Historically, pharmaceutical marketers have analyzed efforts aimed toward healthcare providers (HCPs) by looking solely at past performance. But while it may seem logical to focus on what’s been successful in the past with an aim to recreate those results, there’s untapped potential in adopting a predictive outlook—one that leverages data, statistical algorithms, and machine learning techniques as a means to anticipate future behavior.

By utilizing predictive analytics, pharmaceutical marketers will have the capabilities necessary to deploy hyper-targeted campaigns that use holistic, contextual data points to accurately predict business outcomes at the HCP level—whether that’s likelihood to adopt a new therapy or remain loyal to an existing one.

Using Quantity to Improve Quality

Other industries have already begun implementing data-driven probability estimates toward improved business outcomes—from retargeted Amazon advertisements to Netflix’s algorithm-based viewing recommendations. But pharmaceutical brands have been slow to navigate this shift.

While the pharmaceutical industry already collects piles of invaluable HCP and patient data, too few companies are using it in a predictive context, and are in many cases focusing on surface-level insights about past behaviors. Some brands, for example, might place too much weight on an HCP’s historical prescribing activity or type of medical practice in predicting whether he or she will prescribe a new drug—all the while ignoring seemingly tertiary but potentially predictive information about an HCP’s gender or educational background.

While descriptive analytics about past behavior is certainly valuable, marketers can leverage machine learning to produce a higher quality assessment of what will happen in the future. Unlike their human counterparts, trained algorithms have the power to simultaneously pore over, organize, and derive the most business value out of millions of data points. By rapidly determining a particular HCP’s overall propensity to both experiment and prescribe, algorithms have the power to paint a detailed picture of which patterns, behavior, and demographics are most relevant to determining likely future behaviors. Marketers can then use those patterns to craft accurate, hyper-specific behavioral predictions, literally pinpointing the HCPs most likely to adopt a new treatment or therapy—and in turn craft marketing strategies and tactics based on those predictions.

So, why aren’t more pharma brands using this approach? Generally speaking, people remain reticent about turning to artificial intelligence, concerned that too much automation will increase margins of error or encroach upon human instinct. But those dystopian anxieties are too often blown out of proportion—especially when it comes to marketing, where the interplay between machine learning and human intelligence can and should be complimentary.

Predictive Analytics are the Future

The potential of machine learning for the purposes of predictive HCP marketing is considerable. Not only can it enable marketers to identify new and more meaningful data points, but they also could accelerate the pace at which they are able to collect and interpret them. Machine learning can also allow marketers to move away from data points that have little to no impact on prescribing behaviors and instead collect previously undervalued information and take advantage of it.

By re-orienting their outlook and adopting a strategic mindset powered by predictive analytics, medical marketers will be enabled to pull powerful insights out of the mass and ultimately facilitate more meaningful HCP communication.

  • Kevin Troyanos

    Kevin Troyanos is SVP, Analytics at Saatchi & Saatchi Wellness, where he empowers healthcare marketers with data-driven strategic guidance while developing innovative solutions to healthcare marketing problems through the power of data science.

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