Marketing to Professionals: Tomorrow's Changes Today

January 1, 2007

Pharmaceutical Executive

Volume 0, Issue 0

How hard is it to spot an emerging threat or opportunity in time to actually do something about it? Is it as hard as spotting a scud missile in the deserts of Iraq? As hard as identifying an underwater threat to a submarine using only sonar? As hard as spotting a consumer trend in a vast and complex business like financial services?

How hard is it to spot an emerging threat or opportunity in time to actually do something about it? Is it as hard as spotting a scud missile in the deserts of Iraq? As hard as identifying an underwater threat to a submarine using only sonar? As hard as spotting a consumer trend in a vast and complex business like financial services?

Turns out, analytic techniques developed to help meet exactly these three challenges (and others) are migrating to the pharmaceutical industry. They may not be common yet, but several pharma companies are already employing these techniques, which identify useful patterns in highly complex data, to pursue some important research goals:

  • Early detection of behavioral shifts, alerting marketers to changes well before they happen, as opposed to alerts that detect change after the fact
  • Identification of interactions that lead to changes in behavior, by coming up with metrics that identify social networks and individual levels of influence
  • Knowledge about the behavior of both individual customers and whole markets that can be layered into a broad range of sales and marketing initiatives to enhance outcomes.

Although pharma has only recently begun to use advanced analytical tools, other industries have been turning to them for over a decade, to cut costs and enhance customer relationships. In the consumer banking industry, for instance, these tools helped marketers allocate resources with greater precision by focusing on "segmentations of one," which helped bring about a revolution in the consumer banking industry a decade ago.

"Applying this predictive technology at that time, we distinguished ourselves by finding the right product mix: frequent flyer miles, rebates, and by accurately pinpointing who to give loans to, and what kinds of offers to send that people would accept," says Dan Schutzer, vice president and director of external standards and advanced technology at Citigroup. "I could send five-million letters and get only a one-percent take-up of people who would actually want to buy the new product. But, if I got a two-percent response rate instead of one percent, I did phenomenally better. I'm sending the information, you are responding to it. I'm tailoring, I'm making better decisions."

Kelly D. Myers

The Learning Curve

In the pharmaceutical industry, advanced analytical tools are particularly valuable in identifying key patterns in complex data flows. They help companies figure out how to deliver the right message to the right customer, at a time when the customer is likely to be most receptive. Analytics can be used to detect shifts in market and customer behavior patterns by using "learning" algorithms to identify problems, and then adapt and correct the situation early in the cycle.

The analysis is a two-step process. First, networks of physicians who are likely to interact with one another on a regular basis are identified. Then, changes in practice patterns are quantitatively captured along with the direction of influence.

Using data instead of opinion to quantify influence enables marketers to navigate areas where ordinarily there are blind spots. The quantitative approach ascertains who is influencing whom and confirms it with data. Traditional key opinion leader influence mapping identifies thought leaders, but quantitative influence mapping identifies networks operating on regional and local levels that were not previously perceived as influential.

At Esprit Pharma, Brent Herspiegel, director of marketing for Estrasorb, a topical estrogen treatment for menopausal hot flashes, is responsible for growing the brand in a crowded hormone therapy market, with a relatively small sales force of 175 reps. Using advanced analytics, Herspiegel's team measured the influence of customers within the entire therapeutic class, and identified clusters of physicians who regularly affect the behavior of one another. With influence metrics quantified and clusters identified, this knowledge was then layered on top of the company's speaker program.

Medical education companies are also taking advantage of understanding influence within a local market. HealthLogix, for example, regularly recruits physicians for speaker's programs, advisory boards, and clinical advisory programs. With influence per customer quantified and influence clusters pinpointed, the company uses analytical techniques to target optimal groups to enhance the impact of physician-to-physician interactions.

By using influence data, "we were able to not only identify influential speakers, but also identify the most appropriate audiences likely to be the most receptive to our message," says Brian Budisak, a founding principal of HealthLogix.

"Uncovering influence within social networks allows us to identify physicians who are more likely to be influenced by one another," says Budisak. "Not only does it provide our clients with more productive meetings, but it also makes it easier for us to recruit quality physicians who enjoy each others' company."

Calculated Solutions

It's nearly impossible to distinguish events by using conventional static measurements as indicators. Here are some advanced-analytic solutions to common issues:

Issue: Optimization of speaker program resources.

Solution: Companies must match the right speakers with the right audiences and measure the outcome. For example, use quantitative measures rather than opinion to select the right speakers and the right audiences—audiences that are most likely to interact with others that you want to be privy to your speaker's message. Then, measure the impact over time.

Issue: Physician valuation. For years, prescription data has been the primary variable used to value physicians, ignoring the vast social networks, peer influences, and payer restrictions that together tell a more complete story.

Solution: Pharma must employ quantitative metrics. While there is still a place for qualitative research, advanced analytics provide metrics based on actual behavior. Using "learning" algorithms, analytics allow you to measure and adapt your marketing programs to customer changes as they are occurring—not after the fact, when competitors are doing the same.

Issue: The perfect program cocktail. Some physicians prefer sales calls, some prefer dinner meetings, and others rely on journals or teleconferences to learn about new agents. It is critical to determine the best combination of tactics in order to affect real change.

Solution: Advanced behavioral modeling is defining today's customers in minute detail compared with conventional segmentation techniques. Brand teams have more detailed customer data, including behavioral forecasts, influence networks, and forecasts pinpointing ideal timeframes in which to implement programs— for each individual customer rather than segments.

This emerging shift from opinion-based research to the use of quantitative, predictive models is helping brand teams map out the terrain of a pre-launch, launch and post-launch marketplace with greater accuracy than previously possible. Marketers are becoming more targeted in their approach to drivers within a therapeutic class, from as early as two years pre-launch to the end of a product's life cycle.

Kelly D. Myers is CEO of Qforma. He can be reached at kmyers@qforma.com