How Agile Strategies, Real-Time Data, and AI-Powered Precision Are Transforming ROI Under Tighter Budgets
Prioritize. Find Efficiencies. Do More with Less.
We all know these directives. Market complexity is growing, and pharma marketers continue to feel growing pressure to effectively demonstrate ROI with tighter resources. The average industry marketing budget peaked at $12.5M in 2019 but dropped to $7.2M in 2024, according to MM+M survey data.
The industry has traditionally relied on “always-on” campaigns with an annual media buying cycle and a relatively fixed channel mix purchased in 6- or 12-month increments, often based on a moment-in-time snapshot of HCP or patient audience data. Given today’s fast-moving digital landscape and the drive for efficiency, a more agile approach is needed.
Technology now enables pharma to connect the dots across the customer journey in new ways. By combining triggered media buying with dynamic customer affinity data and continuous optimization, marketers can reach the right audience in the right moments to achieve unprecedented effectiveness and break through the digital noise.
TRIGGERED MEDIA BUYING: Precision Plus Relevance
Technology and AI-powered tools create new opportunities for triggered media buying. This empowers brands to target more precisely by delivering messages in response to specific real-world events or conditions.
Campaigns can rely on various triggers by integrating many sources, such as medical claims insights and lab test results that indicate diagnosis or treatment initiation.
Pharmacy claims may signal first-line treatments, and predictive analytics can help forecast more complex triggering scenarios, such as when a patient is likely to proceed to a second-line therapy. Long a barrier to success, data can be exchanged in real time and complex signaling models can leverage many sources at once to achieve broad coverage and virtually eliminate real-world data lags.
Content deployment follows a trigger alert rapidly to reach that HCP at the right moment to get noticed, create value, and build trust. This enhances relevance and impact and reduces wasted spend. It can also identify additional non-list providers with eligible patients for messaging.
DYNAMIC CUSTOMER AFFINITY: Evolving HCP Preferences
Offering insights into HCP channel preferences, customer affinity is central to media strategy, but it’s often a static picture used in planning based on historical data. What if a campaign could instead personalize content deployment based on up-to-date data on where and how a specific HCP has been actively engaging in that week or month? Real-world data now allows marketers to tap into dynamic affinity at far more granular levels using tens of millions of data points. This might mean understanding what type of message most resonates or which specific platforms an HCP spends time on—and enabling real-time affinity profile updates as their preferences change. Sources may include compliant insights into their digital activities—website visits, email opens, etc.—platform interactions—detailing, virtual meetings—and engagement with previous or even current campaigns.
When affinity data updates in real time, media deployment can be adjusted based on evolving preferences rather than relying on out-of-date information. Machine learning based on actual engagements and recency create a dramatic leap forward beyond other affinity models.
“When human-in-the-loop AI applications automate some of the work, optimization scales far beyond what traditional media buyers can achieve cost-effectively.”
ONGOING OPTIMIZATION: Engineering Continuous Improvement
Campaigns can be evaluated month over month or even week by week to understand reach, frequency, engagement rates, and impact on prescribing behavior. With real-time metrics available and AI-driven models built into a content deployment system, ongoing optimization is now possible.
Marketers can refine and augment trigger criteria based on metrics. Campaign channel mix and messaging can evolve based on HCP affinity shifts and performance data. Automation solutions can even instantaneously adjust an individual HCP’s message journey based on how they do and do not engage with messaging to improve both immediate and future performance. When human-in-the-loop AI applications automate some of the work, optimization scales far beyond what traditional media buyers can achieve cost-effectively.
CONNECTING THE DOTS: Putting This Into Action
Let’s make this more tangible with a real-world example. An oncology brand marketer launching a new treatment for a rare indication needs to engage a small set of specialists to drive launch success. Using these techniques, the campaign could deploy to an HCP within 24-48 hours of their eligible patient testing positive for a specific mutation—trigger. A message deploys on the channel that HCP currently engages with most—email, for example, according to dynamic affinity data—on the platform they use most—more likely to open from an oncology news source than the brand CRM—and with the type of message they prefer—efficacy data is expected to perform best.
If they don’t open the email, it redeploys with an alternate subject line. And when they now engage with it, that insight will then improve future interactions—ongoing optimizations.
When it can take 10 or more touchpoints to impact prescription behavior, it’s essential to maximize every moment by creating relevance, engagement, and value for the brand and for overworked HCPs who are bombarded by information daily.
A NEW APPROACH: Stepping Into the Future
Investing in the right tools and platforms helps achieve this new model, solving challenges in data integration and management, ensuring compliance, and integrating marketing and sales in real time to synchronize efforts seamlessly.
By combining triggered media buying, dynamic affinity data, and ongoing optimization, marketers can achieve precise targeting, enhance message relevance, improve HCP engagement, and see demonstrable ROI. This maximizes media buying and creates a more data-driven, accountable approach that better aligns with fast-evolving needs.
