Ameliorating Buyer’s Remorse

  • Rakesh Agrawal ,
  • Samuel Ieong ,
  • Raja Velu

International Conference on Knowledge Discovery and Data Mining (KDD) |

Published by ACM

Keeping in pace with the increasing importance of commerce conducted over the Web, several e-commerce websites now provide admirable facilities for helping consumers decide what product to buy and where to buy it. However, since the prices of durable and high-tech products generally fall over time as firms continually introduce products that have enhanced features, a buyer of such products is often faced with a dilemma: Should she buy the product now or wait for cheaper prices?

We present the design and implementation of Prodcast, an experimental system whose goal is to help consumers decide when to buy a product. The system makes use of forecasts of future prices based on price histories of the products, incorporating features such as sales volume, seasonality, and competition in making its recommendation. We describe techniques that are well-suited for this task and present a comprehensive evaluation of their relative merits using retail sales data for electronic products. Our back-testing of the system indicates that the system is capable of helping consumers time their purchase, resulting in significant savings to them.