Characterizing comparison shopping behavior: A case study

In this work we study the behavior of users on online comparison shopping using session traces collected over one year from an Indian mobile phone comparison website: http://smartprix.com. There are two aspects to our study: data analysis and behavior prediction. The first aspect of our study, data analysis, is geared towards providing insights into user behavior that could enable vendors to offer the right kinds of products and prices, and that could help the comparison shopping engine to customize the search based on user preferences. We discover the correlation between the search queries which users write before coming on the site and their future behavior on the same. We have also studied the distribution of users based on geographic location, time of the day, day of the week, number of sessions which have a click to buy (convert), repeat users, phones/brands visited and compared. We analyze the impact of price change on the popularity of a product and how special events such as launch of a new model affect the popularity of a brand. Our analysis corroborates intuitions such as increasing price leads to decrease in popularity and vice-versa. Further, we characterize the time lag in the effect of such phenomena on popularity. We characterize the user behavior on the website in terms of sequence of transitions between multiple states (defined in terms of the kind of page being visited e.g. home, visit, compare etc.). We use KL divergence to show that a time-homogeneous Markov chain is the right model for session traces when the number of clicks varies from 5 to 30. Finally, we build a model using Markov logic that uses the history of the user's activity in a session to predict whether a user is going to click to convert in that session. Our methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.