Complementarities in learning from data: insights from general search

Maximilian Schäfer () and Geza Sapi
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Maximilian Schäfer: IMT-BS - DEFI - Département Droit, Économie et Finances - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]
Geza Sapi: European Commission [Brussels]
Authors registered in the RePEc Author Service: Maximilian Schaefer

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Abstract: The ability to make accurate predictions relating to consumer preferences is a key factor of a digital firm's success. Examples include targeted advertisements and, more broadly, business models relying on capturing consumers' attention. The prediction technologies used to learn consumer preferences rely on consumer generated data. Despite the importance of data-driven technologies, there is a lack of knowledge about the precise role that data-scale plays for prediction accuracy. From a policy perspective, a better understanding about the role of data is needed to assess the risks that "big data" might pose for competition. This article highlights potential complementarities between different data dimensions in algorithmic learning. We analyze our hypothesis using search engine data from Yahoo! and provide evidence that more data in the within-user dimension enhances the efficiency of algorithmic learning in the across-user dimension. Our findings suggest that ignoring these complementarities might lead to underestimating scale advantages from data.

Keywords: Sustainable; Development; Goals (search for similar items in EconPapers)
Date: 2023-12
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Citations: View citations in EconPapers (2)

Published in Information Economics and Policy, 2023, 65, pp.101063. ⟨10.1016/j.infoecopol.2023.101063⟩

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Journal Article: Complementarities in learning from data: Insights from general search (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04261926

DOI: 10.1016/j.infoecopol.2023.101063

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