For the rest of the August 2009 issue of CRM magazine, please click here.
[This article is an online-only sidebar to the feature, "Healing the Sick."]
"The pharma business model up until three to four years ago was highly successful. The noble mission was to develop life changing drugs -- and it was successful in its mission into the last three-to-four decades," contends Patrick Homer, a global practice principal in the pharma vertical for SAS Institute.
Homer says the pharma industry is now at a tipping point and it's due to two factors. Number one, we are reaching what Homer calls the "patent cliff" in which a number of products in the market will lose patents. Secondly, pharma is, like all other industries, scaling back due to economic pressures. Salesforces are dwindling and pharma companies must go to market with much smaller margins. Number three, the channels of delivery are changing. Pharma is moving more toward Web-based, social media channels.
To address these trends, pharmaceutical companies must do more with less and reshape their messages -- not the easiest of tasks. As return on investment (ROI) drops, Homer advocates a movement from historical analysis to predictive modeling. SAS Physician Targeting uses predictive analytics to apply factors in the decision process of which physicians to call upon. To illustrate, with "physician retention" within the predictive model, users can identify behaviors that physicians exhibit when they are apt to move away from a brand.
SAS combines CRM data with predictive models to explore the factors physicians consider when signing on with products. Homer reveals that after putting the physician targeting solution in place, one pharma company saw a 27 percent higher response rate to its promotions. The predictive analytics space is young, however. Homer says only the best of the best are doing such modeling with their marketing projects. And despite huge result patterns, it's still very much a tool for visionaries in the space.
Gartner analyst Dale Hagemeyer writes of the benefits of predictive analytics in the life sciences space. "For the past six or so years, life science manufacturers' sales representatives have had decreased access to physicians," he states in a report about the aforementioned vertical. Hagemeyer writes of the benefits of modeling for not only predicting responses of physicians, but also in managing and distinguishing key opinion leaders. Key opinion leaders inherently dictate to practitioners -- in a sense having more of an impact upon decisions than if a physician is targeted directly. "Managing relationships with key opinion leaders in the life science industry is a rapidly emerging and highly sought-after set of business processes," Hagemeyer writes. So, just as organizations invest in customer relationship management, life sciences companies must soon focus on managing key opinion leader relationships.
Modeling in the pharma industry isn't all that new. Hagemeyer relays that it's been used for years in molecular simulation, selecting participants in clinical trials, and structuring sales territories and compensation plans. Modeling surrounding opinion leader management and physician retention, however, are new trends in the pharma industry. The wave soon might reach its peak, though. As Homer mentions, times are changing and ROI is eroding. "If [pharmaceutical companies] can move reporting investments to leverage intelligence with combing other data sources, that's where we see a big awakening," he says.
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