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. 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. 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. 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. Sentiment analysis is carried out to measure the polarity of online reviews, which are included as predictors in these models. 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. In terms of accuracy of prediction, the artificial neural network model is found to perform better than the decision-tree based model. In addition, the regression analysis, with and without sentiment and interaction factors, generates comparable results. The comparative analysis of these models reveals several significant findings. Firstly, all three models confirm that review volume is the most important and significant predictor of sales of books at amazon.in. Secondly, discount rate, discount amount and average ratings have minimal or insignificant effect on sales prediction. 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. This observation from the neural network model is contrary to the extant research which claims that both negative and positive sentiment are significant with the former having more influence in predicting sales. Finally, the interaction effects of review volume with negative and positive sentiment are also found to be significant predictors as per all three models. Hence, overall, out of various factors used for sales prediction of books, review volume, negative sentiment, positive sentiment and their interactions are found to be the most significant ones across all models. The results of this study can be utilized by online sellers to accurately predict the sales volume by adjusting these significant factors, thereby managing the supply chain effectively.

中文翻译：

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印度亚马逊市场的图书销售预测分析：一种机器学习方法

预测客户需求是供应链管理的重要组成部分，因为它有助于避免生产过剩或生产不足并减少交货时间。在电子商务的背景下，准确预测客户需求（通常是通过销量来捕获）需要仔细分析多个因素，即产品类型，购买国家/地区，价格，折扣率，免费送货选项，在线评论情绪等。及其互动。对于像亚马逊这样的电子零售商来说，这种预测能力也非常重要，以便有效地管理供应链并确保客户满意度。这项研究调查了各种建模技术（即回归分析，决策树分析和人工神经网络）对预测亚马逊图书销售的功效。使用各种相关因素及其相互作用作为预测变量。进行情感分析以衡量在线评论的极性，这些评论作为预测因素包括在这些模型中。根据每个模型的结果，分析每个独立预测变量（如折现率，评论情绪等）的重要性，以确定可以由营销商控制以影响销售的头等重要预测变量。在预测的准确性方面，发现人工神经网络模型的性能优于基于决策树的模型。此外，在有或没有情感因素和相互作用因素的情况下，回归分析都能得出可比的结果。这些模型的比较分析揭示了几个重要发现。首先，所有这三个模型都证实，评论量是amazon.in图书销量的最重要和最重要的预测指标。其次，折扣率，折扣金额和平均评级对销售预测的影响微乎其微。第三，根据回归和决策树模型，评论的负面情绪和正面情绪都是单独的重要预测变量，但对于神经网络模型则完全不重要。来自神经网络模型的这一观察结果与现有的研究相反，后者声称负面情绪和正面情绪都是重要的，而前者在预测销售方面的影响更大。最后，根据所有这三种模型，评论量与消极情绪和积极情绪的相互作用也被认为是重要的预测因素。因此，总的来说，在用于图书销售预测的各种因素中，发现书评量，负面情绪，正面情绪及其相互作用是所有模型中最重要的因素。通过调整这些重要因素，在线卖家可以利用这项研究的结果来准确预测销量，从而有效地管理供应链。