Market basket analysis and product bundling at Mirra’s Kitchen

Learning outcomes To appreciate the problems faced by the restaurants during the pandemic. To be able to perform the computations related to market basket analysis metrics using an Microsoft Excel spreadsheet. To be able to perform association rule mining using Python. To identify the products that are likely to go together from the output of market basket analysis and to suggest the product bundles. To recommend suitable pricing for the product bundles and the individual elements of the combo to maximize the profits, using evolutionary optimisation in an Microsoft Excel spreadsheet. Case overview/synopsis This case is about a restaurant in the Kolkata city of West Bengal state of India that wanted to find the degree of association between different products in the customer’s cart so that it could use that information to create suitable product bundles. The case not only exposes the students to appreciate the significance of metrics in market basket analysis but also enables them to compute those metrics using an Microsoft Excel spreadsheet and a Python notebook. The case also aims to enable the students to determine the optimal selling price of the product bundles and the individual elements so as to maximise the profits. Complexity academic level This case can be used in the graduate studies programs such as Masters of Business Administration for the courses related to marketing analytics or pricing analytics during the later stage of the programmes when the specialised courses are covered. Supplementary material Teaching notes are available for educators only. Subject code CSS 7: Management Science.

Authors

Publication Details

Journal
Emerald Emerging Markets Case Studies
Published
2025-04-26
DOI
https://doi.org/10.1108/eemcs-08-2024-0343
Primary Topic
Agricultural Economics and Practices
Type
article
Field-Weighted Citation Impact
0.00
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article

Market basket analysis and product bundling at Mirra’s Kitchen

Sandeep R. Singh, Gaurav Nagpal, Sarveshwar Kumar Inani, Sachin Gupta et al.
Emerald Emerging Markets Case Studies
Agricultural Economics and Practices
article

Market basket analysis and product bundling at Mirra’s Kitchen

Sandeep R. Singh, Gaurav Nagpal, Sarveshwar Kumar Inani, Sachin Gupta, Ankita Nagpal, Manish Das
article en

Abstract

Learning outcomes To appreciate the problems faced by the restaurants during the pandemic. To be able to perform the computations related to market basket analysis metrics using an Microsoft Excel spreadsheet. To be able to perform association rule mining using Python. To identify the products that are likely to go together from the output of market basket analysis and to suggest the product bundles. To recommend suitable pricing for the product bundles and the individual elements of the combo to maximize the profits, using evolutionary optimisation in an Microsoft Excel spreadsheet. Case overview/synopsis This case is about a restaurant in the Kolkata city of West Bengal state of India that wanted to find the degree of association between different products in the customer’s cart so that it could use that information to create suitable product bundles. The case not only exposes the students to appreciate the significance of metrics in market basket analysis but also enables them to compute those metrics using an Microsoft Excel spreadsheet and a Python notebook. The case also aims to enable the students to determine the optimal selling price of the product bundles and the individual elements so as to maximise the profits. Complexity academic level This case can be used in the graduate studies programs such as Masters of Business Administration for the courses related to marketing analytics or pricing analytics during the later stage of the programmes when the specialised courses are covered. Supplementary material Teaching notes are available for educators only. Subject code CSS 7: Management Science.

Emerald Emerging Markets Case StudiesVol. 15(2)
Openalex Percentile: Top 88%
Agricultural Economics and Practices
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