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Predictive competitive intelligence with prerelease online search traffic
Darden School of Business, University of Virginia, Charlottesville, Virginia, USA.ORCID iD: 0000-0003-1878-8134
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-0211-5218
Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, Lancaster, UK.ORCID iD: 0000-0002-5918-7098
2022 (English)In: Production and operations management, ISSN 1059-1478, E-ISSN 1937-5956, Vol. 31, no 10, p. 3823-3839Article in journal (Refereed) Published
Abstract [en]

In today's competitive market environment, it is vital for companies to gain insight about competitors' new product launches. Past studies have demonstrated the predictive value of prerelease online search traffic (PROST) for new product forecasting. Relying on these findings and the public availability of PROST, we investigate its usefulness for estimating sales of competing products. We propose a model for predicting the success of competitors' product launches, based on own past product sales data and competitor's prerelease Google Trends. We find that PROST increases predictive accuracy by more than 18% compared to models that only use internally available sales data and product characteristics of video game sales. We conclude that this inexpensive source of competitive intelligence can be helpful when managing the marketing mix and planning new product releases.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022. Vol. 31, no 10, p. 3823-3839
Keywords [en]
competitive intelligence, Google Trends, market analysis, new product forecasting
National Category
Software Engineering Other Mathematics Business Administration
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-21665DOI: 10.1111/poms.13790ISI: 000830333000001Scopus ID: 2-s2.0-85134801825OAI: oai:DiVA.org:his-21665DiVA, id: diva2:1686064
Note

CC BY 4.0

© 2022 The Authors. Production and Operations Management published by Wiley Periodicals LLC on behalf of Production and Operations Management Society.

First published: 24 June 2022

Correspondence

Oliver Schaer, Darden School of Business, University of Virginia, Charlottesville, VA 22903, USA. Email: schaero@darden.virginia.edu

Available from: 2022-08-08 Created: 2022-08-08 Last updated: 2022-10-19Bibliographically approved

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Kourentzes, Nikolaos

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