AI and Analytics for Business
Research Paper Series
From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data
Managers routinely seek to understand firm performance relative to the competitors. Recently competitive intelligence (CI) has emerged as an important area within business intelligence (BI) where the emphasis is on understanding and measuring a firm’s external competitive environment. A requirement of such systems is the availability of the rich data about a firm’s competitors, which is typically hard to acquire. This paper proposes a method to incorporate competitive intelligence in BI systems by using less granular and aggregate data which is usually easier to acquire. We motivate, develop, and validate an approach to infer key competitive measures about customer activities without requiring detailed cross-firm data. Instead, our method derives these competitive measures for online firms from simple “site-centric” data that are commonly available, augmented with aggregate data summaries that may be obtained from syndicated data providers. Based on data provided by comScore Networks we show empirically that our method performs well in inferring several key diagnostic competitive measures – the penetration, market share and the share of wallet – for various online retailers.
Keywords: Business Intelligence, Competitive Intelligence, Competitive Measures, Probability Models, NBD/Dirichlet