The success of any Business Intelligence project is contingent upon people, not technology. Analysts and end users must work in concert to ask a concise question, identify the data available to answer that question and, validate interpretation of analytic outputs in context of the business environment. From there, the subject-matter experts (statisticians, data analysts, data miners, etc.) must be allowed the freedom to draw upon their breadth of knowledge and experience to select the best methodology for the job.
I cannot tell you how many times a business unit manager has come up to me and with all of the confidence of a just-learned-to-stand toddler and declared “I need a model!” “Really?” I respond. What type of model? Logistic? Linear? What kind of data do you have for me to work with?, and a plethora of other rather technical questions. My point is that predictive models have been used quite successfully in marketing for many years. In a business environment where “half of the organizations surveyed do not take advantage of analytics to help them target, service, or interact with customers” according to Accenture’s Customer Analytics survey, predictive models have gained the esteem and notoriety akin to Steve Jobs.