EconPapers    
Economics at your fingertips  
 

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour: An Empirical Study

Sanjeev Prashar and S.K. Mitra
Additional contact information
Sanjeev Prashar: Indian Institute of Management Raipur, Raipur, India
S.K. Mitra: Indian Institute of Management Raipur, Raipur, India

International Journal of Strategic Decision Sciences (IJSDS), 2015, vol. 6, issue 4, 54-71

Abstract: With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJSDS.2015100104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsds00:v:6:y:2015:i:4:p:54-71

Access Statistics for this article

International Journal of Strategic Decision Sciences (IJSDS) is currently edited by Saeed Tabar

More articles in International Journal of Strategic Decision Sciences (IJSDS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

 
Page updated 2025-03-19
Handle: RePEc:igg:jsds00:v:6:y:2015:i:4:p:54-71