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Predictive competitive intelligence with pre‐release online search traffic

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Predictive competitive intelligence with pre‐release online search traffic. / Schaer, Oliver; Kourentzes, Nikolaos; Fildes, Robert.
In: Production and Operations Management, Vol. 31, No. 10, 31.10.2022, p. 3823-3839.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Schaer, O, Kourentzes, N & Fildes, R 2022, 'Predictive competitive intelligence with pre‐release online search traffic', Production and Operations Management, vol. 31, no. 10, pp. 3823-3839. https://doi.org/10.1111/poms.13790

APA

Schaer, O., Kourentzes, N., & Fildes, R. (2022). Predictive competitive intelligence with pre‐release online search traffic. Production and Operations Management, 31(10), 3823-3839. https://doi.org/10.1111/poms.13790

Vancouver

Schaer O, Kourentzes N, Fildes R. Predictive competitive intelligence with pre‐release online search traffic. Production and Operations Management. 2022 Oct 31;31(10):3823-3839. Epub 2022 Jul 27. doi: 10.1111/poms.13790

Author

Schaer, Oliver ; Kourentzes, Nikolaos ; Fildes, Robert. / Predictive competitive intelligence with pre‐release online search traffic. In: Production and Operations Management. 2022 ; Vol. 31, No. 10. pp. 3823-3839.

Bibtex

@article{1aabdd1cc992491d864cbc428304a537,
title = "Predictive competitive intelligence with pre‐release online search traffic",
abstract = "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.",
keywords = "competitive intelligence, Google trends, market analysis, new product forecasting",
author = "Oliver Schaer and Nikolaos Kourentzes and Robert Fildes",
year = "2022",
month = oct,
day = "31",
doi = "10.1111/poms.13790",
language = "English",
volume = "31",
pages = "3823--3839",
journal = "Production and Operations Management",
issn = "1059-1478",
publisher = "Wiley-Blackwell",
number = "10",

}

RIS

TY - JOUR

T1 - Predictive competitive intelligence with pre‐release online search traffic

AU - Schaer, Oliver

AU - Kourentzes, Nikolaos

AU - Fildes, Robert

PY - 2022/10/31

Y1 - 2022/10/31

N2 - 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.

AB - 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.

KW - competitive intelligence

KW - Google trends

KW - market analysis

KW - new product forecasting

U2 - 10.1111/poms.13790

DO - 10.1111/poms.13790

M3 - Journal article

VL - 31

SP - 3823

EP - 3839

JO - Production and Operations Management

JF - Production and Operations Management

SN - 1059-1478

IS - 10

ER -