If I didn’t know better, I might think the growing field of online conversation monitoring (aka sentiment analysis) would be a no-brainer for the pharma industry.
Healthcare consumers are flocking to the social Web to post about their struggles with a given disease and the medications they hope will help fight it. State-of-the-art search software is increasingly available to capture and process these conversations, meaning pharma has access to a wealth of consumer dialogues. Certainly all of this rich data must be helping brand managers better serve their end users.
So why did a brand manager tell me recently that executives at her company, a global pharma, weren’t convinced their pilot “listening” program was worth continuing?
Because “listening” to patients doesn’t mean that you’re hearing them.
I know we marketers love our data. We love it even more when it paints a quantitative picture of our audience’s world. This might be the reason social monitoring vendors are focusing on the statistical value of all of these conversations. For example, the brand manager mentioned above learned that her brand was discussed in only .0076% of online conversations, versus .0124% for the competitor, and that negative sentiment about her product was up 23% because of patients discussing side effects.
Data like this is interesting, but what can you do with it? Implied tactical suggestions that might work in other industries can spell danger for pharma. Our brand manager said the monitoring vendor fell into one such trap: The vendor recommended that she promote a scientific study, popularized on several message boards, despite the fact that (ouch) it discussed the product off-label.
The moral: Data derived from social media should be viewed as qualitative in nature. For one thing, many drugs serve niche audiences that will yield tiny sample sizes and microscopic quantitative measures. We already know from Forrester et al that only a thin slice of social media users are actually doing the posting, so threads about even widely used drugs can’t be seen as statistically representative.
In addition, quantitative analysis is an inadequate instrument to probe the consumers in question. It's like using a CAT scan to diagnose poison ivy—it misses both the obvious and the subtle that would come from keen observation and listening to a patient's own words.
The real power of social media monitoring in pharma emerges when online patient dialogues are seen within a qualitative construct—not a random statistical sample but real people, talking frankly about their conditions, their concerns about treatment, and their beliefs about medications. The ideal interpreter of these patient experiences should be someone who exhibits “context sensitivity”—that is, a deep understanding of the patient’s treatment journey beyond her activities in the social media.
Of course, at HealthEd we believe that person is a health educator [PDF]. In Part Two of this post, I’d like to help you understand why that’s our belief, and take you through the social landscape in a particular disease category with some of our senior health educators at the wheel.
Stay tuned!
Jeff Greene
Director of Strategic Services, Social Media
HealthEd
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