Advertisement

Retailers’ product location problem with consumer search

  • Raluca M. UrsuEmail author
  • Daria Dzyabura
Article
  • 127 Downloads

Abstract

With few exceptions, today’s retailers sell products across multiple categories. One strategic consideration of such retailers is product location, which determines how easy or difficult different categories are for customers to access. For example, grocery or department stores determine which products will be located closer to the entrance of the store versus at the back of it, while online retailers decide which products to feature on the homepage, and which will require scrolling or keyword search to get to. In this paper, we study how a retailer should optimally locate products within a store, when the locations chosen affect consumer search costs. We show that the retailer has an incentive to prioritize products with lower utility, contrasting with prior work. The intuition for our result is that the consumer may be willing to search less preferred products only at the lower cost, while the more preferred products will be searched even at higher search costs. This strategy benefits the retailer by increasing the number of products the consumer searches and thus, the ones she may buy. Our finding is robust to several extensions: (i) a retailer determining not only product locations, but also prices, (ii) independent (e.g. categories), as well as substitute products, and (iii) a focal retailer that faces competition. From a managerial perspective, we show that allocating products in the store without taking into account how this affects consumer search costs, might mean consumers overlook products they would otherwise purchase.

Keywords

Consumer search Multi-category retailer Product location problem 

JEL Classification

L81 D83 D11 

Notes

Acknowledgments

We are thankful for comments from Alixandra Barasch, Kristina Brecko, Xinyu Cao, Pradeep Chintagunta, Babur De los Santos, Chaim Fershtman, Tobias Gamp, Konstantin Korotkiy, Song Lin, Dmitry Lubensky, Eitan Muller, Cem Ozturk, Vaiva Petrikaite, Robbie Sanders, Andrey Simonov, Adam Smith, Monic Sun, Artem Timoshenko, Miguel Villas-Boas, Chris Wilson, Hema Yoganarasimhan, and attendees of the 2018 Consumer Search and Switching Cost Workshop, the 2018 Workshop on Multi-Armed Bandits and Learning Algorithms, and the 2018 Marketing Science conference. The usual disclaimer applies.

References

  1. Ainslie, A., & Rossi, P. (1998). Similarities in choice behavior across product categories. Marketing Science, 17(2), 91–106.Google Scholar
  2. Anglin, P. (1990). Disjoint search for the prices of two goods consumed jointly. International Economic Review, 31(2), 383–408.Google Scholar
  3. Anderson, S., & Renault, R. (1999). Pricing, product diversity, and search cost: a Bertrand-Chamberlin-Diamond model. The RAND Journal of Economics, 30(4), 719–735.Google Scholar
  4. Arbatskaya, M. (2007). Ordered search. The RAND Journal of Economics, 38 (1), 119–126.Google Scholar
  5. Armstrong, M., Vickers, J., Zhou, J. (2009). Prominence and consumer search. The RAND Journal of Economics, 40(2), 209–233.Google Scholar
  6. Armstrong, M., & Zhou, J. (2011). Paying for prominence. The Economic Journal, 121(556), 368–395.Google Scholar
  7. Athey, S., & Ellison, G. (2011). Position auctions with consumer search. The Quarterly Journal of Economics, 126(3), 1213–1270.Google Scholar
  8. Branco, F., Sun, M., Villas-Boas, J.M. (2012). Optimal search for product information. Management Science, 58(11), 2037–2056.Google Scholar
  9. Branco, F., Sun, M., Villas-Boas, J.M. (2016). Too much information? Information provision and search costs. Marketing Science, 35(4), 605–618.Google Scholar
  10. Burdett, K., & Malueg, D. (1981). The theory of search for several goods. Journal of Economic Theory, 24, 362–376.Google Scholar
  11. Carlin, B., & Ederer, F. (2018). Search fatigue. Review of Industrial Organization. forthcoming.Google Scholar
  12. Carlson, J., & McAfee, P. (1984). Joint search for several goods. Journal of Economic Theory, 32, 337–345.Google Scholar
  13. Chandon, P., Hutchinson, J.W., Bradlow, E.T., Young, S.H. (2009). Does in-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase. Journal of Marketing, 73(6), 1–17.Google Scholar
  14. Chen, Y., & He, C. (2011). Paid placement: Advertising and search on the internet. The Economic Journal, 121(556), 309–328.Google Scholar
  15. Chen, Y., & Yao, S. (2016). Sequential search with refinement: Model and application with click-stream data. Management Science, 34(4), 606–623.Google Scholar
  16. Chib, S., Seetharaman, P., Strijnev, A. (2002). Analysis of multi-category purchase incidence decisions using IRI market basket data. Econometric Models in Marketing, 16, 57–92.Google Scholar
  17. Chick, S., & Frazier, P.I. (2012). Sequential sampling with economics of selection procedures. Management Science, 58(3), 1–16.Google Scholar
  18. Chintagunta, P., & Halder, S. (1998). Investigating purchase timing behavior in two related product categories. Journal of Marketing Research, 35(1), 43–53.Google Scholar
  19. Cormen, T., Leiserson, C., Rivest, R., Stein, C. (2009). Introduction to algorithms, 3rd edn. Cambridge: MIT Press. Chapter 16.5 “Task scheduling problem”.Google Scholar
  20. De los Santos, B., & Koulayev, S. (2017). Optimizing click-through in online rankings with endogenous search refinement. Marketing Science, 36(4), 542–564.Google Scholar
  21. Erdem, T., & Winer, R. (1999). Econometric modeling of competition: A multi-category choice-based mapping approach. Journal of Econometrics, 89, 159–175.Google Scholar
  22. Ellison, G., & Ellison, S. (2009). Search, obfuscation, and price elasticities on the internet. Econometrica, 77(2), 427–452.Google Scholar
  23. Gamp, T. (2017). Guided search. Working article.Google Scholar
  24. Gatti, R. (1999). Multi-commodity consumer search. Journal of Economic Theory, 86, 219–244.Google Scholar
  25. Ghose, A., Ipeirotis, P., Li, B. (2012a). Surviving social media overload: Predicting consumer footprints on product search engines. Working article.Google Scholar
  26. Ghose, A., Ipeirotis, P., Li, B. (2012b). Designing ranking systems for hotels on travel search engines by mining user-generated and crowd-sourced content. Marketing Science, 31(3), 492–520.Google Scholar
  27. Ghose, A., Ipeirotis, P., Li, B. (2014). Examining the impact of ranking on consumer behavior and search engine revenue. Management Science, 60(7), 1632–1654.Google Scholar
  28. Granbois, D. (1968). Improving the study of customer in-store behavior. Journal of Marketing, 32(October), 28–33.Google Scholar
  29. Haan, M., Moraga-Gonzalez, J., Petrikaite, V. (2018). A model of directed consumer search. International Journal of Industrial Organization, forthcoming.Google Scholar
  30. Hagiu, A., & Jullien, B. (2011). Why do intermediaries divert search? The RAND Journal of Economics, 42(1), 337–362.Google Scholar
  31. Hansen, K., Singh, V., Chintagunta, P. (2006). Understanding store-brand purchase behavior across categories. Marketing Science, 25(1), 75–90.Google Scholar
  32. Hui, S., Inman, J., Huang, Y., Suher, J. (2013). The effect of in-store travel distance on unplanned spending: Applications to mobile promotion strategies. Journal of Marketing, 77(March), 1–6.Google Scholar
  33. Johnson, J. (2017). Unplanned purchases and retail competition. American Economic Review, 107(3), 931–965.Google Scholar
  34. Ke, T., Shen, Z.M., Villas-Boas, J.M. (2016). Search for information on multiple products. Management Science, 62(12), 3576–3603.Google Scholar
  35. Ke, T., & Villas-Boas, J.M. (2017). Optimal learning before choice. Working article.Google Scholar
  36. Koulayev, S. (2014). Search for differentiated products: Identification and estimation. The RAND Journal of Economics, 45(3), 553–575.Google Scholar
  37. Larson, J., Bradlow, E., Fader, P. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395–414.Google Scholar
  38. Levav, J., Heitmann, M., Herrmann, A., Iyengar, S. (2010). Order in product customization decisions: Evidence from field experiments. Journal of Political Economy, 118(2), 274–299.Google Scholar
  39. Liu, T. (2011). Learning to rank for information retrieval. Berlin: Springer.Google Scholar
  40. Manchanda, P., Ansari, A., Gupta, S. (1999). The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Science, 18(2), 95–114.Google Scholar
  41. Mehta, N. (2007). Investigating consumers’ purchase incidence and brand choice decisions across multiple product categories: A theoretical and empirical analysis. Marketing Science, 26(2), 196–217.Google Scholar
  42. McAfee, P. (1995). Multiproduct equilibrium price dispersion. Journal of Economic Theory, 67, 83–105.Google Scholar
  43. Ngwe, D., Ferreira, K., Teixeira, T. (2019). The impact of increasing search frictions on online shopping behavior: Evidence from a field experiment. Working paper.Google Scholar
  44. Petrikaite, V. (2018). Consumer obfuscation by a multiproduct firm. The RAND Journal of Economics, 49(1), 206–223.Google Scholar
  45. Redden, J.P. (2008). Reducing satiation: The role of categorization level. Journal of Consumer Research, 34(5), 624–634.Google Scholar
  46. Rhodes, A. (2011). Can Prominence matter even in an almost frictionless market? The Economic Journal, 121(556), 297–308.Google Scholar
  47. Seetharaman, P., Ainslie, A., Chintagunta, P. (1999). Investigating household state dependence effects across categories. Journal of Marketing Research, 36(4), 488–500.Google Scholar
  48. Seetharaman, P., Chib, S., Ainslie, A., Boatwright, P., Chan, T., Gupta, S., Mehta, N., Rao, V., Strijnev, A. (2005). Models of multi-category choice behavior. Marketing Letters, 16(2), 239–254.Google Scholar
  49. Shelegia, S. (2011). Multiproduct pricing in oligopoly. International Journal of Industrial Organization, 30(2), 231–242.Google Scholar
  50. Singh, V., Hansen, K., Gupta, S. (2005). Modeling preferences for common attributes in multicategory brand choice. Journal of Marketing Research, 42(2), 195–209.Google Scholar
  51. Song, I., & Chintagunta, P. (2006). Measuring cross-category price effects with aggregate store data. Management Science, 52(10), 1594–1609.Google Scholar
  52. Song, I., & Chintagunta, P. (2007). A discrete-continuous model for multicategory purchase behavior of households. Journal of Marketing Research, 44(4), 595–612.Google Scholar
  53. Stigler, G. (1961). The economics of information. Journal of Political Economy, 65(3), 213–225.Google Scholar
  54. Ursu, R. (2018). The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Science, 37(4), 530–552.Google Scholar
  55. Varian, H. (2007). Position auctions. International Journal of Industrial Organization, 25(6), 1163–1178.Google Scholar
  56. Weitzman, M. (1979). Optimal search for the best alternative. Econometrica, 47(3), 641–654.Google Scholar
  57. Wilson, C. (2010). Ordered search and equilibrium obfuscation. International Journal of Industrial Organization, 28(5), 496–506.Google Scholar
  58. Wolinsky, A. (1986). True monopolistic competition as a result of imperfect information. The Quarterly Journal of Economics, 101(3), 493–512.Google Scholar
  59. Yoganarasimhan, H. (2018). Search personalization using machine learning. Management Science, forthcoming.Google Scholar
  60. Zhou, J. (2011). Ordered search in differentiated markets. International Journal of Industrial Organization, 29(2), 253–262.Google Scholar
  61. Zhou, J. (2014). Multiproduct search and the joint search effect. American Economic Journal, 104(9), 2918–2939.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Marketing Department, Stern School of BusinessNew York UniversityNew YorkUSA
  2. 2.New Economic SchoolMoscowRussia

Personalised recommendations