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How a little math magic can be used to predict better performance.
Condensed...
SiriusDecisions research indicates that b-to-b buyers complete 67 percent of their journey online. The sales rep has less of a role in these early stages. As a result, marketing analysis has shifted from modeling the sales rep’s interaction with buyers to measuring marketing’s online interaction with the buyer, making it possible to predict the future behavior of that buyer or similar buyers.
[B-to-B marketers] are using mix modeling techniques to identify the best next tactic to present to a buyer as she or he moves through the sales cycle. These models are used to calculate the likelihood of progression through the sales cycle given an optimal set of tactics.
We estimate that 10 to 15 percent of b-to-b marketers (large companies with the infrastructure and resources to make investments in analytics) are using these methods. However, mix modeling is important to smaller companies as well, for a couple of reasons. First, it’s coming your way. You probably have the data for this kind of analysis, but if you don’t, it’s most likely because you are not collecting it. Second, companies are beginning to see this as an area of competitive differentiation. Now is a good time to take stock of the data available to you and start collecting information for predictive modeling.
This is an important heads up for all of us, and probably a very smart starting point for your scientific analysis: what content works best in the early stages of the buying process (esp. since the sales rep plays a small role during the beginning of the sales cycle). What are your mix modeling techniques? Let's incorporate mix modeling into our vernacular.