The case for advanced analytics

By Patrick Homer

The traditional commercial model for pharmaceutical companies – an army of sales reps backed by extensive mass marketing – is under siege. Sales forces are shrinking, salesforce spending is flat, and sales reps get less time than ever with busy physicians. Furthermore, a large sales force has less impact in an era of managed care.

With fewer dollars to invest in marketing, the mix of channels and messages has to be right. With fewer sales reps on the street, the choice of which physicians to target must be more finely honed. With the brand being lauded or lambasted on social media, communication efforts must be coordinated and cohesive across channels. With payers having more influence over prescribing patterns, pharmas have to carefully manage those relationships. All signs point to one conclusion: It is time for pharmas to apply more sophisticated methods for understanding, predicting and optimizing their sales and marketing investments.

"This is not hype," says Scott Evangelista, principal at Deloitte Consulting. "When will senior management truly adopt, embrace and demand robust predictive analytics in decision making? It is still an open question, and it is happening slower than I would have expected, given the enormous pressure on spending."

Other industries – retail, telecom and financial services, to name a few – long ago developed analytic capabilities for sales and marketing programs. Why has the high-stakes pharmaceutical industry been so late to the game? Advanced analytics do figure prominently on the science and manufacturing side of the business. Analytics enable pharmaceutical companies to quickly transform biomedical data into clinical insights, accelerate drug discovery and development processes, and analyze the supply chain to manage production efficiencies and risk. Why not apply the power of business analytics to make commercialization programs even more successful?

"Pharmaceutical companies have been late because the burning platform has not been there," Evangelista says. "They've been flush with cash. They've been very profitable. The more profitable companies are, the less they look for the pennies and the minor tweaks and twists that would boost efficiency and return on investment. With the expected impacts from health care reform, their margins are going to shrink – we're already seeing that in the market. This has ignited the platform, and the flames are getting closer."

Analytics in Action – Three Prime Applications

Physician Targeting
Rising-cost pressures, increased regulation and declining salesforce productivity are causing pharmaceutical companies to re-examine the way they target physicians for promotion. Gone are the days of virtually unlimited access to prescribers. Companies now must ensure that every sales call counts while reducing costs, increasing precision and attaining greater insight into physician prescribing behavior.

Historically, pharmaceutical companies have targeted the physicians who prescribed highly in the past, on the assumption that these physicians would continue to prescribe highly in the future. But this "top-decile" approach gives no consideration for physicians whose prescribing has peaked and is on the decline – nor does it recognize for low-decile physicians who have potential to become high prescribers of the target brand.

Predictive analytics look at all the variables that come into play to create not just a list but a behavioral profile of high-value physicians. Then you can go back into the universe of other physicians and find those who have the same characteristics. These physicians – the ones with high potential but untapped value – represent the most productive ones to target for promotional contacts.

Using predictive analytics instead of a conventional, top-decile approach, a midsized company delivered a 15 percent lift in prescribing potential – an additional $132 million – across its product portfolio. A top-five pharma company released 24 percent of additional prescribing potential worth $77 million on one brand alone. Another company discovered that results for the launch phase of its new product in a new market could have been 37 percent higher if an analytically detailed target list had been used.
In addition to lift, analytical physician targeting also delivers unique insights into why those physicians are prescribing – behavioral characteristics of loyalty, adoption and patient mix synergies.

Marketing Mix Optimization
Which channels, segments and offers will yield the best results? Traditional marketing mix analysis is clunky in answering this question. It tends to be historic and not predictive – only marginally useful for decision making. Invariably, the data lags reality. By the time marketers get it, it is stale. As pharmaceutical companies contend with a more complex, competitive and restrictive marketing environment, they need more than a static snapshot of the past.

Predictive marketing mix analysis provides forward-looking insights for finance and senior management. Push a button to automatically run analyses that optimize expenditures across geography and media channels. What-if analysis simulates what would happen when a variable in the marketing mix is changed.

Which mix of marketing channels will yield the best return? Where is the saturation point for a particular channel? Being able to answer these questions is nothing new, but it has typically been figured out at the end of a campaign. Predictive analytics enable you to look at an upcoming campaign and make predictions about the best course of action.

Managed Care Contract Rebate Optimization
To gain preferred-tier status with health payors/plans, pharmaceutical companies make extensive use of contracts with rebate provisions for reaching certain utilization thresholds. When done correctly, rebate strategies can optimize market share, revenue and profitability.
But how do you design contract rebate strategies for best results? A cohesive business analytics platform drives more effective managed care contracting by:

• Integrating the required data from multiple sources, such as syndicated data providers, internal contract management systems, external data providers and census records.
• Measuring and monitoring rebate contract parameters and performance, such as prescriptions by patient type, product switching momentum, new therapy starts and market share.
• Using "what if" modeling against hundreds or even thousands of variables to assess the predicted effect of various rebate scenarios on tier/restriction status, market share, sales volume and ROI.

Necessity, not luxury
The commercial side of the pharmaceutical industry has been accumulating mountains of data over the last several years. A lot of the new data sets could deliver some unique insights, if mined appropriately. Access to that data has also changed. Data is readily accessible, and so is the processing power needed to find significant variables across multiple data sets. But above all, today's pharma business climate demands precision business analytics. What was once a luxury is now a necessity. Brand winners will be defined by their predictive insights. Companies have the data, the processing capability and the analytics. All they need now is to use them.

Bio:  Patrick Homer is a thought leader in the field of transformational analytics in commercial pharma.  He currently leads the global commercial pharma practice at SAS and has been involved with both strategy and development of SAS solutions in this area.

*Note: Statements from Daniel Feldman quoted in this document reflect his personal views, not those of Bristol-Myers Squibb or its affiliates.