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Attention and Saliency on the Internet: Evidence from an Online Recommendation System

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  • Christian Helmers
  • Pramila Krishnan
  • Manasa Patnam

Abstract

Using high-frequency transaction-level data from an online retail store, we examine whether consumer choices on the internet are consistent with models of limited attention. We test whether consumers are more likely to buy products that receive a saliency shock when they are recommended by new products. To identify the saliency effect, we rely on i) the timing of new product arrivals, ii) the fact that new products are per se highly salient upon arrival, drawing more attention and iii) regional variation in the composition of recommendation sets. We find a sharp and robust 6% increase in theaggregate sales of existing products after they are recommended by a new product. To structurally disentangle the effect of saliency on a consumer’s consideration and choice decision, we use data on individual transactions to estimate a probabilistic choice set model. We find that the saliency effectis driven largely by an expansion of consumers’ consideration sets.

Suggested Citation

  • Christian Helmers & Pramila Krishnan & Manasa Patnam, 2015. "Attention and Saliency on the Internet: Evidence from an Online Recommendation System," Cambridge Working Papers in Economics 1563, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1563
    Note: pk237
    as

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    References listed on IDEAS

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    1. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    2. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    3. Michael Dinerstein & Liran Einav & Jonathan Levin & Neel Sundaresan, 2014. "Consumer Price Search and Platform Design in Internet Commerce," Discussion Papers 13-038, Stanford Institute for Economic Policy Research.
    4. Paola Manzini & Marco Mariotti, 2014. "Stochastic Choice and Consideration Sets," Econometrica, Econometric Society, vol. 82(3), pages 1153-1176, May.
    5. Marco A. Haan & José L. Moraga‐González, 2011. "Advertising for Attention in a Consumer Search Model," Economic Journal, Royal Economic Society, vol. 121(552), pages 552-579, May.
    6. Raj Chetty & Adam Looney & Kory Kroft, 2009. "Salience and Taxation: Theory and Evidence," American Economic Review, American Economic Association, vol. 99(4), pages 1145-1177, September.
    7. Kfir Eliaz & Ran Spiegler, 2011. "Consideration Sets and Competitive Marketing," Review of Economic Studies, Oxford University Press, vol. 78(1), pages 235-262.
    8. Hauser, John R & Wernerfelt, Birger, 1990. " An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Oxford University Press, vol. 16(4), pages 393-408, March.
    9. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, September.
    10. Alan T. Sorensen, 2007. "BESTSELLER LISTS AND PRODUCT VARIETY -super-," Journal of Industrial Economics, Wiley Blackwell, vol. 55(4), pages 715-738, December.
    11. Basar, Gözen & Bhat, Chandra, 2004. "A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 889-904, December.
    12. S. Dellavigna., 2011. "Psychology and Economics: Evidence from the Field," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 5.
    13. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Incorporating random constraints in discrete models of choice set generation," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 91-102, April.
    14. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    15. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    16. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
    17. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters,in: E-commerce, pages 541-558 National Bureau of Economic Research, Inc.
    18. Octavian Carare, 2012. "The Impact Of Bestseller Rank On Demand: Evidence From The App Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 717-742, August.
    19. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    20. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    21. Chiang, Jeongwen & Chib, Siddhartha & Narasimhan, Chakravarthi, 1998. "Markov chain Monte Carlo and models of consideration set and parameter heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 223-248, November.
    22. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    23. Draganska, Michaela & Klapper, Daniel, 2010. "Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data," Research Papers 2063, Stanford University, Graduate School of Business.
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    Cited by:

    1. Eliaz, Kfir & Oren-Kolbinger, Orli & Weisburd, Sarit, 2017. "Limited Attention, Salience and Changing Prices: Evidence from a Field Experiment in Online Supermarket Shopping," CEPR Discussion Papers 12014, C.E.P.R. Discussion Papers.
    2. Florian Heiss & Daniel McFadden & Joachim Winter & Amelie Wuppermann & Bo Zhou, 2016. "Inattention and Switching Costs as Sources of Inertia in Medicare Part D," NBER Working Papers 22765, National Bureau of Economic Research, Inc.
    3. Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Papers 71, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.

    More about this item

    Keywords

    Limited attention; advertising; online markets.;

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • K11 - Law and Economics - - Basic Areas of Law - - - Property Law
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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