Market Basket Analysis: A Case Study of a Dehradun Based Bakery Shop
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Abstract
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. The main goal of such an analysis is to entice
customer to buy more than they usually do. Market basket analysis (MBA) is an example of an analytics technique employed by retailers to understand customer purchase behaviors, which can aid the retailer in
correct decision making. It is used to determine what items are frequently bought together or placed in the same basket by customers (Kaur & Kang, 2016). In this research we study the new trends, challenges, and the impact of market basket analysis on consumer buying behavior by using Data Analytics software Rapidminer to create frequent item-sets and associations between the products purchased together in a bakery shop in Dehradun. The study exploits the certain marketing activities which later can be leveraged effectively by the Bakery shop and hence increase their sales and profit margin.
References
1. Aggarwal C, Wolf J. ANew Method for Similarity Indexing of Market Basket Data. New York. (1999)
2. Boztug Y, Reutterer T. 2006. Acombined approach for segment-specific market basket analysis. Austria: Elsevier, 294-312
3. Chen Y, Tang K, Shen R, Hu Y. 2004. Market basket analysis in a multiple store environment. USA: Elsevier,339-354
4. Chib S, Seetharaman P, Strijnev A. 2002. Analysis of multi-category Purchase incidence decisions Using IRI market basket data
5. Decker R, Monein K. 2003. Market basket analysis with neural gas networks and self- organising maps. Germany: Henry Stewart Publications, 337-386.
6. Huang C, Chaung K, Chen M. 2002 An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios. Taiwan.
7. Kaur M, Kang S. 2016. Market Basket Analysis: Identify the changing trends of market data using association rule mining. Sanguar: Elsevier, 78-85
8. Lee J, Jun C, Lee J, Kim S. 2005. Classification-based collaborative filtering using market basket data. South korea. Elsevier, 700-704
9. Mild A, Reutterer T. 2002. An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Austria.
10. Muller, Henning, Pun, Thierry, Squire, David. Learning from user behavior in image retrieval : application of market basket analysis. International Journal of Computer Vision,2004, vol. 56, no. 1- 2, p. 65-77
11. Nanopoulos A, Manopoulos Y. 2002. Efficient similarity search for market basket data. Greece: Springer-verlag.
12. Szymkowiak M, Klimanek T, Józefowski T. 2018. Applying market basket analysis to official Statistical data. Poland. DE Gruyter. Vol-22.
13. Samaraweera WJ, Waduge CP, and Meththananda R. Market Basket Analysis: AProfit Based Approach to Apriori Algorithm. Sri lanka.2017
14.Shende P; Mehendarge S; Chougule S; Kulkarni P; Hatwar U. 2017 .Innovative ideas to improve shopping mall experience over E-commerce websites using beacon technology and data mining algorithms. IEEE.
15. Yun C, Chaung K, Chen M. Using Category-Based Adherence to Cluster Market-Basket Data. Taiwan.
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