Booth Id:
BEHA029
Category:
Behavioral and Social Sciences
Year:
2020
Finalist Names:
Shiono, Kanade (School: Tokyo Metropolitan Hibiya High School)
Abstract:
How can we assess customer satisfactions with online shopping? In this research, I conducted multiple analyses, including regression analyses, network analysis, frequency analysis, and structural equation modeling, with the sales data of lipsticks from Rakuten Market. My goal is to clarify what affects customer satisfactions and how to make attractive advertisements.
I first made two fish bone diagrams as my hypothesis, aiming to increase customers satisfactions and increase shops’ sales.
Regression analysis between cost and age based on the heading “cost-effectiveness” showed that customers purchase the cheaper products, costing approximately 2000 yen regardless of their age. To find the reason for the first result, I made two groups depending on the product price customers bought and carried out multiple regression analysis with several variables I made. I found two groups in Rakuten Market: those who had already chosen the products they wanted and searched for the cheapest product of that brand, and customers who searched online for good quality products. To investigate the effects of brand names and customer incentives like “free delivering” written in product names, I gathered customer reviews by specific sales words contained in product names and conducted network analysis and frequency analysis. This analysis reveals that there is a difference between what shops intend to advertise and what customers are attracted. Finally, structural equation modeling was used to find how each factor contributes to increasing customer satisfactions.
I believe this research can improve shops’ sales efficiency by considering customers desire.