An illustration of Bayes' theorem and its use as a decision‐making aid for competitive intelligence and marketing analysts

Purpose – This paper is intended to enable competitive intelligence practitioners using an important method for everyday work when confronted with conditional uncertainties: the Bayes' theorem. Design/methodology/approach – The paper shows how the mathematical concept of the Bayes theorem applies to competitive intelligence problems. The main approach is to illustrate the concepts by a near‐real world example. The paper also provides background for further reading, especially for psychological problems connected with Bayes' theorem. Findings – The main finding is that conditional uncertainties represent a common problem in competitive intelligence. They should be computed explicitly rather than estimated intuitively. Otherwise, serious misinterpretations and complete project failures might follow. Research limitations/implications – In psychological literature it is a known fact that conditional uncertainties sometimes cannot be handled correctly. Conditional uncertainties seem to be handled well when they are about human properties. This should be verified or falsified in the competitive intelligence context. Practical implications – In general, the application of Bayes' theorem should be seen as one of the foundations of competitive intelligence education. Especially, when it is clear in which intelligence research situations conditional uncertainties can or cannot be handled intuitively, competitive intelligence education and practice should be adapted to these findings. Originality/value – CI practitioners can underestimate the value of Bayes' theorem in practice as they are often unaware of the (psychological) problems around handling conditional uncertainties intuitively. The article demonstrates how to take a computational approach to conditional uncertainties in CI projects. Thus, it can be used as part of appropriate CI training material.