Predictive competitive intelligence with prerelease online search traffic

• 成果类型: Article
• 署名作者: Schaer, Oliver; Kourentzes, Nikolaos; Fildes, Robert
• 署名单位: University of Virginia; University of Skovde; Lancaster University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13790
• 发表日期: 2022
• 页码: 3823-3839
• 关键词: competitive intelligence; Google Trends; market analysis; new product forecasting

摘要

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.