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When Shopbots Meet Emails: Implications for Price Competition on the Internet

Author

Listed:
  • Yuxin Chen

    (New York University)

  • K. Sudhir

    (Yale School of Management)

Abstract

The Internet has dramatically reduced search costs for customers through tools such as shopbots. The conventional wisdom is that this reduction in search costs will increase price competition leading to a decline in prices and profits for online firms. In this paper, we provide an argument for why in contrast to conventional wisdom, competition may be reduced and prices may rise as consumer search costs for prices fall. Our argument has particular appeal in the context of the Internet, where email targeting and the ability to track and record customer behavior are institutional features that facilitate cost effective targeted pricing by firms. We show that such targeted pricing can serve as an effective counterweight to keep average prices high despite the downward pressure on prices due to low search costs. Surprisingly, we find that the effectiveness of targeting itself improves as search costs fall; therefore prices and profits can increase as search costs fall. The intuition for our argument is as follows: Consider a market where consumers are heterogeneous in their loyalty as well as their cost per unit time to search. In the brick and mortar world, it takes consumers a very large amount of time to search across multiple firms. Therefore few customers will search in equilibrium because the gains from search will be relatively small compared to the cost of search. In such a market, a firm will not be able to distinguish whether its customers bought from it due to their high loyalty or due to their unwillingness to search for low prices because of the high search cost. On the Internet, the amount of time to search across multiple stores is minimal (say zero). Now irrespective of their opportunity cost of time, all consumers can search because the time to search is negligible. If in spite of this, a consumer does not search in this environment, she is revealing that her loyalty to the firm that she buys from is very high. The key insight is that as search becomes easy for everyone, then lack of search indicates strong customer loyalty and thus can be used as a proxy to segment the market into loyal and price sensitive segments. Thanks to email technology, firms can selectively set differential prices to different customers, i.e. a high price to the loyal segment and a low price to the price sensitive segment, at relatively low cost. The increased competition due to price transparency caused by low search costs can thus be offset by the ability of firms to price discriminate between their loyal (price insensitive) customers and their price sensitive customers. In fact, we find that it can reduce the extent of competition among the firms and raise their profits. Most surprisingly, the positive effect of targeting on prices improves when search costs fall, because firms can learn more about the differences in customer loyalty, thus improving the effectiveness of targeted pricing. The effectiveness of targeted pricing however is moderated by the extent of opt-in by customers who give their permission for firms to contact them directly by email. Our analysis offers interesting strategic insights for managers about how to address the competitive problems associated with low search costs on the Internet: (1) It suggests that firms should invest in better technologies for personalization and targeted pricing so as to prevent the Internet from becoming a competitive minefield that destroys firm profitability. In fact we show that low search costs can facilitate better price personalization and can thus aid in improving the effectiveness of targeted pricing efforts. (2) The analysis also offers guidelines for online customer acquisition efforts. The critical issue for competitive advantage is not in increasing market share per se, but in increasing the loyalty of customers. While a larger share of very loyal customers reduces competitive intensity, surprisingly a larger share of customers who are not very loyal can be a competitive disadvantage. In order for customer acquisition to be profitable, it should be accompanied by a superior product or service that can ensure high loyalty. (3) Investing in online privacy initiatives that assures consumers that their private information will not be abused other than to offer them "deals" is worthwhile. Such assurances will encourage consumers to opt into firm mailing lists. This facilitates successful targeting which in turn ameliorates the competitive threats due to low search costs on the Internet. (4) When the overwhelming majority of customers are satisfied with online privacy, the remaining privacy conscious customers who are not willing to pay a higher price to maintain their privacy will be left out of the market. While this may be of some concern to privacy advocates, it is interesting that total consumer welfare can be higher even if some consumers are left out of the market. Our analysis captures the competitive implications of the interaction between two institutions facilitated by the Internet: Shopbots and Emails. But the research question addressed is more fundamental: What is the nature of competition in an environment with low costs for both consumer search and firm-to-consumer personalized communications? The strategic insights obtained in the paper may be beneficially applied even to offline businesses that can replicate such an environment. For example, offline firms could have websites on which they post prices allowing for easy price comparisons. They could also use tools such as frequency programs to create addressable databases that enable them to communicate with customers by direct mail and email (as many airlines and stores do).

Suggested Citation

  • Yuxin Chen & K. Sudhir, 2002. "When Shopbots Meet Emails: Implications for Price Competition on the Internet," Review of Marketing Science Working Papers 1-3-1007, Berkeley Electronic Press.
  • Handle: RePEc:bep:rmswpp:1-3-1007
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    References listed on IDEAS

    as
    1. J. Yannis Bakos, 1997. "Reducing Buyer Search Costs: Implications for Electronic Marketplaces," Management Science, INFORMS, vol. 43(12), pages 1676-1692, December.
    2. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Individual Marketing with Imperfect Targetability," Marketing Science, INFORMS, vol. 20(1), pages 23-41, November.
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    Cited by:

    1. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    2. Bilal AFSAR & Jawaria Andleeb QURESHI & Asim REHMAN & Rehmat Ullah BANGASH, 2011. "Consumer Panacea Over Internet Usage In Pakistan," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(1), pages 43-52, May.
    3. Erik Brynjolfsson & Astrid Dick & Michael Smith, 2010. "A nearly perfect market?," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 1-33, March.
    4. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    5. Maarten C. W. Janssen & Marielle C. Non, 2009. "Going Where the Ad Leads You: On High Advertised Prices and Searching Where to Buy," Marketing Science, INFORMS, vol. 28(1), pages 87-98, 01-02.
    6. Zheyin (Jane) Gu & Yunchuan Liu, 2013. "Consumer Fit Search, Retailer Shelf Layout, and Channel Interaction," Marketing Science, INFORMS, vol. 32(4), pages 652-668, July.
    7. Dutta, Champa Bati & Das, Debasish Kumar, 2017. "What drives consumers' online information search behavior? Evidence from England," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 36-45.
    8. V. Kumar & Ashutosh Dixit & Rajshekar (Raj) G. Javalgi & Mayukh Dass, 2016. "Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 24-45, January.
    9. Raj, S.P. & Rhee, Byong-Duk & Sivakumar, K., 2020. "Manufacturer adoption of a unilateral pricing policy in a multi-channel setting to combat customer showrooming," Journal of Business Research, Elsevier, vol. 110(C), pages 104-118.
    10. Jie Jennifer Zhang & Bing Jing, 2007. "The Impacts of Shopbots on Online Consumer Search," Working Papers 07-34, NET Institute, revised Sep 2007.

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    More about this item

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • L0 - Industrial Organization - - General
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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