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

该项目使用数据挖掘技术对药品营销的各种促销支出的投资回报进行建模，然后使用该模型得出结论，说明制药业如何以更有效的方式分配营销支出，从而降低消费者的成本。首先，根据若干相关的自变量为输出变量(通常是给定时间段内的新处方)建立模型。在这一建立模型阶段，重点关注的问题包括选择模型中使用的最佳变量集，以及衡量预测能力的最佳方法，同时充分考虑到数据的时间序列性质。测试了MARS和MART(分别为多个自适应回归样条线和树)等技术。为了处理相关预测因素的问题，考虑和比较了依赖偏最小二乘回归的模型，无论是否借助遗传算法或其他算法来选择最具预测性的变量集，以及混合模型(具有固定和随机影响)。测试了岭回归方法的扩展，如LASSO(最小绝对收缩和选择算子)和LARS(最小角度回归LASSO)。有向无环图被用来帮助解开预测者对新处方的直接和间接影响。另一种待测试的方法是使用倾向计分方法来改进行业实践，即使用匹配的样本来估计各种营销变量的影响。一旦建立，该模型就被用来评估每一项营销活动对新处方的贡献。一旦确定了这些贡献，接下来就进行模拟，以测试建模组合中的变化对预期处方数量的影响。然后应用线性规划或二次规划来建议最优营销组合，使用从模型中获得的方程作为目标函数。然后，就如何从更好的优化营销组合中实现节省，向制药业提出可行的建议。总而言之，该项目分三个阶段进行。1)根据一些相关的预测因素，例如促销样品的支出或期刊广告的支出，建立了给定时间段内一种药物的新处方数量的模型。2)然后使用该模型来评估每种营销活动对新处方的贡献，并确定最优营销组合。3)然后就如何通过更好的优化营销组合实现节约，向制药业提出了可行的建议。该项目依赖于一所商业大学的PI和一家拥有多年向客户提供可行的数据库营销建议的企业合作PI之间的强大协同效应。该项目还将为博士生提供宝贵的企业曝光率。该项目的结果预计将有助于降低消费者的药品成本，更广泛地说，有助于控制医疗成本。

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.