GOALI: Optimization of the marketing mix in the health care industry, with a view to reducing consumer costs

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This project employs data mining techniques to model the return on investment from various types of promotional spending to market a drug, and then uses the model to draw conclusions on how the pharmaceutical industry might go about allocating marketing expenditures in a more efficient manner, thus reducing costs to the consumer. First, a model is built for the output variable (typically new prescriptions in a given time period) in terms of a number of relevant independent variables. In this model building phase, attention is focused on issues such as the choice of the best set of variables to use in the model, as well as the best ways to measure predictive power, while taking full account of the time series nature of the data. Techniques such as MARS and MART (Multiple Adaptive Regression Splines and Trees, respectively) are tested. To handle the problem of correlated predictors, models are considered and compared that rely on partial least squares regression with or without the help of genetic algorithms or other algorithms to select the most predictive set of variables, as well as mixed models (with fixed and random effects). Extensions of the ridge regression method such as LASSO (Least Absolute Shrinkage and Selection Operator) and LARS (Least Angle Regression laSso) are tested. Directed Acyclic Graphs are employed to help unravel direct and indirect effects of predictors on new prescriptions. Another approach to be tested is that of using propensity score methods to improve on the industry practice of estimating effects of various marketing variables with matched samples. Once built, the model is used to evaluate the contribution of each marketing activity to the new prescriptions. Once these contributions have been ascertained, simulations follow to test the effect of changes in the modeling mix on the expected prescription volume. Linear or quadratic programming is then put in place to propose an optimal marketing mix, using as an objective function the equation obtained from the model. Actionable recommendations can then be given to the pharmaceutical industry on how to achieve savings from a better optimized marketing mix. To summarize, the project proceeds in three phases. 1) A model is built for the number of new prescriptions to a drug in a given time period in terms of a number of relevant predictors, such as for example spending on promotional samples, or spending on journal advertising. 2) The model is then used to evaluate the contribution of each marketing activity to the new prescriptions and to define an optimal marketing mix. 3) Actionable recommendations to the pharmaceutical industry are then derived on how to achieve savings from a better optimized marketing mix. The project relies on strong synergies between the PI at a business university and a corporate co-PI with years of experience providing actionable database marketing advice to clients. The project will also provide valuable corporate exposure to a PhD student. Results from the project are expected to help lower the cost of drugs to the consumer and more generally to help control health care costs.