A Meta-Model Implementation with Tabu Search Technique to Determine the Buying Pattern of Online Customers

Abstract

Customers shopping pattern depends on features like demography, psychology, culture etc. Observing factors that affect online shopping behavior helps in classifying customers and devising proper marketing strategy. In this paper a metamodel was proposed to classify Indian online shoppers based on their online shopping pattern. Here 163 observations with 14 attributes related to online shopping data were gathered through a questionnaire. Online shopping overhead was relatively reduced by removing irrelevant features from the dataset by using Greedy Stepwise search and Tabu search. This reduced set of shopping data is applied to RBF classifier to correctly predict customer shopping trends. Results were evaluated using some vital performance metrics to determine classification efficiency. The proposed model using Tabu Search was inferred to be more performance driven since the classification accuracy with Tabu search was 88.35% while the error rate was reduced to 0.0786. The model development time was found to be just 1.23 sec with Tabu search. The Mathews Coefficient was determined to be 0.774 with Tabu search. The proposed work can also be extended to classify other real time data sets. Classification accuracy may further be enhanced by adding physiographic and cultural inputs to the proposed classifier.