Designing a Better Shopbot

A primary tool that consumers have for comparative shopping is the shopbot, which is short for shopping robot. These shopbots automatically search a large number of vendors for price and availability. Typically a shopbot searches a predefined set of vendors and reports all results, which can result in time-consuming searches that provide redundant or dominated alternatives. Our research demonstrates analytically how shopbot designs can be improved by developing a utility model of consumer purchasing behavior. This utility model considers the intrinsic value of the product and its attributes, the disutility from waiting, and the cognitive costs associated with evaluating the offers retrieved. We focus on the operational decisions made by the shopbot: which stores to search, how long to wait, and which offers to present to the user. To illustrate our model we calibrate the model to price and response time data collected at online bookstores over a six-month period. Using prior expectations about price and response time, we show how shopbots can substantially increase consumer utility by searching more intelligently and then selectively presenting offers.

[1]  J. Mazanec,et al.  Consumer decision making. , 1994 .

[2]  Ramayya Krishnan,et al.  Prices and Price Dispersion on the Web: Evidence from the Online Book Industry , 2001 .

[3]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[4]  J. Bakos.  Reducing buyer search costs: implications for electronic marketplaces , 1997 .

[5]  E. Olusegun George,et al.  A remark on the shape of the logistic distribution , 1978 .

[6]  Jeffrey O. Kephart,et al.  Shopbots and Pricebots , 1999, IJCAI.

[7]  John G. Lynch,et al.  Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution , 2000 .

[8]  G. A. Mack,et al.  Order Statistics (2nd Ed.) , 1983 .

[9]  A. Rangaswamy,et al.  Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, , 2000 .

[10]  Itamar Simonson.  Original ArticlesThe effect of product assortment on buyer preferences4 , 1999 .

[11]  Donald G. Morrison,et al.  Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data , 1995 .

[12]  Joel Huber,et al.  A theory of cutoff formation under imperfect information , 1996 .

[13]  Kevin Lane Keller,et al.  Effects of Quality and Quantity of Information on Decision Effectiveness , 1987 .

[14]  M. D. Wilkinson,et al.  Management science , 1989, British Dental Journal.

[15]  Gerald L. Lohse,et al.  An Information Search Cost Perspective for Designing Interfaces for Electronic Commerce , 1999 .

[16]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .

[17]  Oren Etzioni,et al.  A scalable comparison-shopping agent for the World-Wide Web , 1997, AGENTS '97.

[18]  M. W. Green,et al.  2. Handbook of the Logistic Distribution , 1991 .

[19]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[20]  Z. Griliches,et al.  Econometric Models for Count Data with an Application to the Patents-R&D Relationship , 1984 .

[21]  A. Tversky.  Features of Similarity , 1977 .

[22]  J. Lattin,et al.  Consideration: Review of Research and Prospects for Future Insights , 1997 .

[23]  D. McFadden.  Econometric Models of Probabilistic Choice , 1981 .

[24]  Paul E. Green,et al.  Thirty Years of Conjoint Analysis: Reflections and Prospects , 2001, Interfaces.

[25]  Manfred Kochen,et al.  On the economics of information , 1972, J. Am. Soc. Inf. Sci..

[26]  I. Simonson,et al.  The effect of product assortment on buyer preferences 4 4 The paper has benefited from the comments , 1999 .

[27]  Steven M. Shugan.  The Cost Of Thinking , 1980 .

[28]  M. F. Luce,et al.  Constructive Consumer Choice Processes , 1998 .

[29]  David Bawden,et al.  Perspectives on information overload , 1999 .

[30]  Nicholas R. Jennings,et al.  A Roadmap of Agent Research and Development , 2004, Autonomous Agents and Multi-Agent Systems.

[31]  Erik Brynjolfsson,et al.  Consumer Decision-Making at an Internet Shopbot , 2001 .

[32]  Norm Archer,et al.  A buyer behaviour framework for the development and design of software agents in e-commerce , 2000, Internet Res..

[33]  A. Tversky.  Elimination by aspects: A theory of choice. , 1972 .

[34]  Michael R. Baye,et al.  Information Gatekeepers on the Internet and the Competitiveness of Homogeneous Product Markets , 2001 .

[35]  B. Kahn,et al.  How Tolerable is Delay? Consumers’ Evaluations of Internet Web Sites after Waiting , 1998 .

[36]  Gerald L. Lohse,et al.  Cognitive Lock-In and the Power Law of Practice , 2003 .

[37]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[38]  Peter S. Fader,et al.  Modeling Consumer Choice among SKUs , 1996 .

[39]  W. Wayne Talarzyk,et al.  Electronic Information Systems for Consumers: An Evaluation of Computer-Assisted Formats in Multiple Decision Environments , 1993 .

[40]  Peter E. Rossi,et al.  The Value of Purchase History Data in Target Marketing , 1996 .

[41]  H. A. David,et al.  Order Statistics (2nd ed). , 1981 .

[42]  Pattie Maes,et al.  Kasbah: An Agent Marketplace for Buying and Selling Goods , 1996, PAAM.

[43]  John G. Lynch,et al.  Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces , 1997 .

[44]  B. Ratchford,et al.  Consumer information search revisited: Theory and empirical analysis , 1997 .

[45]  C. E. Clark.  The Greatest of a Finite Set of Random Variables , 1961 .

[46]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[47]  D. McFadden.  Econometric Models for Probabilistic Choice Among Products , 1980 .

[48]  Stephen J. Hoch,et al.  A psychological approach to decision support systems , 1996 .

[49]  Andrew B. Whinston,et al.  Integrating User Preferences and Real-Time Workload in Information Services , 2000, Inf. Syst. Res..

[50]  A. Pazgal,et al.  Internet Shopping Agents: Virtual Co-Location and Competition , 2001 .

[51]  Ahmed Karmouch,et al.  Mobile software agents: an overview , 1998, IEEE Commun. Mag..

[52]  Godwin J. Udo,et al.  Factors Affecting E-Commerce Web Site Effectiveness , 2002, J. Comput. Inf. Syst..

[53]  Eric J. Johnson,et al.  A componential analysis of cognitive effort in choice , 1990 .

[54]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[55]  David Poole,et al.  The Independent Choice Logic for Modelling Multiple Agents Under Uncertainty , 1997, Artif. Intell..

[56]  Greg M. Allenby,et al.  On the Heterogeneity of Demand , 1998 .

[57]  John W. Payne,et al.  Effort and Accuracy in Choice , 1985 .

[58]  John W. Payne,et al.  The adaptive decision maker: Name index , 1993 .

[59]  Peter Boatwright,et al.  Reducing Assortment: An Attribute-Based Approach , 2001 .

[60]  Peter S. Fader,et al.  On the Depth and Dynamics of Online Search Behavior , 2004, Manag. Sci..

[61]  J. Jacoby,et al.  Brand Choice Behavior as a Function of Information Load , 1974 .

[62]  Jakob Nielsen,et al.  Designing web usability , 1999 .

[63]  Paul E. Green,et al.  A Hybrid Utility Estimation Model for Conjoint Analysis , 1981 .

[64]  E. Clemons,et al.  The Nature of Competition in Electronic Markets: An Empirical Investigation of Online Travel Agent Offerings , 1999 .