Project Overview
A Smarter Way to Run Campaigns
About the Company:
The brand featured in this case study is an emerging biopharma commercializing a novel COPD treatment across the US. A launch-stage product entering a competitive respiratory market with lean resources and significant pressure to perform.
The brand came in with a clear list of goals: marketing spend needed to be justified, field and marketing activities needed to tell a coherent story, and leadership needed visibility into what was actually driving prescribing behavior, not just engagement metrics.
The team had existing agency relationships and a CRM in place. What they lacked was a way to connect their marketing activity to downstream Rx outcomes, or to coordinate those signals with their field team in real time.
Partnership Details:
This deployment was part of ODAIA's early adopter program. This brand was among the first commercial deployments of what is now ODAIA Marketing Intelligence, and their experience directly shaped how the product was built.
Campaigns were activated through ODAIA's partner API that connects to agencies and media partners, with recommended content dispatched automatically and engagement data flowing back into ODAIA in real time. Read the full press release.
Results at a Glance:
The brand began with a pilot group of 12,000 HCPs to engage. After seeing the positive results of the first few cycles, they quickly expanded to their full marketing universe of 70,000 HCPs.
Sample results from one month of using Marketing Intelligence are as follows:
- 33,518 HCPs were channel-eligible and received a tactic via automated dispatch
- 26,807 HCPs engaged with the recommended content
- 10,645 HCPs converted to writing a prescription in the window following engagement
Compared to the usual shotgun approach, the brand chose to focus engagement on a smaller, more strategically selected group of physicians. The relevance of the messages and timing yielded better results for the brand.
The Challenge
The Problem Worth Solving
Millions spent engaging HCPs. No way to know what was actually working.
Launching a novel treatment in a competitive respiratory market demands precision. The brand had the resources and agency relationships to run campaigns — but three compounding problems were limiting their commercial impact.
- Campaigns reached the wrong physicians. HCP targeting was built on personas and historical prescribing volume. Physicians who were on the verge of adopting, but hadn't yet written a script, were filtered out. The model was backward-looking by design, which meant the team was always chasing the market rather than anticipating it.
- "Performance" stopped at engagement. Campaign results were reported in opens, clicks, and event attendance. The team could demonstrate that an email campaign performed well in digital metrics. They could not demonstrate that it impacted prescribing behavior which made it difficult to justify spend, optimize channels, or make meaningful changes from one campaign cycle to the next.
- Field and marketing teams operated from different information. The marketing team ran digital campaigns on one schedule. The field team called on HCPs from a separate plan. There was no shared view of which physicians had been recently engaged digitally, which messages they had received, or which were showing early prescribing signals. Coordinating a follow-up rep call after a digital touchpoint was entirely manual — and usually delayed or missed.
These problems compounded together. Imprecise targeting produced irrelevant engagement. Irrelevant engagement couldn't be tied to Rx. And without a shared data layer, field and marketing teams couldn't course-correct between cycles.
The result was a campaign operation that generated activity but struggled to demonstrate impact.
This is a challenge common across pharma commercial teams — and one we explore in depth in our recent blog on re-thinking HCP marketing.
The Solution
ODAIA Marketing Intelligence
Individualized sequences. Automated dispatch. Closed-loop attribution.
ODAIA Marketing Intelligence was first deployed against a pilot subset of HCPs and later expanded to the full physician universe. ODAIA used the brand's existing commercial data as is, so no new data infrastructure was required. The platform integrated with their CRM and connected to their agency and other media partners through a bidirectional API.
In practice, this translated into six interconnected capabilities — each one addressing a specific gap in how the brand had been running campaigns.
Every HCP, Evaluated Daily
The Value Engine assigned each physician a PowerScore from 10 to 0, reflecting their prescribing patterns, engagement history, channel preference, and journey stage.
- PowerScores refreshed continuously as new behavioral data arrived — the priority list always reflected the current market, not a segment built months earlier.
- The platform didn't just identify who to reach, it identified who to remove — protecting budget before a single tactic was dispatched.
Personalized Sequences, Not Batch Campaigns
The Sequencing Engine generated daily recommendations for every prioritized HCP.
- Which channel, message, and when — built on the individual's behavior and journey stage, not a persona.
- To support this at scale, the platform performed more than 86 billion computations, per week.
Automated Delivery Through Activation Partners
Approved recommendations were automatically delivered.
- Formatted for each platform with no manual uploads or reformatting required.
- Engagement data flowed back into ODAIA in real time, triggering the next personalized sequence based on how each HCP responded.
- The brand's team focused on strategy. The system handled execution.
Field and Marketing, Working From the Same Signals
Field and digital channels operated in coordination, not in parallel.
- Physicians who responded well to rep engagement were prioritized for follow-up conversations supported by clinical materials.
- Those who engaged primarily through digital received targeted content through those channels.
- Consistent messaging, adapted delivery — based on how each HCP actually behaved.
Campaigns That Get Smarter Each Week
Most campaigns run on a fixed schedule until the cycle ends. ODAIA works differently.
- As physicians interacted with campaign materials, new engagement signals fed back into the model daily.
- Each dispatch cycle was informed by the last, making the campaign more accurate over time without additional lift from the team.
- This created a continuous analytical loop, each pass through produced a more accurate model than the last.
Rx Based Measurement
ODAIA's Attribution Engine statistically connected each HCP's marketing engagement to downstream prescribing activity.
- Results were reported in one-week tactic cycles measured against four weeks of Rx data, not opens or clicks.
- Before each cycle: a Campaign Simulation Report showing projected outcomes.
- After each cycle: a Campaign Performance Report showing what actually happened, per channel, per HCP.
- Every output was explainable to leadership.
The Value Engine, Sequencing Engine, and Attribution Engine each play a distinct role, and together they're what makes personalization possible across a universe of hundreds of thousands of physicians. Learn more about the engines powering ODAIA.
The Results
Measurable Commercial Lift
From 33,518 tactics delivered to 10,645 traced prescriptions in one cycle.
Most omnichannel campaigns can tell you how many HCPs opened an email. Few can tell you how many went on to write a prescription. This is what a single cycle looked like. Every number is traceable to a real HCP, a real tactic, and a real outcome.
Key Takeaways
What Changes When You Connect Campaigns to Rx
This deployment demonstrated what changes when a commercial team shifts from static targeting to continuous behavioral modeling.
- Precision outperforms reach. Narrowing from 70,000 physicians to 33,518 relevant targets isn't a limitation, it's the whole point. The platform identified the physicians most likely to convert based on forward-looking behavioral signals that conventional segmentation would have missed. Focused targeting produced better outcomes than broad outreach.
- Every output is explainable. The 10,645 conversions weren't inferred from population-level estimates. The Attribution Engine traced each conversion back to a specific tactic, channel, and timing sequence at the individual HCP level. That's the kind of attribution that makes budget conversations with leadership straightforward.
- The model compounds over time. Attribution data feeds back into the Sequencing Engine continuously. Each cycle's recommendations are informed by the results of the last. The brand that starts with one cycle of data will have a meaningfully stronger model six cycles in and results that improve without additional lift from the team.
What makes this replicable is that none of it required a new data build or months-long implementation. The platform worked with the brand's existing commercial data, integrated with their existing partners, and started generating individualized recommendations within weeks.
The brand began with a smaller test population of HCPs, validated the results, and expanded to their full physician universe based on what they saw. That's how confidence in a new approach gets built — not with a big-bang commitment, but with evidence that compounds cycle by cycle.
If you're a pharma brand marketer evaluating whether this approach fits your commercial model, see how it applies to your team.
