a person buys a particular model of smartphone, the retailer may suggest other products such as phone cases, screen protectors, memory cards or other accessories for that particular phone. This is due to the frequency with which other consumers bought these items in the same transaction as the phone.
MBA is also used in physical retail locations. Due to the increasing sophistication of point of sale systems coupled with big data analytics, stores are using purchase data and MBA to help improve store layouts so that consumers can more easily find items that are frequently purchased together.