The Impact of Big Data on Firm Performance: An Empirical Investigation

Abstract

We examine the impact of “big data” on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.

Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

“…One consideration within this vast topic is a (hypothesized) positive feedback loop: more data lead to more data-driven insights, allowing a company to serve its customers better, to attract more customers and, in turn, to collect even more data. Nevertheless, there seems to be a broad consensus that data are generally governed by decreasing returns to scale, like any other production factor (Varian 2018;Bajari et al 2019).…”

“…In this paper, we will limit ourselves to a short discussion of how the number of observations affects the accuracy achieved by the forecasting and inference methods used by the FP&A department within the frame of our simulation. For empirical results, we refer interested readers to Bajari et al (2019), which contains a study of the performance of Amazon's retail forecasting system. The study finds performance gains in the time dimension (i.e., from longer data history), but not in the product dimension (i.e., panel data forecasts do not improve with more products within a category).…”

Digital Platforms and Antitrust

“…The relationship between value creation and data also depends on the specific market and the advancement of sectoral-specific applications of machine learning and artificial intelligence. Bajari et al (2019) look at the effect of increasing the size of data sample on accuracy in the context of Amazon's retail demand forecasting system. They feed their statistical models with data along two dimensions: the number of products N in the same category and the number of periods T for which a particular product has been on sale.…”

Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

“…Our project also contributes to the literature on the value of consumer data to firms (Rossi et al 1996, Trusov et al 2016, Miller & Skiera 2017, Bajari et al 2019, Aridor et al 2021, Rafieian & Yoganarasimhan 2021, Sun et al 2021, Wernerfelt et al 2022, Lei et al 2023 forthcoming) by highlighting the importance of considering data composition when evaluating its value. Iansiti (2021) theorizes that the marginal value of data is initially very high as firms try to overcome the model's "cold-start" phase.…”