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Market segmentation in online platforms

Author

Listed:
  • Berbeglia, Franco
  • Berbeglia, Gerardo
  • Van Hentenryck, Pascal

Abstract

This paper studies ranking policies in a stylized trial-offer marketplace model, in which a single firm offers multiple products and has consumers who express heterogeneous preferences. Consumer trials are influenced by past purchases, the inherent appeal of the products, and the ranking of each product. Consumer purchases conditional on trying the product are dependent on the inherent quality for the given consumer segment. The platform owner needs to devise a ranking policy to display the products to maximize the number of purchases in the long run, and to decide whether to display the number of past purchases. The model proposed attempts to understand the impact of market segmentation in a trial-offer market with position bias and social influence. Under our model, consumer choices are based on a very general choice model known as the mixed multinomial logit model, which embeds product appeal, ranking, and past purchases into the taste parameters. We analyze the long-term dynamics of this highly complex stochastic model and we quantify the expected benefits of market segmentation as well as the value of social influence. When past purchases are displayed, consumer heterogeneity makes buyers try the sub-optimal products, reducing the overall sales rate. We show that consumer heterogeneity makes the ranking problem NP-hard. We then analyze the benefits of market segmentation. We find tight bounds to the expected benefits of offering a distinct ranking to each consumer segment. Finally, we show that the market segmentation strategy always benefits from social influence when the average quality ranking is used. One of the managerial implications is that the firm is better off using an aggregate ranking policy when the variety of consumer preference is limited, but it should perform a market segmentation policy when consumers are highly heterogeneous. We also show that this result is robust to relatively small consumer classification mistakes; when these are large, an aggregate ranking is preferred.

Suggested Citation

  • Berbeglia, Franco & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2021. "Market segmentation in online platforms," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1025-1041.
    • Handle: RePEc:eee:ejores:v:295:y:2021:i:3:p:1025-1041
    DOI: 10.1016/j.ejor.2021.03.056
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    References listed on IDEAS

    as
    1. Anderson, Chris K. & Xie, Xiaoqing, 2014. "Pricing and market segmentation using opaque selling mechanisms," European Journal of Operational Research, Elsevier, vol. 233(1), pages 263-272.
    2. Maldonado, Felipe & Van Hentenryck, Pascal & Berbeglia, Gerardo & Berbeglia, Franco, 2018. "Popularity signals in trial-offer markets with social influence and position bias," European Journal of Operational Research, Elsevier, vol. 266(2), pages 775-793.
    3. Lutz, Christoph & Newlands, Gemma, 2018. "Consumer segmentation within the sharing economy: The case of Airbnb," Journal of Business Research, Elsevier, vol. 88(C), pages 187-196.
    4. Janson, Svante, 2004. "Functional limit theorems for multitype branching processes and generalized Pólya urns," Stochastic Processes and their Applications, Elsevier, vol. 110(2), pages 177-245, April.
    5. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    6. Coco Krumme & Manuel Cebrian & Galen Pickard & Sandy Pentland, 2012. "Quantifying Social Influence in an Online Cultural Market," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    7. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    8. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    9. Andrés Abeliuk & Gerardo Berbeglia & Manuel Cebrian & Pascal Van Hentenryck, 2015. "The Benefits of Social Influence in Optimized Cultural Markets," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    10. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    11. Zhen, Xueping & (George) Cai, Gangshu & Song, Reo & Jang, Sungha, 2019. "The effects of herding and word of mouth in a two-period advertising signaling model," European Journal of Operational Research, Elsevier, vol. 275(1), pages 361-373.
    12. Lee, Haengju & Eun, Yongsoon, 2020. "Discovering heterogeneous consumer groups from sales transaction data," European Journal of Operational Research, Elsevier, vol. 280(1), pages 338-350.
    13. Molinero, Xavier & Riquelme, Fabián & Serna, Maria, 2015. "Cooperation through social influence," European Journal of Operational Research, Elsevier, vol. 242(3), pages 960-974.
    14. Ram Gopal & Hooman Hidaji & Raymond A. Patterson & Erik Rolland & Dmitry Zhdanov, 2016. "Design Improvements for Message Propagation in Malleable Social Networks," Production and Operations Management, Production and Operations Management Society, vol. 25(6), pages 993-1005, June.
    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. Paat Rusmevichientong & David Shmoys & Chaoxu Tong & Huseyin Topaloglu, 2014. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 2023-2039, November.
    17. Guillermo Gallego & Anran Li & Van-Anh Truong & Xinshang Wang, 2020. "Approximation Algorithms for Product Framing and Pricing," Operations Research, INFORMS, vol. 68(1), pages 134-160, January.
    18. Andrés Abeliuk & Gerardo Berbeglia & Manuel Cebrian & Pascal Van Hentenryck, 2016. "Assortment optimization under a multinomial logit model with position bias and social influence," 4OR, Springer, vol. 14(1), pages 57-75, March.
    19. Engström, Per & Forsell, Eskil, 2018. "Demand effects of consumers’ stated and revealed preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 43-61.
    20. Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
    21. Gallego, Guillermo & Li, Anran & Truong, Van-Anh & Wang, Xinshang, 2020. "Approximation algorithms for product framing and pricing," LSE Research Online Documents on Economics 101983, London School of Economics and Political Science, LSE Library.
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