A machine learning based heuristic to predict the efficacy of online sale
This addresses the challenge for online buyers in evaluating sale effectiveness, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled the problem of predicting the efficacy of online sales by quantifying discount significance based on product features and original price, achieving 91.11% accuracy on a Flipkart Summer Sale dataset using Support Vector Machine.
It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the \textit{"significance"} of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11\%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale.