IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v22y2024i4d10.1007_s11129-024-09282-3.html
   My bibliography  Save this article

Investigating complementarities in subscription software usage using advertising experiments

Author

Listed:
  • Jon Zeller

    (Stanford Graduate School of Business)

  • Sridhar Narayanan

    (Stanford Graduate School of Business)

Abstract

In this study, we examine complementarities in usage across a set of related software products from a multi-product firm. We employ a novel experimental approach to causally estimate complementarities, leveraging rich usage data and advertising experiments that directly affect the usage of only one product at a time to measure complementarities based on consumption rather than purchase. Our approach is particularly useful as digital contexts are characterized by the simultaneous presence of both substitutability and complementarity between products. They also have scant price variation, bundled pricing plans, and infrequent purchase or subscription renewal decisions, often making typical cross-price elasticity measures for complementarities infeasible. We apply our approach to data from a software company with a suite of related products and find evidence for varying degrees of complementarity across both user groups and products. We show that accounting for complementarities significantly affects the measurement of ad effectiveness and may impact ad targeting decisions by the firm. We explore heterogeneity in complementarities, finding that they are larger for users who have used the products heavily in the past, but small or zero for those who have not. Ours is one of the first studies to causally examine complementarity in usage in the context of subscription products, and our identification strategy can be applied to a variety of contexts.

Suggested Citation

  • Jon Zeller & Sridhar Narayanan, 2024. "Investigating complementarities in subscription software usage using advertising experiments," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 389-443, December.
    • Handle: RePEc:kap:qmktec:v:22:y:2024:i:4:d:10.1007_s11129-024-09282-3
    DOI: 10.1007/s11129-024-09282-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-024-09282-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11129-024-09282-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Randall Lewis & David Reiley, 2014. "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 235-266, September.
    2. Steve Berry & Ahmed Khwaja & Vineet Kumar & Andres Musalem & Kenneth Wilbur & Greg Allenby & Bharat Anand & Pradeep Chintagunta & W. Hanemann & Przemek Jeziorski & Angelo Mele, 2014. "Structural models of complementary choices," Marketing Letters, Springer, vol. 25(3), pages 245-256, September.
    3. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    4. Nedungadi, Prakash, 1990. "Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(3), pages 263-276, December.
    5. Milgrom, Paul & Shannon, Chris, 1994. "Monotone Comparative Statics," Econometrica, Econometric Society, vol. 62(1), pages 157-180, January.
    6. R. Venkatesh & Wagner Kamakura, 2003. "Optimal Bundling and Pricing under a Monopoly: Contrasting Complements and Substitutes from Independently Valued Products," The Journal of Business, University of Chicago Press, vol. 76(2), pages 211-232, April.
    7. Tetyana Kosyakova & Thomas Otter & Sanjog Misra & Christian Neuerburg, 2020. "Exact MCMC for Choices from Menus—Measuring Substitution and Complementarity Among Menu Items," Marketing Science, INFORMS, vol. 39(2), pages 427-447, March.
    8. Hongju Liu & Pradeep K. Chintagunta & Ting Zhu, 2010. "Complementarities and the Demand for Home Broadband Internet Services," Marketing Science, INFORMS, vol. 29(4), pages 701-720, 07-08.
    9. Gregory S. Crawford & Ali Yurukoglu, 2012. "The Welfare Effects of Bundling in Multichannel Television Markets," American Economic Review, American Economic Association, vol. 102(2), pages 643-685, April.
    10. Arora, Ashish & Forman, Chris & Yoon, Ji Woong, 2010. "Complementarity and information technology adoption: Local area networks and the Internet," Information Economics and Policy, Elsevier, vol. 22(3), pages 228-242, July.
    11. Ludovic Stourm & Raghuram Iyengar & Eric T. Bradlow, 2020. "A Flexible Demand Model for Complements Using Household Production Theory," Marketing Science, INFORMS, vol. 39(4), pages 763-787, July.
    12. Jeremy T. Fox & Natalia Lazzati, 2017. "A note on identification of discrete choice models for bundles and binary games," Quantitative Economics, Econometric Society, vol. 8(3), pages 1021-1036, November.
    13. Jean-Pierre H. Dubé, 2018. "Microeconometric Models of Consumer Demand," NBER Working Papers 25215, National Bureau of Economic Research, Inc.
    14. Kenneth E. Train & Daniel L. McFadden & Moshe Ben-Akiva, 1987. "The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices," RAND Journal of Economics, The RAND Corporation, vol. 18(1), pages 109-123, Spring.
    15. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    16. Milgrom, Paul & Roberts, John, 1990. "The Economics of Modern Manufacturing: Technology, Strategy, and Organization," American Economic Review, American Economic Association, vol. 80(3), pages 511-528, June.
    17. Bhat, Chandra R. & Castro, Marisol & Pinjari, Abdul Rawoof, 2015. "Allowing for complementarity and rich substitution patterns in multiple discrete–continuous models," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 59-77.
    18. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
    19. Matthew Gentzkow, 2007. "Valuing New Goods in a Model with Complementarity: Online Newspapers," American Economic Review, American Economic Association, vol. 97(3), pages 713-744, June.
    20. Andrew Ainslie & Peter E. Rossi, 1998. "Similarities in Choice Behavior Across Product Categories," Marketing Science, INFORMS, vol. 17(2), pages 91-106.
    21. Xiao Liu & Timothy Derdenger & Baohong Sun, 2018. "An Empirical Analysis of Consumer Purchase Behavior of Base Products and Add-ons Given Compatibility Constraints," Marketing Science, INFORMS, vol. 37(4), pages 569-591, August.
    22. Lewbel, Arthur, 1985. "Bundling of substitutes or complements," International Journal of Industrial Organization, Elsevier, vol. 3(1), pages 101-107, March.
    23. Navdeep S. Sahni & Dan Zou & Pradeep K. Chintagunta, 2017. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Management Science, INFORMS, vol. 63(8), pages 2688-2705, August.
    24. Sanghak Lee & Jaehwan Kim & Greg M. Allenby, 2013. "A Direct Utility Model for Asymmetric Complements," Marketing Science, INFORMS, vol. 32(3), pages 454-470, May.
    25. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, November.
    26. S. Sriram & Pradeep K. Chintagunta & Manoj K. Agarwal, 2010. "Investigating Consumer Purchase Behavior in Related Technology Product Categories," Marketing Science, INFORMS, vol. 29(2), pages 291-314, 03-04.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    2. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    3. Jean-Pierre H. Dubé, 2018. "Microeconometric Models of Consumer Demand," NBER Working Papers 25215, National Bureau of Economic Research, Inc.
    4. Tao Sun, 2024. "Bundle Choice Model with Endogenous Regressors: An Application to Soda Tax," Papers 2412.05794, arXiv.org.
    5. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
    6. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Discrete Choice Models for Bundles," Discussion Papers Series 625, School of Economics, University of Queensland, Australia.
    7. Alessandro Iaria, & Wang, Ao, 2021. "An Empirical Model of Quantity Discounts with Large Choice Sets," The Warwick Economics Research Paper Series (TWERPS) 1378, University of Warwick, Department of Economics.
    8. Baranchuk, Nina & Kieschnick, Robert & Moussawi, Rabih, 2014. "Motivating innovation in newly public firms," Journal of Financial Economics, Elsevier, vol. 111(3), pages 578-588.
    9. Steve Berry & Ahmed Khwaja & Vineet Kumar & Andres Musalem & Kenneth Wilbur & Greg Allenby & Bharat Anand & Pradeep Chintagunta & W. Hanemann & Przemek Jeziorski & Angelo Mele, 2014. "Structural models of complementary choices," Marketing Letters, Springer, vol. 25(3), pages 245-256, September.
    10. Bryan Bollinger & Naim Darghouth & Kenneth Gillingham & Andres Gonzalez-Lira, 2023. "Valuing Technology Complementarities: Rooftop Solar and Energy Storage," NBER Working Papers 32003, National Bureau of Economic Research, Inc.
    11. Iaria, Alessandro & ,, 2020. "Inferring Complementarity from Correlations rather than Structural Estimation," CEPR Discussion Papers 14273, C.E.P.R. Discussion Papers.
    12. Pellegrini, Andrea & Pinjari, Abdul Rawoof & Maggi, Rico, 2021. "A multiple discrete continuous model of time use that accommodates non-additively separable utility functions along with time and monetary budget constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 37-53.
    13. Takanori Adachi & Takeshi Ebina & Makoto Hanazono, 2017. "Endogenous Product Boundary," Manchester School, University of Manchester, vol. 85(1), pages 13-40, January.
    14. Yao Luo, 2023. "Bundling and nonlinear pricing in telecommunications," RAND Journal of Economics, RAND Corporation, vol. 54(2), pages 268-298, June.
    15. Fu Ouyang & Thomas T. Yang, 2023. "Semiparametric Discrete Choice Models for Bundles," Papers 2306.04135, arXiv.org, revised Nov 2023.
    16. Kidokoro, Yukihiro, 2016. "A micro foundation for discrete choice models with multiple categories of goods," Journal of choice modelling, Elsevier, vol. 19(C), pages 54-72.
    17. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
    18. Richards, Timothy J. & Hamilton, Stephen F. & Yonezawa, Koichi, 2018. "Retail Market Power in a Shopping Basket Model of Supermarket Competition," Journal of Retailing, Elsevier, vol. 94(3), pages 328-342.
    19. Kim, Chul & Smith, Adam N. & Kim, Jaehwan & Allenby, Greg M., 2023. "Outside good utility and substitution patterns in direct utility models," Journal of choice modelling, Elsevier, vol. 49(C).
    20. Pereira, Pedro & Ribeiro, Tiago & Vareda, João, 2013. "Delineating markets for bundles with consumer level data: The case of triple-play," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 760-773.

    More about this item

    Keywords

    Complementarities; Subscription goods; Digital goods; Advertising; Causal effects; Field experiments;
    All these keywords.

    JEL classification:

    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:22:y:2024:i:4:d:10.1007_s11129-024-09282-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.