Market Basket Analysis: Case Study of a Supermarket

Pillai, Anup; Jolhe, Dhananjay A.

doi:10.1007/978-981-15-3639-7_87

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…In this milieu, understanding the intricacies of customer purchasing patterns has become a paramount concern. Market Basket Analysis (MBA) emerges as a powerful tool for unravelling the complex dynamics inherent in retail transactions (Pillai & Jolhe, 2020). The retail landscape is profoundly transforming, shaped by shifting consumer preferences, technological advancements, and a globalized marketplace.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Retail Supermarket Financial Performance Through Market Basket Analytics Using Apriori Algorithm in Indonesia Market Case

Heikal,

Gandhi

2024

AQA

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Market Basket Analysis is a powerful technique in data mining and retail analytics that explores associations and patterns among items frequently purchased together by consumers. This technique reveals insights into consumer purchasing behaviour and facilitates the creation of compelling product bundles. This study identifies five distinct product bundling strategies tailored to diverse consumer personas prevalent in the Indonesian market, such as "Health enthusiast", "Exotic Flavor Explorer", "Food Enthusiast", "Fitness Freak", and "Budget-conscious Home Cook". These product bundling strategies leverage market basket analysis to enhance the shopping experience, meeting Indonesian consumers' diverse preferences and lifestyles in the retail supermarket landscape. The analysis provides a basis for effective promotional campaigns and personalized marketing efforts. By recognizing associations between products, supermarkets in Indonesia can design targeted promotions, encouraging customers to explore complementary items and potentially increase their overall spending.

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Section: Introductionmentioning

confidence: 99%

Enhancing Retail Supermarket Financial Performance Through Market Basket Analytics Using Apriori Algorithm in Indonesia Market Case

Heikal,

Gandhi

2024

AQA

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“…Several studies related to supermarkets such as Hedge and Suresh (2017), Kurniawan et al (2018), Rokaha et al (2018), Makhitha and Khumalo (2019), Malagueño et al (2019) and Nguyen (2021) used either the questionnaires or transactions receipts from customers to collect the required data. In contrast, some studies in recent years such as Griva et al (2018), Kanavos et al (2018), Zhao and Ning (2020), Çiçekli and Kabasakal (2021) and Pillai and Jolhe (2021) used supermarket databases for analysis. In practice, the use of supermarket databases provides valuable insights for supermarket management to understand customer needs as well as to further develop strategies to promote products and improve sales through cross-selling.…”

Section: Introductionmentioning

confidence: 99%

“…In doing so, the rules that relate to product-level purchase decisions were also associated with gender, location and age group that enable marketing practitioners to find customers' purchase patterns and designed personalized promotions. Moreover, Pillai and Jolhe (2021) applied market basket analysis on the transactions data in a supermarket to determine the arrangement of goods, design of sales promotion and discounts for different customer segments to enhance customer satisfaction and thereby increase the sales. The results also provided valuable insights for cross-selling, up-selling and new product integration tasks for supermarket management.…”

Section: Introductionmentioning

confidence: 99%

“…(2018), Kanavos et al. (2018), Zhao and Ning (2020), Çiçekli and Kabasakal (2021) and Pillai and Jolhe (2021) used supermarket databases for analysis. In practice, the use of supermarket databases provides valuable insights for supermarket management to understand customer needs as well as to further develop strategies to promote products and improve sales through cross-selling.…”

Section: Introductionmentioning

confidence: 99%

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A data examination on a supermarket dataset in Taiwan

Tang

Yang

et al. 2022

TQM

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PurposeThe purpose of this study is to focus on a database from a supermarket for market segmentation.Design/methodology/approachRather than using the questionnaire to collect the customers' purchase behaviors, this study uses a database from a supermarket as an example to illustrate how market segmentation can be performed by adding useful variables particularly from behavioral variables and through Chi-square test and analysis of variance.FindingsThe illustration of the database from a branch supermarket shows that two demographic variables (gender and age group) and two behavioral variables (date of transactions and time of purchase) are critical variables for market segmentation when total money spent and purchased merchandize items are taken into consideration.Practical implicationsThis case study can be a reference for supermarket practitioners to follow in order to increase profit and to provide better services for their customers.Originality/valueNo studies have been found to combine demographic variables, behavioral variables, merchandize items and total amount for market segmentation simultaneously. Different combinations of demographic variables, behavioral variables and merchandize items can be further used to perform market segmentation more effectively. This framework used in this case study can be further applied in similar industries including retail industries for market segmentation as well as to fulfill customer needs in practice.

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“…The frequent pattern and association rules mining methodology first proposed by Agrawal et al [1]. The support and confidence are two measures commonly used to derive the association rules [2]. The support measure calculates the frequency of itemsets in transactional database (i.e., how many times it appears in customer transactions) and the itemsets which satisfy the user specified minimum support threshold level is considered as frequent itemsets.…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

Rana

Mondal

2021

COMPUTE

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Market Basket Analysis is an observational data mining methodology to investigate the consumer buying behavior patterns in retail Supermarket. It analyzes customer baskets and explores the relationship among products that helps retailers to design store layouts, make various strategic plans and other merchandising decisions that have a big impact on retail marketing and sales. Frequent itemsets mining is the first step for market basket analysis. The association rules mining uncovers the relationship among products by looking what products the customers frequently purchase together. In retail marketing, the transactional database consists of many itemsets that are frequent only in a particular season however not taken into consideration as frequent in general. In some cases, association rules mining at lower data level with uniform support doesn't reflect any significant pattern however there is valuable information hiding behind it. To overcome those problems, we propose a methodology for mining seasonally frequent patterns and association rules with multilevel data environments. Our main contribution is to discover the hidden seasonal itemsets and extract the seasonal associations among products in additionally with the traditional strong regular rules in transactional database that shows the superiority for making season based merchandising decisions. The dataset has been generated from the transaction slips in large supermarket of Bangladesh that discover 442 more seasonal patterns as well as 1032 seasonal association rules in additionally with the regular rules for 0.1% minimum support and 50% minimum confidence.

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Market Basket Analysis: Case Study of a Supermarket

Cited by 13 publications

References 5 publications

Enhancing Retail Supermarket Financial Performance Through Market Basket Analytics Using Apriori Algorithm in Indonesia Market Case

Enhancing Retail Supermarket Financial Performance Through Market Basket Analytics Using Apriori Algorithm in Indonesia Market Case

A data examination on a supermarket dataset in Taiwan

Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

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