What Delayed Measurement is Costing Healthcare Marketers – And Society at Large

By John Mangano, Senior Vice President, DeepIntent

When a doctor prescribes a new drug or therapy, the effects are often life-changing.

In that sense, the healthcare industry is unique. Successful marketing campaigns directly translate into outcomes that can significantly improve patients’ physical or mental quality of life and even save their lives.

Of course, timely reporting is valuable to all marketers, but in healthcare, there are higher stakes involved. The more effectively pharma marketers can reach patients and providers with timely, relevant information about health conditions and their treatments, the more likely they are to take action.

Yet, many healthcare marketers today rely on metrics that report on patient reach with data that is delayed often weeks or longer. Conversion analysis often takes place months after campaigns end, and campaign optimization is delayed even further. Added together, these delays represent not just a financial cost to healthcare marketers, who spend more than $6.5 billion annually, but an opportunity to improve patient outcomes.

Everyone Benefits from Better Measurement 

Today’s campaign measurement solutions typically provide pharmaceutical marketers with a report that looks at campaign impressions with a snapshot of the profile of the patients reached, often called Audience Quality. With these types of insights, marketers can reduce impressions that have a lower likelihood of reaching the right patient and maximize impressions that are reaching people most likely to benefit from the treatment.

These insights are updated at the quickest weekly but often represent ads served two or more weeks before the report, creating an effective lag of at least a month or longer as there may not be enough sample to measure statistical significance. This means that underperforming segments of the campaign are wasting months and millions of impressions serving ads to less qualified patients. 

With this type of timing, it’s no surprise that many patients do not feel like ads are relevant to them. Marketers responsible for reaching the right patient may be making incorrect assumptions based on outdated or incomplete data that doesn’t consider all of the levers available to them. It’s not just a matter of efficiency, either. When dealing with life-threatening illnesses, the consequences can be quite significant to patients’ health.

Thankfully, by combining daily, anonymized clinical and pharmacy data with campaign data, marketers can gain a much better understanding of campaign performance and optimize campaigns faster than ever before. As an analytics person, I tend to see the story in numbers. If we assume an optimization might create a modest 3% improvement in reaching the right kind of patients, then we can see how frequent optimizations matter in the end. 

 

Monthly Optimization Gain

Weekly Optimization Gain

Week 1

No Optimization

3%

Week 2

No Optimization

3%

Week 3

No Optimization

3%

Week 4

3%

3%

Total Improvements Seen in First Month

3%

9.3%

Total Improvements Seen in 3 Months

9%

38%

Total Improvements Seen in 1 Year

43%

301%

In the first month alone, weekly optimization triples the impact of monthly optimization, and over the course of a year, the impact is seven-fold. 

As marketers, we know that campaigns ebb and flow, and often a campaign will see diminishing returns over time. The above logic still holds as campaigns see lower effectiveness in time – and frequent optimizations based on real-time, clinical data can even be applied to counter these diminishing returns.

Improve Campaign Performance by Integrating Timely, Real-World Health Data

The digitization of healthcare records is well over a decade in the making, but it’s taken a while for that innovation to trickle its way into the daily processes of pharmaceutical marketers. As a result, many are not taking full advantage of anonymized, real-time health data, despite profound advantages in doing so. 

By integrating real-time, privacy-safe health data with the marketing technologies that serve ads, healthcare marketers can now measure qualified patient reach and verified new prescription starts. Within days, there is enough data to determine which of their channels and demographics are most effective at achieving their campaigns goals such as audience quality and new-to-brand scripts. That information can then be interpreted manually or in real-time using machine learning to optimize campaigns toward higher audience quality and better inform future campaigns.

For example, careful analyses by digital marketers allows for optimization on macro and micro levels to ensure that pharma campaigns have the greatest impact in terms of patient reach and new patient starts. Most marketers optimize where ads appear based on the aggregate profiles of previously-exposed page viewers collected over time. In doing this, the campaigns increase their impact on the content that is most likely to reach right-fit potential patients. Or they can speed up optimizations of connected TV (CTV) buys based on the inventory they know best matches the interests of their target audiences. This happens today, but it is often done after waiting for a month’s worth of data. This timing historically is a result of the fact that measurement and ad serving are separate and disparate components.

The key to all of this is moving toward solutions that consolidate real-time data in one place, allowing marketers to make informed decisions about the information needs of their patient populations.

For too long in healthcare, the optimization of campaigns has been siloed and hamstrung by measurement delays that have made analysis difficult or incomplete. No longer. Modern measurement solutions offer daily data refreshes that allow marketers to make faster, more strategic decisions.

The technology has finally arrived to measure and optimize campaigns. It’s time healthcare marketers caught up.

John Mangano

 

About the Author: John Mangano

John Mangano has spent a career leading marketing analytics teams with senior roles at companies including Healthgrades, Digitas Health LifeBrands, and Comscore. He joined DeepIntent in 2021 as a senior vice president, analytics and marketing sciences, to guide the development of the company’s real-time, claims-based media measurement, optimization, and targeting models.