Consumer targeting is about to have its day in the sun. Let me explain.
Consumer targeting has always been attractive to Pharma marketers. Who wouldn’t pick media efficiency, quality consumer interactions and marketing performance? But it has generally come with some big trade-offs, namely price, volume and privacy concerns. So up until this point, media buyers have been limited to buying search terms and contextual or endemic placements to deliver targeted impressions, which they have had to supplement with larger spends in broader reach media to achieve their volume goals.
Two things are changing: The imperative, and our ability.
The Imperative: Getting more Urgent
The emerging healthcare environment is all about managing patient outcomes. And as outcomes become the new metric across healthcare, the basis for Pharma reimbursement will shift from volume to value. Pharma marketers will have to move their focus from generating high volumes of new potential patients based on indication to generating positive clinical results for individual patients.
This change is fundamental. Where we now succeed by getting volumes of consumers to request—and doctors to prescribe—our medications more often, tomorrow we are likely to be better off financially with fewer but better aligned and better faring patients taking our drugs.
As an industry, we need to get smarter, and our regulatory bodies need to get more comfortable, with using targeting techniques to pinpoint whom we need to engage with to maximally benefit both the brand and the patient.
Our Ability: Increasing Sophistication of Predictive Techniques
Three targeting approaches, based on three sources of data: geo-demographic, behavioral and transaction, are extensively used in other industries for consumer targeting, and are being leveraged, albeit to a lesser extent, in some categories of healthcare.
But here’s where it gets to be really fun. Imagine the power when triangulating these sources of targeting data to create a richer, more actionable picture of the consumer population, at the therapeutic category level, that would enable Pharma brands to pinpoint where we should spend our marketing dollars and whom we need to engage for the maximum benefit of both the brand and the patient.
We can now combine demographic, behavioral, and transaction data sources—in aggregate, in accordance with HIPAA—to create propensity models that will help us predict, with a high level of accuracy, a person’s likelihood to get on a specific drug. While we would never disqualify a consumer from receiving health information, we can now use such predictive models to calibrate spending, customize messaging and deliver the right level of information per the propensity score.
Such models can dramatically influence our media buying approach. Once we identify segments that over-index against the likelihood-to-convert score—these may be publicly-available segments or custom-built segments of consumers who look and behave like our patients—we can better target our media buys, at unparalleled scale, against these segments.
The propensity models currently being built for DTC predict likelihood to convert, but as data sets grow and our experience increases, we will be able to use the same logic to estimate likelihood to be compliant, which is a key indicator for positive health outcomes. We’re not there yet, but that’s where the industry is headed. Keep learning, gain the experience, and stay tuned.