Building marketing models that make money

Building models that help marketers make productive decisions and that they actually use is hard. I learned some lessons in my 30+ years of building and applying models for sizing and deploying sales forces, for estimating brand health, and for estimating the impact on revenue of marketing mix and of the attractiveness to consumers of product attributes. One is that building scientific models that improve productivity is an art. Other lessons include these: to balance model complexity versus ease of understanding and estimation; to involve managers in any subjective estimates for models they will implement; to make measures available to managers when they need them and at the level of organization they need; to use the predictive validity of a hold-out sample to persuade managers of a model's credibility; and to recognize that even for empirical models, subjective estimates about the future may be necessary. Productive marketing models may have different attributes than those published in prestigious academic journals.