The first problem is with marketing itself -- it isn't a hard science, and as such, outcomes can fall well outside of the historical range of expected outcomes. You may say well, I'll just have to take into account the unknowns then. But the thing about unknowns is just that -- they're unknown. You can try to anticipate them, but there's no way you can account for all of them. A lot of the things people build into those models are known unknowns. But the reality of unknowns is really more like Murphy's Law.
You can't anticipate every unknown. And you have to account for that when you're leaning on predictive analytics.
I've made predictive analytics sound pretty dubious thus far, but we shouldn't throw the baby out with the bathwater. Using predictive analytics is flawed when you use it to build up complex models, and then base your marketing decisions off of those models with the expectation of 100% accuracy. You can't divorce yourself from common sense. That's why probabilistic analytics are a better bet than predictive analytics. You account for the variables you can anticipate, use that data to make smart decisions, but don't guarantee specific results.
► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).