Skip to main content
Log in

Demand sensing in e-business

  • Published:
Sadhana Aims and scope Submit manuscript

Abstract

In this paper, we identify various models from the optimization and econometrics literature that can potentially help sense customer demand in the e-business era. While modelling reality is a difficult task, many of these models come close to modelling the customer's decision-making process. We provide a brief overview of these techniques, interspersing the discussion occasionally with a tutorial introduction of the underlying concepts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andrews R L, Srinivasan T C 1991 Studying consideration effects in empirical choice models using scanner panel data.J. Marketing Res. 32: 30–41

    Article  Google Scholar 

  • Bachi A, Saroop A 2003Internet auctions: Some issues and problems, (Springer-Verlag) Lecture Notes in Computer Science (eds) G Goos, J Hartmanis, J van Leeuwen, vol. LNCS-2918, pp 112–119

  • Bapna R, Goes P, Gupta A, Karuga G 2002 Predictive caliberation of online multi-unit ascending auctions.Proc. WITS-2002, Twelfth Annual Workshop on Information Technology and Systems, Barcelona, Spain

  • Bell D E, Raiffa H 1979 Managerial value and intrinsic risk aversion. Working Paper, Technical Report 79-65, Harvard Business School, Boston, MA

    Google Scholar 

  • Ben-Akiva M, Boccora B 1995 Discrete choice models and latent choice sets.Int. J. Res. Marketing 12: 9–24

    Article  Google Scholar 

  • Bertsekas D P 1995Nonlinear programming (Belmont, MA: Athena Scientific)

    MATH  Google Scholar 

  • Boyd J, Mellman J 1980 The effect of fuel economy standards on the US automotive market: A hedonic demand analysis.Transportation Res. A14: 367–378

    Google Scholar 

  • Bronnenberg, Bart J, Vanhonacker W 1996 Limited choice sets, local price response and implied measures of price competition.J. Marketing Res. 33: 163–173

    Article  Google Scholar 

  • Cardell S, Dunbar F 1980 Measuring the societal impacts of automobile downsizing.Transportation Res. A14: 423–434

    Google Scholar 

  • Chiang J, Chib S, Narasimhan C 1999 Markov chain Monte Carlo and models of consideration set and parameter heterogeneity.J. Econometrics 89: 223–248

    Article  MATH  Google Scholar 

  • Chintagunta P K, Jain D C, Vilcassim N J 1991 Investigating heterogeneity in brand preferences in logit models for panel data.J. Marketing Res. 28: 417–429

    Article  Google Scholar 

  • Chipman J 1960 The foundations of utility.Econometrica 28: 193–224

    Article  MathSciNet  Google Scholar 

  • Dempster A, Laird N, Rubin D 1977 Maximum likelihood from incomplete data via the EM algorithm.J. R. Stat. Soc. 39: 1–38

    MATH  MathSciNet  Google Scholar 

  • Fortheringham A S 1988 Consumer store choice and choice set definition.Marketing Sci. 7: 299–310

    Article  Google Scholar 

  • Guadagni P M, Little J D C 1983 A logit model of brand choice calibrated on scanner data.Marketing Sci. 2: 203–238

    Google Scholar 

  • Haab T, McConnell K 1996 Count data models and the problem of zeros in recreation demand analysis.Am. J. Agri. Econ. 78: 89–102

    Article  Google Scholar 

  • Huberman B A, Hogg T, Swami A 2000 Using unsuccessful auction bids to identify latent demand. Downloaded from http://www.hpl.hp.com/research/idl/papers/auctions/auctions.pdf

  • Hulland J S 1992 An emprical investigation of consideration set formation.Adv. Consumer Res. 19: 253–254

    Google Scholar 

  • Kamakura W A, Russell G 1989 A probabilistic choice model for market segmentation and elasticity structure.J. Marketing Res. 26: 379–390

    Article  Google Scholar 

  • Lange K 1999Numerical analysis for statisticians (New York: Springer)

    MATH  Google Scholar 

  • Lee H, Padmanabhan P, Whang S 1997 The paralyzing curse of the bull whip effect in a supply chain.Sloan Manage. Rev.: 93–102

  • Manski C 1977 The structure of random utility models.Theor. Decision 8: 229–254

    Article  MATH  MathSciNet  Google Scholar 

  • Marschak J 1960Stanford symposium on mathematical methods in the social sciences (ed.) K Arrow, (Stanford, CA: University Press) pp 312–329

    Google Scholar 

  • McFadden, Daniel 1978 Estimation techniques for the elasticity of substitution and other production parameters.Production economics: A dual approach to theory and applications (eds) M Fuss, D McFadden, (Amsterdam: Elsevier Science, North-Holland) vol. 2 pp. 73–124

    Google Scholar 

  • McFadden D, Train K 1996 Consumers' evaluation of new products: Learning from self and others.J. Polit. Econ. 104: 683–703

    Article  Google Scholar 

  • McFadden D, Train K 2000 Mixed MNL models for discrete response.J. Appl. Econometrics 15: 447–470

    Article  Google Scholar 

  • McLachlan G, Krishnan T 1997The EM algorithm and extensions (New York: Wiley)

    MATH  Google Scholar 

  • Mitra, Anusree 1995 Advertising and the stability of consideration sets over multiple purchase occasions.Int. J. Res. Marketing 12: 81–94

    Article  Google Scholar 

  • Oliver K, Moeller L, Lakenan B 2004 Smart customization: Profitable growth through tailored business streams. Downloaded from http://www.strategy-business.com/media/file/sb34_04104.pdf

  • Pindyck R S, Rubinfeld D 2000Microeconomics (Englebert cliffs, NJ: Prentice Hall)

    Google Scholar 

  • Raju C V L, Narahari Y, Ravikumar K 2004a Learning dynamic prices in electronic retail markets with customer segmentation.Ann. Oper. Res. 41 (to appear)

  • Raju C V L, Narahari Y, Ravikumar K 2004b Learning nonlinear dynamic prices in electronic markets with price sensitive customers, stochastic demands, and inventory replenishments.Electron. Commerce Res. (communicated) (http://www.orie.cornell.edu/~paatrus/psfiles/applied-pricing.pdf)

  • Raju C V L, Narahari Y, Ravikumar K 2004c Learning nonlinear dynamic prices in multi-seller electronic markets with price sensitive customers, stochastic demands, and inventory replenishments.IEEE Trans. Syst. Man Cybernetics (communicated)

  • Ravikumar K, Sundar D K, Batra G, Saluja R 2004 Learning in dynamic pricing games of electronic service markets.Mach. Learning (communicated) (http://doi.ieeecomptersociety.org/10.1109/COEC.2003.1210269)

  • Roberts J H, Lattin J 1991 Development and testing of a model of consideration set composition.J. Marketing Res. 28: 429–440

    Article  Google Scholar 

  • Roberts J H, Urban G 1988 Modelling multiattribute utility, risk and belief dynamics for new consumer durable brand choice.Manage. Sci. 34: 167–185

    Google Scholar 

  • Rusmevichientong P, Salisbury J, Truss L T, Roy B, Glynn P 2004 Opportunities and challenges in using online preference data for vehicle pricing: A case study at general motors.J. Revenue Pricing Manage, (communicated)

  • Saroop A, Bagchi A 2000 Decision support system for timed bids in internet auctions.Proc. WITS-2000, Tenth Annu. Workshop on Information Technology and Systems, Brisbane, Australia, pp 229–234

  • Saroop A, Bagchi A 2002 Artificial neural networks for predicting final prices in eBay auctions.Proc. WITS-2002, Twelfth Annu. Workshop on Information Technology and Systems, Barcelona, Spain, pp 19–24

  • Shocker A, Ben-Akiva M, Boccara B, Nedungadi P 1991 Consideration set influences on consumer decision-making and choice: Issues, models and suggestions.Marketing Lett. 2: 181–197

    Google Scholar 

  • Siddarth S, Bucklin R, Morrison D 1995 Making the cut: Modelling and analysing choice set restriction is scanner panel data.J. Marketing Res. 32: 255–266

    Article  Google Scholar 

  • Thurstone L 1927 A law of comparative judgement,Psychol. Rev. 34: 273–286

    Article  Google Scholar 

  • Train K E 2003Discrete choice methods with simulation. (Cambridge: University Press)

    MATH  Google Scholar 

  • von Haefen R H 2003 Latent consideration sets and continuous demand system models. Downloaded from http://cals.arizona.edu/rogervh/consider073103.pdf

  • Wales T, Woodland A 1983 Estimation of consumer demand systems with binding non-negativity constraints.J. Econometrics 21: 263–285

    Article  MATH  Google Scholar 

  • Watkins C, Dayan P 1992Q-learning, machine learning 8: 279–292

    MATH  Google Scholar 

  • Wu J, Rangaswamy A 2003 A fuzzy set model of search and consideration with an application to an online market.Marketing Sci. 22: 411–434

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Ravikumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ravikumar, K., Saroop, A., Narahari, H.K. et al. Demand sensing in e-business. Sadhana 30, 311–345 (2005). https://doi.org/10.1007/BF02706250

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02706250

Keywords

Navigation