What’s Market Basket Analysis?

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Market basket analysis is the use of data mining techniques to analyze customer purchasing data to find patterns and relationships between purchased products. This information can help a retailer design on-site or e-commerce shopping spaces. It can also be used to develop marketing campaigns. Valuation can be expensive, so it can only be used for popular items.

Both affinity analysis and cluster analysis can be used when evaluating market baskets. An affinity analytics tool can be used to find activities, such as purchases, that occur at the same time. Cluster analysis is a type of statistical technique that organizes raw data into categories and is often used for more complex analyses. When evaluating market baskets, the sales of one item or group of items will be examined in relation to the sales of one or more other items or groups.

Other concepts in market basket analysis include frequency, support, and security. Frequency refers to the number of times customers buy products A and B at the same time. Support is the number of times products A and B have been purchased together out of the total number of sales. The term trust refers to the number of times products A and B were purchased together, compared to the number of times only product A was purchased.

For example, a swimwear retailer might use market basket analysis to compare the sale of swimwear to suntan lotion. The retailer finds that many of its customers buy swimwear, and these shoppers often buy sunscreen as well. You can use the results of this analysis to organize your on-site or e-commerce store to increase the chances of your customers purchasing these related products. At the on-site shop, you can place displays of suntan lotion alongside your bathing suits. Suntan lotion can be listed as a related item in an e-commerce store.

While market basket analysis is frequently used for items purchased at the same time, it can also be applied to purchases made over a period of time. The retailer can target advertising to take advantage of the likelihood of a customer purchasing products in a particular order. For example, one furniture store finds that customers who buy cribs then buy two single beds about two years later. The furniture store may obtain customer contact information and email promotions for twin beds approximately 18 months after the crib purchase.

Market basket analysis can become expensive and difficult to implement when retailers have large numbers of commodities to analyze. To be more affordable, retailers often focus their efforts on those items that have both high support and high trust. These items are likely to generate enough profit to make the cost of the analysis worthwhile.

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