Defining The Decisive Factors on Purchase and Comparing Feature Importance Methods

Abstract

Online retail companies focus on two activities for getting revenue in their businesses and to survive in the market. First activity is increasing traffic of the online shopping platform and second activity is converting this traffic to revenue for the company. Marketing facilities try to attract customers to the online shopping platforms at great costs. Because of the costs of getting traffic, it is crucial to make customers order. Online shopping platforms need to understand which factors are decisive on customer purchase decision. In this study, which factors are decisive on consumer purchase decisions will be studied on an e-commerce retail platform from Turkey, Hepsiburada. Which of these factors are most decisive try to be defined: traffic source type (google, campaign, direct etc.) of the customer, customer persona or segment, which types of page or page components has been seen, product position on the page, does the customer benefited from campaign or discount, product review scores and counts, has the product recommended or not. In this study, data will be gathered from Hepsiburada transactions stored in google's big query environment. Performance problems will be solved via SQL optimization and other methods. Data quality issues will be fixed to get consistent results. Then statistical methods, supervised machine learning and deep learning methods will be applied to data for getting feature importances. Importance value of the features will show which factor decisive on customer purchase decision. Feature importance values will be compared and evaluated according to method, model results. Hyperparameter tunings is applied to the methods. Also, the model performances will be compared and evaluated. This study uses and compares 7 methods and there is no comprehensive study in literature in terms of method variety.