Advertisement

Omnichannel Analytics

  • Marcel Goic
  • Marcelo OlivaresEmail author
Chapter
Part of the Springer Series in Supply Chain Management book series (SSSCM, volume 8)

Abstract

Retail business models have evolved over the years to create a value chain that combines multiple channels to interact with customers and suppliers. At the same time, technological advances have enabled the collection of various forms of data which can be used to support managerial decisions. This chapter provides a constructive framework to understand the practice of retail analytics—the data-driven approach to support decisions based on models and quantitative methods—through the dynamic evolution of various channels of what is now referred to as omnichannel retail. This framework is supported with several research examples that illustrate the differences in terms of data, decisions, and methods used in various retail channels, and also show more recent examples of convergence and integration across channels.

Keywords

Retail management Operations management Quantitative marketing Analytics Empirical research 

Notes

Acknowledgements

Part of this chapter was based on the theses of Renzo Fuenzalida, Andrea Rojas, and Cesar Ferreiro in Industrial Engineering, Universidad de Chile. We thank Stefano Maccioni for help as a research assistant. The authors received financial support from the Complex Engineering Systems Institute (grant CONICYT PIA FB0816) and Fondecyt grant 1181201.

References

  1. Agarwal, A., Hosanagar, K., & Smith, M. D. (2011). Location, location, location: An analysis of profitability of position in online advertising markets. Journal of Marketing Research, 48(6), 1057–1073.CrossRefGoogle Scholar
  2. Albuquerque, P., & Bronnenberg, B. J. (2012). Measuring the impact of negative demand shocks on car dealer networks. Marketing Science, 31(1), 4–23.CrossRefGoogle Scholar
  3. Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, Jing, B., et al. (2008). Putting one-to-one marketing to work: Personalization, customization, and choice. Marketing Letters, 19(3–4), 305.Google Scholar
  4. Basker, E. (2005). Job creation or destruction? Labor market effects of Wal-Mart expansion. Review of Economics and Statistics, 87(1), 174–183.CrossRefGoogle Scholar
  5. Basker, E., & Michael, N. (2009). The evolving food chain: competitive effects of Wal-Mart’s entry into the supermarket industry. Journal of Economics & Management Strategy, 18(4), 977–1009.CrossRefGoogle Scholar
  6. Bell, D., Santiago G., & Antonio, M. (2014). How to win in an omnichannel world. MIT Sloan Management Review, 56(1), 45.Google Scholar
  7. Bell, D. R., Gallino, S., & Moreno, A. (2017). Offline showrooms in omnichannel retail: Demand and operational benefits. Management Science, 64(4), 1629–1651.CrossRefGoogle Scholar
  8. Bell, D. R., Ho, T. H., & Tang, C. S. (1998). Determining where to shop: Fixed and variable costs of shopping. Journal of Marketing Research, 35(3), 352–369.CrossRefGoogle Scholar
  9. Berman, R., & Feit, E. M. (2018). Enhancing Power of Marketing Experiments Using Observational Data. The Wharton School Research Paper; Wharton Customer Analytics Initiative Research Paper. http://dx.doi.org/10.2139/ssrn.3140631Google Scholar
  10. Besanko, D., Gupta, S., & Jain, D. (1998). Logit demand estimation under competitive pricing behavior: An equilibrium framework. Management Science, 44(11-part-1), 1533–1547.Google Scholar
  11. Blattberg, R. C., Briesch, R., & Fox, E. J. (1995). How promotions work. Marketing Science, 14(3 Suppl.), G122–G132.CrossRefGoogle Scholar
  12. Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669–688.CrossRefGoogle Scholar
  13. Bradley, R. (2003). Price index estimation using price imputation for unsold items. In Scanner data and price indexes (pp. 349–382). Chicago: University of Chicago Press.CrossRefGoogle Scholar
  14. Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79–95.CrossRefGoogle Scholar
  15. Breuer, R., Brettel, M., & Engelen, A. (2011). Incorporating long-term effects in determining the effectiveness of different types of online advertising. Marketing Letters, 22(4), 327–340.CrossRefGoogle Scholar
  16. Bronnenberg, B. J., & Mela, C. F. (2004). Market roll-out and retailer adoption for new brands. Marketing Science, 23(4), 500–518.CrossRefGoogle Scholar
  17. Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249–267.CrossRefGoogle Scholar
  18. Burke, R. R. (2006). The third wave of marketing intelligence. In Retailing in the 21st Century (pp. 113–125). Berlin: Springer.CrossRefGoogle Scholar
  19. Busse, M. R., Lacetera, N., Pope, D. G., Silva-Risso, J., & Sydnor, J. R. (2013). Estimating the effect of salience in wholesale and retail car markets. American Economic Review, 103(3), 575–79.CrossRefGoogle Scholar
  20. Cachon, G. P., Gallino, S., & Olivares, M. (2018). Does adding inventory increase sales? Evidence of a scarcity effect in us automobile dealerships. Management Science, 65(4), 1469–1485.CrossRefGoogle Scholar
  21. Christen, M., Gupta, S., Porter, J. C., Staelin, R., & Wittink, D. R. (1997). Using market-level data to understand promotion effects in a nonlinear model. Journal of Marketing Research, 34, 322–334.CrossRefGoogle Scholar
  22. Chuang, H. H. C., Oliva, R., & Perdikaki, O. (2016). Traffic-based labor planning in retail stores. Production and Operations Management, 25(1), 96–113.CrossRefGoogle Scholar
  23. Cohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using Big Data to Estimate Consumer Surplus: The Case of Uber. Technical Report. Cambridge: National Bureau of Economic Research.Google Scholar
  24. Conlon, C. T., & Mortimer, J. H. (2013). Demand estimation under incomplete product availability. American Economic Journal: Microeconomics, 5(4), 1–30.Google Scholar
  25. Cui, R., Zhang, D. J., & Bassamboo, A. (2018). Learning from inventory availability information: Evidence from field experiments on Amazon. Management Science, 65(3), 1216–1235.CrossRefGoogle Scholar
  26. Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243.CrossRefGoogle Scholar
  27. Danaher, P. J., Mullarkey, G. W., & Essegaier, S. (2006). Factors affecting web site visit duration: A cross-domain analysis. Journal of Marketing Research, 43(2), 182–194.CrossRefGoogle Scholar
  28. De los Santos, B., Hortaçsu, A., & Wildenbeest, M. R. (2012). Testing models of consumer search using data on web browsing and purchasing behavior. American Economic Review, 102(6), 2955–80.Google Scholar
  29. DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54(4), 627–641.CrossRefGoogle Scholar
  30. Dinerstein, M., Einav, L., Levin, J., & Sundaresan, N. (2018). Consumer price search and platform design in internet commerce. American Economic Review, 108(7), 1820–1859.CrossRefGoogle Scholar
  31. Dinner, I. M., Van Heerde, H. J., & Neslin, S. A. (2014). Driving online and offline sales: The cross-channel effects of traditional, online display, and paid search advertising. Journal of Marketing Research, 51(5), 527–545.CrossRefGoogle Scholar
  32. Ellet, J. (2018). New research shows growing impact of online research on in-store purchases. Forbes (Ed.). [Online; posted 8 Feb 2018]. https://www.forbes.com/sites/johnellett/2018/02/08/new-research-shows-growing-impact-of-online-research-on-in-store-purchases/#68e3aa9e16a0
  33. Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287–1309.CrossRefGoogle Scholar
  34. Farley, J. U., & Ring, L. W. (1966). A stochastic model of supermarket traffic flow. Operations Research, 14(4), 555–567.CrossRefGoogle Scholar
  35. Fisher, M., Gallino, S., & Li, J. (2017). Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Science, 64(6), 2496–2514.CrossRefGoogle Scholar
  36. Fisher, M., Gallino, S., & Netessine, S. (2018, August 1). Setting retail staffing levels: A methodology validated with implementation. Wharton working paper. Available at SSRN: https://ssrn.com/abstract=2977812 or  https://doi.org/10.2139/ssrn.2977812.
  37. Fisher, M. L., Raman, A., & McClelland, A. S. (2009). Rocket science retailing. Boston: Harvard Business School Press.Google Scholar
  38. Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A metaanalysis. Journal of Retailing, 90(2), 217–232.CrossRefGoogle Scholar
  39. Fu, Y. (2018). Essays on Empirical Operations Management in Retail Sales and Service. PhD thesis. Wharton School, University of Pennsylvania.Google Scholar
  40. Fuenzalida, R. (2017). Implementation of a Multichannel Management Support System for Physical Stores Based on Customer Online Data. MA thesis. Santiago: Department of Industrial Engineering, University of Chile.Google Scholar
  41. Gallino, S., & Moreno, A. (2014). Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Science, 60(6), 1434–1451.CrossRefGoogle Scholar
  42. Gallino, S., & Moreno, A. (2018). The value of fit information in online retail: Evidence from a randomized field experiment. Manufacturing & Service Operations Management, 20(4), 767–787.CrossRefGoogle Scholar
  43. Glaeser, C. K., Fisher, M., & Su, X. (2019). Optimal retail location: Empirical methodology and application to practice: Finalist–2017 M& SOM practice-based research competition. Manufacturing & Service Operations Management, 21(1), 86–102.CrossRefGoogle Scholar
  44. Goic, M., Álvarez, R., & Montoya, R. (2018). The effect of house ads on multichannel sales. Journal of Interactive Marketing, 42, 32–45.CrossRefGoogle Scholar
  45. Goic, M., Guajardo, J., & Ma, L. (2018). The effect of social recommendation and location in reaching and converting mobile customers. https://ssrn.com/abstract=3274718 CrossRefGoogle Scholar
  46. Goic, M., Rojas, A., & Saavedra, I. (2016). Investigating the effectiveness of triggered email marketing. https://ssrn.com/abstract=3217130 CrossRefGoogle Scholar
  47. Goldfarb, A., & Tucker, C. E. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71.CrossRefGoogle Scholar
  48. Gonzalez, A. (2016). Amazon sues alleged providers of fake reviews. News article. https://www.seattletimes.com/business/amazon/amazon-sues-alleged-providers-of-fake-reviews/
  49. Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6.CrossRefGoogle Scholar
  50. Guadagni, P. M., & Little, J. D. C. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2(3), 203–238.CrossRefGoogle Scholar
  51. Gupta, S. (1988). Impact of sales promotions on when, what, and how much to buy. Journal of Marketing Research, 25(4), 342–355.CrossRefGoogle Scholar
  52. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.CrossRefGoogle Scholar
  53. Hauser, J. L., Urban, G. L., Liberali, G., & Braun, M. (2009). Website morphing. Marketing Science, 28(2), 202–223.CrossRefGoogle Scholar
  54. Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: Retailer-level and channel-level outcomes of online-offline channel integration. Journal of Retailing, 91(2), 309–325.CrossRefGoogle Scholar
  55. Holmes, A., Byrne, A., & Rowley, J. (2013). Mobile shopping behaviour: Insights into attitudes, shopping process involvement and location. International Journal of Retail & Distribution Management, 42(1), 25–39.CrossRefGoogle Scholar
  56. Huang, T., & Van Mieghem, J. A. (2014). Clickstream data and inventory management: Model and empirical analysis. Production and Operations Management, 23(3), 333–347.CrossRefGoogle Scholar
  57. Hui, S. K., Bradlow, E. T., & Fader, P. S. (2009). Testing behavioral hypotheses using an integrated model of grocery store shopping path and purchase behavior. Journal of Consumer Research, 36(3), 478–493.CrossRefGoogle Scholar
  58. Hui, S. K., Fader, P. S., & Bradlow, E. T. (2009a). Path data in marketing: An integrative framework and prospectus for model building. Marketing Science, 28(2), 320–335.CrossRefGoogle Scholar
  59. Hui, S. K., Fader, P. S., & Bradlow, E. T. (2009b). Research note|the traveling salesman goes shopping: The systematic deviations of grocery paths from TSP optimality. Marketing Science, 28(3), 566–572.CrossRefGoogle Scholar
  60. Jain, A., Misra, S., & Rudi, N. (2016). Sales assistance, search and purchase decisions: An analysis using retail video data. http://dx.doi.org/10.2139/ssrn.2699765Google Scholar
  61. Kang, J. S., Kuznetsova, P., Luca, M., & Choi, Y. (2013). Where not to eat? Improving public policy by predicting hygiene inspections using online reviews. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1443–1448).Google Scholar
  62. Kannan, P. K., Reinartz, W., & Verhoef, P. C. (2016, September). The path to purchase and attribution modeling: Introduction to special section. International Journal of Research in Marketing, 33(3), 449–456.CrossRefGoogle Scholar
  63. Kesavan, S., & Mani, V. (2015). An overview of industry practice and empirical research in retail workforce management. In Retail supply chain management (pp. 113–145). Boston: SpringerGoogle Scholar
  64. Ketelaar, P. E., Bernritter, S. F., van Woudenberg, T. J., Rozendaal, E., Konig, R. P., Hühn, A. E., et al. (2018). “Opening” location-based mobile ads: How openness and location congruency of location-based ads weaken negative effects of intrusiveness on brand choice. Journal of Business Research, 91, 277–285.CrossRefGoogle Scholar
  65. Kleijnen, M., De Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83(1), 33–46.CrossRefGoogle Scholar
  66. Kök, A. G., & Fisher, M. L. (2007). Demand estimation and assortment optimization under substitution: Methodology and application. Operations Research, 55(6), 1001–1021.CrossRefGoogle Scholar
  67. Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, 50(5), 561–576.CrossRefGoogle Scholar
  68. Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395–414.CrossRefGoogle Scholar
  69. Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881–894.CrossRefGoogle Scholar
  70. Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941–1973.CrossRefGoogle Scholar
  71. Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40–56.CrossRefGoogle Scholar
  72. Li, J., Netessine, S., & Koulayev, S. (2017). Price to compete. . . with many: How to identify price competition in high-dimensional space. Management Science, 64(9), 4118–4136.CrossRefGoogle Scholar
  73. Li, S., Sun, B., & Montgomery, A. L. (2011). Cross-selling the right product to the right customer at the right time. Journal of Marketing Research, 48(4), 683–700.CrossRefGoogle Scholar
  74. Li, X. (2016). Could deal promotion improve merchants’ online reputations? The moderating role of prior reviews. Journal of Management Information Systems, 33(1), 171–201. http://dx.doi.org/10.1080/07421222.2016.1172450 CrossRefGoogle Scholar
  75. Liaukonyte, J., Teixeira, T., & Wilbur, K. C. (2015). Television advertising and online shopping. Marketing Science, 34(3), 311–330.CrossRefGoogle Scholar
  76. Lu, Y., Musalem, A., Olivares, M., & Schilkrut, A. (2013). Measuring the effect of queues on customer purchases. Management Science, 59(8), 1743–1763.CrossRefGoogle Scholar
  77. Luca, M. (2016). Reviews, reputation, and revenue: The case of Yelp.com. Com (March 15, 2016). Harvard Business School NOM Unit working paper, (12-016).Google Scholar
  78. Mallapragada, G., Chandukala, S. R., & Liu, Q. (2016). Exploring the effects of “What” (product) and “Where” (website) characteristics on online shopping behavior. Journal of Marketing, 80(2), 21–38.CrossRefGoogle Scholar
  79. Manchanda, P., Dubé, J. P., Goh, K. Y., & Chintagunta, P. K. (2006). The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1), 98–108.CrossRefGoogle Scholar
  80. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.Google Scholar
  81. Mehra, A., Kumar, S., & Raju, J. S. (2017). Competitive strategies for brick-and-mortar stores to counter “showrooming”. Management Science, 64(7), 3076–3090.CrossRefGoogle Scholar
  82. Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29–39.CrossRefGoogle Scholar
  83. Montgomery, A. L., & Smith, M. D. (2009). Prospects for personalization on the internet. Journal of Interactive Marketing, 23(2), 130–137.CrossRefGoogle Scholar
  84. Montgomery, A. L., Hosanagar, K., Krishnan, R., & Clay, K. B. (2004a). Designing a better shopbot. Management Science, 50(2), 189–206.CrossRefGoogle Scholar
  85. Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C. (2004b). Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4), 579–595.CrossRefGoogle Scholar
  86. Moon, K., Bimpikis, K., & Mendelson, H. (2017). Randomized markdowns and online monitoring. Management Science, 64(3), 1271–1290.CrossRefGoogle Scholar
  87. Murray, K. B., & Häubl, G. (2009). Personalization without interrogation: Towards more effective interactions between consumers and feature-based recommendation agents. Journal of Interactive Marketing, 23(2), 138–146.CrossRefGoogle Scholar
  88. Musalem, A., Bradlow, E. T., & Raju, J. S. (2008). Who’s got the coupon? Estimating consumer preferences and coupon usage from aggregate information. Journal of Marketing Research, 45(6), 715–730.CrossRefGoogle Scholar
  89. Musalem, A., Olivares, M., Bradlow, E. T., Terwiesch, C., & Corsten, D. (2010). Structural estimation of the effect of out-of-stocks. Management Science, 56(7), 1180–1197.CrossRefGoogle Scholar
  90. Musalem, A., Olivares, M., & Schilkrut, A. (2016). Retail in High Definition: Monitoring Customer Assistance Through Video Analytics. Columbia Business School Research Paper (pp. 15–73)Google Scholar
  91. Nantel, J. (2004). My virtual model: Virtual reality comes into fashion. Journal of Interactive Marketing, 18(3), 73–86.CrossRefGoogle Scholar
  92. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.CrossRefGoogle Scholar
  93. Okazaki, S., & Mendez, F. (2013). Perceived ubiquity in mobile services. Journal of Interactive Marketing, 27(2), 98–111.CrossRefGoogle Scholar
  94. Paco, U. (1999). Why we buy: The science of shopping. Cornell Hotel and Restaurant Administration Quarterly, 3(42), 8–9.Google Scholar
  95. Park, Y. -H., & Fader, P. S. (2004). Modeling browsing behavior at multiple websites. Marketing Science, 23(3), 280–303.CrossRefGoogle Scholar
  96. Parker, C., Ramdas, K., & Savva, N. (2016). Is IT enough? Evidence from a natural experiment in India’s agriculture markets. Management Science, 62(9), 2481–2503.CrossRefGoogle Scholar
  97. Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012). Effect of traffic on sales and conversion rates of retail stores. Manufacturing & Service Operations Management, 14(1), 145–162.CrossRefGoogle Scholar
  98. Phillips, R. L. (2005). Pricing and revenue optimization. Palo Alto: Stanford University Press.Google Scholar
  99. Seiler, S., & Pinna, F. (2017). Estimating search benefits from path-tracking data: Measurement and determinants. Marketing Science, 36(4), 565–589.CrossRefGoogle Scholar
  100. Postma, O. J., & Brokke, M. (2002). Personalisation in practice: The proven effects of personalisation. Journal of Database Marketing & Customer Strategy Management, 9(2), 137–142.CrossRefGoogle Scholar
  101. Roberts, J. H., & Lattin, J. M. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research, 28, 429–440.CrossRefGoogle Scholar
  102. Roggeveen, A. L., Nordfält, J., & Grewal, D. (2016). Do digital displays enhance sales? Role of retail format and message content. Journal of Retailing, 92(1), 122–131.CrossRefGoogle Scholar
  103. Rossi, P. E., McCulloch, R. E., & Allenby, G. M. (1996). The value of purchase history data in target marketing. Marketing Science, 15(4), 321–340.CrossRefGoogle Scholar
  104. Sackmann, S., Strüker, J., & Accorsi, R. (2006). Personalization in privacy-aware highly dynamic systems. Communications of the ACM, 49(9), 32–38.CrossRefGoogle Scholar
  105. Schwartz, D., Fischhoff, B., Krishnamurti, T., & Sowell, F. (2013). The Hawthorne effect and energy awareness. Proceedings of the National Academy of Sciences, 110(38), 15242–15246.CrossRefGoogle Scholar
  106. Sorensen, A. T. (2007). Bestseller lists and product variety. The Journal of Industrial Economics, 55(4), 715–738.CrossRefGoogle Scholar
  107. Sorensen, H. (2003). The science of shopping. Marketing Research, 15(3), 30–30.Google Scholar
  108. Soysal, G. P., & Krishnamurthi, L. (2012). Demand dynamics in the seasonal goods industry: An empirical analysis. Marketing Science, 31(2), 293–316.CrossRefGoogle Scholar
  109. Suh, K. S., & Lee, Y. E. (2005). The effects of virtual reality on consumer learning: An empirical investigation. Management Information Systems Quarterly, 29, 673–697.CrossRefGoogle Scholar
  110. Terwiesch, C., Olivares, M., Staats, B. R., & Gaur, V. (2019). A review of empirical operations management over the last two decasdes. Manufacturing & Service Operations Management.  https://doi.org/10.1287/msom.2018.0755.
  111. Thomas, J. S., & Sullivan, U. Y. (2005). Managing marketing communications with multichannel customers. Journal of Marketing, 69(4), 239–251.CrossRefGoogle Scholar
  112. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.CrossRefGoogle Scholar
  113. Underhill, P. (2005). Call of the mall: The geography of shopping by the author of why we buy. New York: Simon and Schuster.Google Scholar
  114. Van Doorn, J., & Hoekstra, J. C. (2013). Customization of online advertising: The role of intrusiveness. Marketing Letters, 24(4), 339–351.CrossRefGoogle Scholar
  115. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omnichannel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181.CrossRefGoogle Scholar
  116. Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007). Multichannel customer management: Understanding the research-shopper phenomenon. International Journal of Research in Marketing, 24(2), 129–148.CrossRefGoogle Scholar
  117. Vesanen, J. (2007). What is personalization? A conceptual framework. European Journal of Marketing, 41(5/6), 409–418.CrossRefGoogle Scholar
  118. Visual IQ, Nielsen (2018). How Cookies, Pixels and IDs help you get the data you need. eBook. https://www.visualiq.com/resources/marketing-attribution-newsletter-articles/cookies-tags-and-pixels-tracking-customer-engagement#
  119. Vulcano, G., Van Ryzin, G., & Ratliff, R. (2012). Estimating primary demand for substitutable products from sales transaction data. Operations Research, 60(2), 313–334.CrossRefGoogle Scholar
  120. Wang, K., & Goldfarb, A. (2017). Can offline stores drive online sales? Journal of Marketing Research, 54(5), 706–719.CrossRefGoogle Scholar
  121. Wang, R. J. H., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91(2), 217–234.CrossRefGoogle Scholar
  122. Williams, J. C., Lambert, S., & Kesavan, S. (2018). How the gap used an app to give workers more control over their schedules. Access onlin: https://hbr.org/2017/12/how-the-gap-used-an-app-to-give-workers-more-control-over-their-schedules
  123. Wu, J., & Rangaswamy, A. (2003). A fuzzy set model of search and consideration with an application to an online market. Marketing Science, 22(3), 411–434.CrossRefGoogle Scholar
  124. Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing, 27(3), 262–270.CrossRefGoogle Scholar
  125. Yom-Tov, G. B., Ashtar, S., Altman, D., Natapov, M., Barkay, N., Westphal, M., & Rafaeli, A. (2018, April). Customer sentiment in web-based service interactions: Automated analyses and new insights. In Companion proceedings of the Web Conference 2018 (pp. 1689–1697). International World Wide Web Conferences Steering Committee.Google Scholar
  126. Yuen, K. F., Wang, X., Ng, L. T. W., & Wong, Y. D. (2018). An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Policy, 66, 1–8.CrossRefGoogle Scholar
  127. Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2013). Modeling consumer learning from online product reviews. Marketing Science, 32(1), 153–169.CrossRefGoogle Scholar
  128. Zheng, F. (2016). Spatial Competition and Preemptive Entry in the Discount Retail Industry. Columbia Business School Research Paper (pp. 16–37)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial EngineeringUniversidad de ChileSantiagoChile

Personalised recommendations