From a press release on TargetRx's website I found the following:
Data collected from physicians via survey are then merged with actual prescribing and other behavioral data and analyzed using proprietary analytic methods to develop predictive models of physician prescribing behavior. The proprietary analytics are based in part on TargetRx's patent-pending Method and
System for Analyzing the Effectiveness of Marketing Strategies. TargetRx
received notice of allowance on this patent application from the U.S. Patent and
Trademark Office in January 2006. The unique method of collecting and analyzing
data enables TargetRx to predict prescribing changes as well as decompose
prescribing to understand what specific aspects of the promotion, product, or
physicians' interactions with patients and payors are causing changes.
I was not able to find any further details. The question is what here is proprietary? What does "unique method of collecting and analyzing" mean? Conducting surveys is not new, so that's not it. Linking the two data sources (survey data and prescribing data) doesn't sound too hard, if there is a unique identifier for each doctor. Has TargetRX developed a new predictive method???
Apropos sales reps and prescribing doctors, an interesting paper1 by Rubin and Waterman, professors of statistics from Harvard and UPenn, discusses the use of "propensity scores" for evaluating causal effects such as marketing interventions. Unlike regression models that cannot prove causality, propensity scores compare matched observations, where matching is based on the multivariate profile of each observation. The application in the paper is a model that ranks the most likely doctors that will increase their prescribing due to a sales rep visit. An ordinary regression model that compares the number of prescriptions of doctors who were visited by sales reps and those who did not cannot account for phenomena such as: sales reps prefer visiting high-prescribing doctors because their compensation is based on the number of prescriptions!
1 "Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology", D B Rubin and R P Waterman (2006), Statistical Science, special issue on "Statistical Challenges and Opportunities in eCommerce", forthcoming.