Data-Powered Healthcare Media: 3 Enhancements to Dramatically Improve Results

“In God we trust. All others must bring data.” – W. Edwards Deming

Every day the world generates 2.5 quintillion bytes of data. Thanks to digital technologies, 90% of this “Big Data” was created in the last few years, and marketers have begun to leverage its tremendous potential. For healthcare brands targeting specialized audiences, data can greatly improve media performance and accountability.

Let’s look at three powerful opportunities to data-enhance healthcare media campaigns at each step in the process:

1. Planning: Outcome Modeling

Outcome modeling makes media planning more objective. By leveraging the wealth of available data—including audience profiles, media consumption habits, product purchases, marketing performance, customer attitudes—outcome modeling enables strategists to optimize media strategy elements and maximize results.

Unifying disparate information and evaluating all relevant data, outcome modeling analyzes how media influences consumer behavior, and equips strategists with the tools to plan against key performance indicators (e.g., awareness, preference, information seeking) and project the impact of multiple media plan options.

Outcome models deliver:

• Media spend required to hit goals
 Optimized channel mix
• Budget allocation by channel
• Optimized flighting
• Media performance (e.g., weekly GRPs)
• Business outcomes (e.g., sales)
• Campaign performance (e.g., return on ad spend)
• Scenario planning comparing media options

Data richness and quality are essential to successful outcome modeling. For example, consider a tool that could access robust data from two sources: 1) A proprietary survey of 200,000 consumers spanning 36 categories across 78 different channels (paid, owned, earned), and 2) A rich ecosystem of best-in-class data providers, previously unconnected, encompassing near-real-time data from Nielsen, Rentrak, ComScore, Simmons, Experian, MRI and Kantar.

Such data would provide deep customer insights (e.g., how TV affects search performance) and a powerful competitive advantage. For example, an immunology brand could use outcome modeling to plan media that delivered equivalent GRPs to higher-spending competitors at a significantly lower budget.

2. Buying: Programmatic

Programmatic media provides two great advantages: Purchasing audiences instead of media content and automating media buying processes. Buying specific audiences via enhanced data is a significant advancement in media targeting, although predominantly in the digital space as programmatic TV continues to develop.

Combining brands’ customer data with third-party information, programmatic audience buying generates deep insights, reaching targets wherever they are online, across health and non-health content.

The primary advantages of programmatic media are:

• Enhanced targeting: Purchase based on an individual’s value, not broad demographics.
• Deeper insights: More knowledge about customer interests, behavior and new engagement opportunities.
• Greater efficiency: Lower CPMs, improved reach/frequency, more scalable and automated processes.
• Improved performance: Higher clickthroughs and conversions, increased engagement, lower CPAs, better ROI.

Some healthcare marketers previously avoided programmatic because of concerns around placement uncertainty, privacy and inventory quality. However, new ad technologies can block specific placement concerns and ensure anonymized, privacy-protected data usage. And while programmatic inventory often excludes premium content, quality inventory continues to be added, especially for professional audiences.

What pharma brand wouldn’t want more effective campaigns while spending less with a streamlined workload? Recently a neurology therapy used programmatic to avoid premium health sites’ higher pricing and competitor proliferation, engaging the target in non-health content, and improving results at a far lower cost.

3. Measurement: Attribution

In healthcare, like many categories, the product purchasing journey winds through multiple channels and touch points. Attribution modeling seeks to quantify the contribution of each marketing contact in driving the desired action (e.g., filling an Rx).

However, there is currently too much data missing to consistently track one person across multiple media channels and devices. Since digital measurement has fewer limitations, marketers can first prioritize assigning proper value to digital touch points.

Nearly half of digital marketers still rely on “last-touch” attribution (100% credit to the last media exposure), which typically over-credits search and undervalues display. More accurate digital attribution assigns credit to channels using one of the following fractional credit methodologies that best fit the brand and target:

• Linear: All media exposures credited equally.
• Time-decay: Last-touch credited most often with prior exposures credited decreasingly.
• First-last position: First and last touches credited most, others credited equally at lower levels.

One oncology brand historically credited the last paid search exposure 100% for driving a clickthrough and site visit, but after more accurately crediting earlier customer searches, improved its keyword mix to deliver higher campaign results.

Attribution analyzes user-specific data to direct current spend, and differs from media mix modeling, which leverages aggregate historical data to guide future spend.


Healthcare marketers can immediately drive greater media effectiveness by seizing these three “Big Data” opportunities to enhance media planning, buying and measurement.


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