Market basket analysis might tell a retailer that customers often purchase shampoo and conditioner together, so putting both items on promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely drive sales of the other.
Market basket analysis may provide the retailer with information to understand the purchase behavior of a buyer. This information will enable 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). An apocryphal early illustrative example for this was when one super market chain discovered in its analysis that male customers that bought diapers often bought beer as well, have put the diapers close to beer coolers, and their sales increased dramatically. Although this urban legend is only an example that professors use to illustrate the concept to students, the explanation of this imaginary phenomenon might be that fathers that are sent out to buy diapers often buy a beer as well, as a reward.3 This kind of analysis is supposedly an example of the use of data mining. A widely used example of cross selling on the web with market basket analysis is Amazon.com's use of "customers who bought book A also bought book B", e.g. "People who read History of Portugal were also interested in Naval History".
Market basket analysis can be used to divide customers into groups. A company could look at what other items people purchase along with eggs, and classify them as baking a cake (if they are buying eggs along with flour and sugar) or making omelets (if they are buying eggs along with bacon and cheese). This identification could then be used to drive other programs. Similarly, it can be used to divide products into natural groups. A company could look at what products are most frequently sold together and align their category management around these cliques.4
Business use of market basket analysis has significantly increased since the introduction of electronic point of sale.5 Amazon uses affinity analysis for cross-selling when it recommends products to people based on their purchase history and the purchase history of other people who bought the same item. Family Dollar plans to use market basket analysis to help maintain sales growth while moving towards stocking more low-margin consumable goods.6
An important clinical application of affinity analysis is that it can be performed on medical patient records in order to generate association rules. The obtained association rules can be further assessed to find different conditions and features that coincide on a large block of information.7 It is crucial to understand whether there is an association between different factors contributing to a condition to be able to administer the effective preventive or therapeutic interventions. In evidence-based medicine, finding the co-occurrence of symptoms that are associated with developing tumors or cancers can help diagnose the disease at its earliest stage.8 In addition to exploring the association between different symptoms in a patient related to a specific disease, the possible correlations between various diseases contributing to another condition can also be identified using affinity analysis.9
Larose, Daniel T.; Larose, Chantal D. (2014-06-23). Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/9781118874059. ISBN 978-1-118-87405-9. 978-1-118-87405-9 ↩
"Demystifying Market Basket Analysi". Retrieved 28 December 2018. http://www.information-management.com/news/demystifying-market-basket-analysis ↩
"The parable of the beer and diapers". The Register. Retrieved 3 September 2009. https://www.theregister.co.uk/2006/08/15/beer_diapers/ ↩
Product Network Analysis Archived 2018-11-18 at the Wayback Machine Forte Consultancy Group http://www.forteconsultancy.com/Ourideas/551/Product_Network_Analysis_%E2%80%93_The_Next_Big_Thing_in_Retail_Data_Mining.aspx ↩
"Family Dollar Supports Merchandising with IT". Archived from the original on 6 May 2010. Retrieved 3 November 2009. https://web.archive.org/web/20100506040136/http://www.retailerdaily.com/entry/45999/family-dollar-merchandising/ ↩
Sanida, Theodora; Varlamis, Iraklis (June 2017). "Application of Affinity Analysis Techniques on Diagnosis and Prescription Data". 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). Thessaloniki: IEEE. pp. 403–408. doi:10.1109/CBMS.2017.114. ISBN 978-1-5386-1710-6. 978-1-5386-1710-6 ↩
Sengupta, Dipankar; Sood, Meemansa; Vijayvargia, Poorvika; Hota, Sunil; Naik, Pradeep K (29 June 2013). "Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor". Bioinformation. 9 (11): 555–559. doi:10.6026/97320630009555. PMC 3717182. PMID 23888095. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717182 ↩
Lakshmi, K.S; Vadivu, G. (2017). "Extracting Association Rules from Medical Health Records Using Multi-Criteria Decision Analysis". Procedia Computer Science. 115: 290–295. doi:10.1016/j.procs.2017.09.137. https://doi.org/10.1016%2Fj.procs.2017.09.137 ↩