Predicting Customer Will

If hindsight is twenty-twenty, foresight–at least in the world of market research–still has a ways to go. Simulation, both with Monte Carlo software and with a conjoint simulation approach, has been used by market researchers for some time now.  Recently David G. Bakken,who maintains a blog on the Smart Data Collective site, pointed out that the drawback of these models is that even those that incorporate random number generation are static. That is, the inputs and the coefficients determine the model outcomes.  
What’s wrong with deterministic models?  Nothing, I gather, except for the limitation that those that are applied to marketing research questions tend to treat the target customers, the companies devising product strategies, and their affiliates in advertising and PR as blocs that make decisions without benefit of individual will. 
Agent-based models, which were born in the social sciences, simulate the interactions of multiple players, each of whom will act, absolutely rationally, in his or her own best interests.  Bakken believes that agent-based modeling used in tandem with traditional risk analysis models or evolutionary programming methods such as genetic algorithms, offers a more dynamic means of accounting for the future behavior of potential customers.  
On the face of it, Bakken’s proposal seems to have merit.  If the technique works for the social sciences, maybe it will work for marketing research.  After all, what is marketing if not a commercial application of social science?

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