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

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. Funding: This work was supported by funding from National Key Research and Development Project [2024YFB3312900], the National Natural Science Foundation of China [Grants 72141305, 72192803 and 72203202], Fundamental Research Funds for the Central Universities (Z. Yuan), and the CKGSB Research Institute (T. Sun). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1138 .