Whilst the 20th Century was dominated by classical (or what we prefer to call ‘Frequentist’) statistics the last decade has seen a resurgence of interest in ‘Bayesian’ approaches to determining probability. This has particularly been the case in marketing, where ‘Bayesian’ approaches are increasingly being sought across the many areas where modelling can provide a competitive advantage. So what has an approach inspired by a 250 year old theorem got to do with modern, omnichannel marketing?
The interest reflects something of a paradigm shift in the way we view probability which is particularly appealing to marketers. Marketers in many sectors today are facing competition from some previously unlikely sources; they are facing greater complexity in the choice of tools in their marketing armoury due to the explosion of digital; and they are facing greater need for accountability. To accommodate these demands, they are looking for a way of making more informed decisions, but in the real-world are often faced with a paucity of data.
In reality, marketers are rarely making decisions in a vacuum and have a wealth of previous knowledge and experience to draw on. A ‘Bayesian’ approach enables these views on the underlying (and often incomplete) data they are presented with to be taken into account rather than simply relying on counting what is there.
For instance, market researchers relying on traditional statistical approaches often report that predictions are too aggressive and unrealistic. Their model might suggest, for example, that a price of reduction of, say, 10% or 20% would result in an increase in market share that experience tells the marketer is too large. When uncertainty is taken into consideration, market share predictions become more realistic and tend to agree with current and past experience. ‘Bayesian’ approaches therefore offer a more elegant solution to an important problem.
If marketing is really about understanding human behaviour, then ‘Bayesian’ approaches enable us to study the complexities of behaviour in a more realistic fashion than was previously possible.
We’ve got lots more to say on the subject of ‘Bayesian’, which is why we’ve produced a guide which you can download from the link below.