Thirdly, both negative sentiment and positive sentiment of the reviews are individually significant predictors as per regression and decision-tree model, but they are not significant at all as per neural network model. Secondly, discount rate, discount amount and average ratings have minimal or insignificant effect on sales prediction. Firstly, all three models confirm that review volume is the most important and significant predictor of sales of books at amazon.in. The comparative analysis of these models reveals several significant findings. In addition, the regression analysis, with and without sentiment and interaction factors, generates comparable results. In terms of accuracy of prediction, the artificial neural network model is found to perform better than the decision-tree based model. The importance of each independent predictor variable, such as discount rate, review sentiment etc., is analyzed based on the outcome of each model to determine top significant predictors which can be controlled by the marketer to influence sales. Sentiment analysis is carried out to measure the polarity of online reviews, which are included as predictors in these models. This study investigates the efficacy of various modeling techniques, namely, regression analysis, decision-tree analysis and artificial neural network, for predicting the sales of books at amazon.in, using various relevant factors and their interactions as predictor variables. For e-tailers such as, Amazon, this kind of prediction capability is also extremely important in order to manage the supply chain efficiently as well as ensure customer satisfaction.

In the context of e-commerce, accurate prediction of customer demand, typically captured by sales volume, requires careful analysis of multiple factors, namely, type of product, country of purchase, price, discount rate, free delivery option, online review sentiment etc., and their interactions. Prediction of customer demand is an important part of Supply Chain Management, as it helps to avoid over or under production and reduces delivery time.

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