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Democratizing Data Analytics Products to Drive SME Growth: A Natural Experiment

Author

Listed:
  • Zhe Yuan

    (School of Economics, Zhejiang University, Hangzhou 310058, China; and Center for Research of Private Economy, Zhejiang University, Hangzhou 310058, China; and Future Regional Development Laboratory, Zhejiang University, Hangzhou 310058, China; and Academy of Financial Research, Zhejiang University, Hangzhou 310058, China)

  • Yitong Wang

    (Kogod School of Business, American University, Washington, District of Columbia 20016)

  • Tianshu Sun

    (Cheung Kong Graduate School of Business, Beijing 100006, China)

  • Huilan Xu

    (School of Economics, Zhejiang University, Hangzhou 310058, China)

Abstract

Problem definition : Digital platforms provide sellers with the opportunity to leverage data analytics products, granting them convenient access to statistical analyses of their business data. To promote the adoption of data analytics products and facilitate the growth of small and medium enterprises (SMEs), a world-leading E-commerce platform initiated a “Democratizing Data Analytics” (DDA) campaign in May 2021, which gives free data analytics products access to all sellers on the platform. Leveraging a unique panel data set of over 370,000 sellers and a natural experiment, our study seeks to identify the causal effect of data analytics product adoption on seller performance. Methodology/results : This natural experiment led to a sharp increase in SMEs’ adoption of data analytics. Employing look-ahead propensity score matching (LA-PSM) coupled with difference-in-differences methods, we find that sellers who adopted an analytics product experienced a 14.4% increase in the number of transactions and an 18.8% increase in their revenue. We further explore the rich heterogeneity across sellers and find that younger, smaller sellers, and sellers with fewer cumulative transactions, benefit more from data analytics adoption, highlighting the value of data analytics product adoption for SME growth. Finally, we uncovered the intermediate process and mechanisms underlying such growth by examining how SMEs use data analytics and detailed operational decisions they make. After the adoption of data analytics products, sellers (1) spend more on advertising; (2) edit product titles, descriptions, and images more frequently; and (3) carry more new product stock keeping units and more product categories. Managerial implications : Our research indicates SMEs significantly benefit from adopting data analytics, underscoring the pivotal role these tools play in promoting their growth and development. Meanwhile, platforms or digital economies can increase their gross merchandise volume (GMV) by providing data analytics products to their sellers.

Suggested Citation

  • Zhe Yuan & Yitong Wang & Tianshu Sun & Huilan Xu, 2025. "Democratizing Data Analytics Products to Drive SME Growth: A Natural Experiment," Manufacturing & Service Operations Management, INFORMS, vol. 27(4), pages 1053-1067, July.
  • Handle: RePEc:inm:ormsom:v:27:y:2025:i:4:p:1053-1067
    DOI: 10.1287/msom.2024.1138
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    References listed on IDEAS

    as
    1. Jay Dixon & Bryan Hong & Lynn Wu, 2021. "The Robot Revolution: Managerial and Employment Consequences for Firms," Management Science, INFORMS, vol. 67(9), pages 5586-5605, September.
    2. Felipe Caro & Jérémie Gallien, 2007. "Dynamic Assortment with Demand Learning for Seasonal Consumer Goods," Management Science, INFORMS, vol. 53(2), pages 276-292, February.
    3. Robert E. Carpenter & Bruce C. Petersen, 2002. "Is The Growth Of Small Firms Constrained By Internal Finance?," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 298-309, May.
    4. Maxime C. Cohen, 2018. "Big Data and Service Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1709-1723, September.
    5. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    6. Sagit Bar-Gill & Erik Brynjolfsson & Nir Hak, 2024. "Helping Small Businesses Become More Data-Driven: A Field Experiment on eBay," Management Science, INFORMS, vol. 70(11), pages 7345-7372, November.
    7. Puneet Manchanda & Grant Packard & Adithya Pattabhiramaiah, 2015. "Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Customer Community," Marketing Science, INFORMS, vol. 34(3), pages 367-387, May.
    8. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2024. "Revisiting Event-Study Designs: Robust and Efficient Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3253-3285.
    9. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    10. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    11. Prasanna Tambe & Lorin M. Hitt, 2012. "The Productivity of Information Technology Investments: New Evidence from IT Labor Data," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 599-617, September.
    12. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    13. Shutao Dong & Sean Xin Xu & Kevin Xiaoguo Zhu, 2009. "Research Note ---Information Technology in Supply Chains: The Value of IT-Enabled Resources Under Competition," Information Systems Research, INFORMS, vol. 20(1), pages 18-32, March.
    14. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    15. Hamsa Bastani & Kimon Drakopoulos & Vishal Gupta & Ioannis Vlachogiannis & Christos Hadjichristodoulou & Pagona Lagiou & Gkikas Magiorkinis & Dimitrios Paraskevis & Sotirios Tsiodras, 2021. "Efficient and targeted COVID-19 border testing via reinforcement learning," Nature, Nature, vol. 599(7883), pages 108-113, November.
    16. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    17. Lin William Cong & Xiaohan Yang & Xiaobo Zhang, 2024. "Small and Medium Enterprises Amidst the Pandemic and Reopening: Digital Edge and Transformation," Management Science, INFORMS, vol. 70(7), pages 4564-4582, July.
    18. Erik Brynjolfsson & Lorin Hitt, 1996. "Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending," Management Science, INFORMS, vol. 42(4), pages 541-558, April.
    19. Maxime C. Cohen & Renyu Zhang & Kevin Jiao, 2022. "Data Aggregation and Demand Prediction," Operations Research, INFORMS, vol. 70(5), pages 2597-2618, September.
    20. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    21. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    22. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    23. Wallace J. Hopp & Jun Li & Guihua Wang, 2018. "Big Data and the Precision Medicine Revolution," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1647-1664, September.
    24. Sanjeev Dewan & Chung-ki Min, 1997. "The Substitution of Information Technology for Other Factors of Production: A Firm Level Analysis," Management Science, INFORMS, vol. 43(12), pages 1660-1675, December.
    25. Prasanna Tambe & Xuan Ye & Peter Cappelli, 2020. "Paying to Program? Engineering Brand and High-Tech Wages," Management Science, INFORMS, vol. 66(7), pages 3010-3028, July.
    26. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    27. Ruomeng Cui & Gad Allon & Achal Bassamboo & Jan A. Van Mieghem, 2015. "Information Sharing in Supply Chains: An Empirical and Theoretical Valuation," Management Science, INFORMS, vol. 61(11), pages 2803-2824, November.
    28. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
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