Understanding Customer Behaviour with Machine Learning
Rohan Bali1, Satyajee Srivastava2

1Rohan Bali, Student, Department of School of Computer Science and Engineering, Galgotias University , Greater Noida, India.
2Satyajee Srivastava, Assistant Professor, Department of School of Computer Science and Engineering, Galgotias University , Greater Noida, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4017-4020 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9069038620/2020©BEIESP | DOI: 10.35940/ijrte.F9069.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The study of customer behavior both in online and offline purchases plays a very important role for the seller. The aim of this study is to identify customers on various parameters and thus re-define policies based on the behavior of customers. This paper works on churn analytics for retaining customers, a market-based analysis for identifying the support and confidence among products and a recommendation system built on the IBCF approach. Churn Analytics helps the seller to answer about whether the customers are leaving there products or services. The goal of every seller is to maintain a low churn rate and thus have large margins and bigger profits. Further, performing a market-based analysis can be very fruitful for a supermart. This approach helps in organizing the items in a store in an efficient and scientific manner. This paper uses different machine learning algorithms techniques to conduct churn for the given data. It then calculates the accuracy and precision of each model using a confusion matrix. Confusion matrix thus helps us in selecting the best model to get more accurate results. This paper conducts the above analysis using the ‘Apriori’ algorithm. To conclude, a recommendation system is used to suggest customers products based on the history of their purchase or the similarities of that product with other products or other consumers. Thus, this study will help in understanding various aspects of customer behavior.
Keywords: Analytics, Apriori, Churn, Customer Behaviour, IBCF
Scope of the Article: Machine Learning.