Author(s): ?engül CAN

Nowadays, the developing technologies and increasing data storage capacity have revealed the concept of big data. Data mining techniques are used to reach meaningful information found in big data. There is intense interaction between data mining and marketing. In the global competitive environment, this intense interaction affects firms. Companies develop special applications for their customers in order to compete. To develop the personal applications, accurate and detailed information about customers are needed. Reaching to accurate information that is hidden in a large amount of data can be possible by data mining technique. One of the most commonly used techniques in data mining is market basket analysis. Market basket analysis is used to analyze purchasing behavior of customers using their products information. In this study, association rules analysis was applied to market data which contains 440 data and 8 attributes. All data and 6 qualities were included in the analysis. According to obtained findings, of purchased products, 66% contained vegetables and cleaning products together. Similarly, coexistence probability of milk, vegetables and prepared foods was found to be over 50%. In addition, buying vegetable possibility was 97% for individuals who buy milk, cleaning products and prepared foods. Moreover, it was found that buying vegetable possibility was depending on buying of Milk, Cleaning Product, Prepared Food, Fresh Fruit, Cleaning Product, Prepared Food and Milk, Cleaning Product, Prepared Food, Frozen Food in 96% rate. Buying cleaning product possibility was depending on buying of vegetables or Milk, Vegetables, Prepared Food products in 94% rate. It was thought that determination of market shelf positions by taking these rules into consideration could create positive results for companies. Also, it was thought that if companies conduct special campaigns for their customers by taking these rules into consideration, this could come back to as customer loyalty and profitability to companies.


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