Application of Market Basket Analysis to Redesign Supermarket Layouts for Optimum Returns in Kenyan Supermarkets
Abstract
Market Basket Analysis also known as Association Rule Mining is a data mining technique for discovering/
identifying interesting relations between variables in a large database. It helps businesses
uncover relationships between frequently purchased products by consumers. This technique unveils
insights into consumer purchasing behavior, facilitates the creation of compelling product bundles,
and better shelve planning in a physical supermarket. Market Basket Analysis aims to identify patterns
and associations among products by analyzing transactional data, such as customer purchase
history or shopping cart contents. This study explores the application of Market Basket Analysis
(MBA) to optimize supermarket layouts for enhanced profitability in Kenyan supermarkets. Utilizing
both the Apriori and ECLAT algorithms, the research aims to identify the most frequently
purchased items, explore items commonly bought together, and compare the performance of these
two models in computing item relationships. The research adopts a descriptive design to analyze
transactional data from the supermarket. Transactional data from a Kenyan supermarket database
was extracted and preprocessed for analysis. Utilizing a minimum support threshold of 0.01 and a
minimum confidence level of 0.6, 223 frequent item sets and 96 rules were generated. This analysis
identified several significant association rules, such as {HARPIC, SUGAR WHITE} => {VIM POWDER}
with a support of 0.0106 and confidence of 0.7079, and {SUGAR WHITE, VIM POWDER} =>
{HARPIC} with a support of 0.0106 and confidence of 0.7895. Additionally, the rule {JAMAA B/S,
PASTA TAK, SUGAR WHITE} => {AJAB NGANO} was identified with the support of 0.0112 and confidence
of 0.8827, among others. This meant that these items can be placed on the shelves next or
close to each other as they are highy co-related. This will enhance their visibility increasing there
chances of being bought by customers thus increasing sales as well meeting the customers varying
preferences. The insights gained from this analysis ensures better product placement on the
shelves.The findings provide actionable insights for supermarket managers to enhance cross-selling
opportunities, optimize store layouts, and improve profitability and customer satisfaction.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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