TY - GEN
T1 - Ameliorating buyer's remorse
AU - Agrawal, Rakesh
AU - Ieong, Samuel
AU - Velu, Raja
PY - 2011
Y1 - 2011
N2 - 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, 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.
AB - 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, 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.
KW - Forecasting
KW - Prodcast
KW - Recommendation
UR - http://www.scopus.com/inward/record.url?scp=80052664917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052664917&partnerID=8YFLogxK
U2 - 10.1145/2020408.2020466
DO - 10.1145/2020408.2020466
M3 - Conference contribution
AN - SCOPUS:80052664917
SN - 9781450308137
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 351
EP - 359
BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
PB - Association for Computing Machinery
T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011
Y2 - 21 August 2011 through 24 August 2011
ER -