TITLE:
A Random Forest Approach for Predicting Online Buying Behavior of Indian Customers
AUTHORS:
Rohit Joshi, Rohan Gupte, Palanisamy Saravanan
KEYWORDS:
Online Purchase Intention, Prior Online Purchase Experience, Geographical Location, India, E-Commerce, Retailing, Random Forest Model
JOURNAL NAME:
Theoretical Economics Letters,
Vol.8 No.3,
February
13,
2018
ABSTRACT: Online retailing
in India has shown remarkable growth in the recent years. Despite having a low internet
penetration rate of 34.5%, India has the second largest number of internet users
in the world after China. Given the growing importance of the online retail industry
in India and its diverse set of sensitivities and region wise socio-psychological
barriers, it is imperative for retailers to understand customer shopping preferences.
In this paper, we attempt to understand various factors influencing the online buying
behavior of Indian customers in different product categories, across geographic
locations in India. Also, we developed and validated the Random Forest prediction
model for each identified product category, to understand if the Indian online shopping
market is ready for these product categories or the traditional channel is preferred
over by customer. A questionnaire based survey is used to collected data from 124
Indian respondents from 18 states of India. The survey captured from both offline
and online shopping environment to aggregate understanding of customers’ shopping
preferences. The high Sensitivity (above 85%) of the Random Forest model for Books
and Electronics categories suggests inclination of purchase intension of customer
towards online shopping. Retailers can use this model to predict the buying behavior
of customers based on the location. However, for product categories like Movies,
Sports equipment and Handbags, the high value of Specificity signifies the model prediction towards offline
purchase intensions. So for these product categories retailers may like to focus
more on customer services at retail stores.