Market Basket Analysis is used to identify or gain interesting insights into the way customers buy items. It finds out the combination of items which are most likely to be bought together. For example buying milk and bread together can lead to buying butter. Identifying such patterns from the transaction data collected over the years provide new window of opportunity to explore and understand the purchasing behavior and thus can be used to increase sales through cross selling and targeted marketing. Market basket analysis is not limited to retail sales but are applied to a wide range of areas like analyzing credit card purchases, fraud detection for insurance claims and so on. Data Mining technique called association rule mining is usually used. Algorithms like FP-Growth and Apriori algorithm are usually used for mining the transaction data to get the desired patterns.
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Market basket analysis is a very interesting part of data analytics. It's very much part of all the e-retail websites. Its been slowly inculcated in the retail stores as well.To know more about data analytics visit us at Data Analytics Institute In Bangalore.
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Market basket analysis, also known as association analysis or affinity analysis, is a data mining technique used by retailers and businesses to uncover buying patterns from customer purchase history. It helps identify products that are frequently bought together by customers.
Here's a breakdown of key concepts in market basket analysis:
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Transaction Data: This is the foundation of market basket analysis. It consists of individual customer purchases, typically recorded at the point of sale (POS) system. Each transaction includes details like:
Items purchased: Product IDs or descriptions.
Quantity purchased: How many units of each item were bought.
Transaction ID: A unique identifier for each purchase.
Date and Time: When the purchase was made.
Itemsets: These are groups of items that appear together in a transaction. For instance, {bread, milk} is an itemset if customers frequently buy bread and milk together.
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