The pharma industry has spent the last decade building trigger-based HCP targeting programs - using everything from claims and EMR data to labs and genomics. But not all data is created equal.
Lab and genomic data don’t just enable more precise targeting - they consistently deliver higher performance across critical KPIs, especially when speed and specificity matter. Lab and genomic signals originate closer to the point of clinical decision-making, whereas claims-based triggers often reflect events that occurred days or even weeks earlier. When precision matters, proximity matters.
Here are the five KPIs that every commercial team should be tracking - and why lab and genomic data usually beat claims-based approaches:
1. HCP engagement rate post-trigger
What it measures: The % of HCPs who engage (e.g., rep visit, email open, portal login) within a defined period after receiving a trigger.
Typical benchmarks:
• Lab/Genomic: 30–45%
• Claims: 15–25%
Why it matters: Genomic and lab data are more immediate and clinically actionable - they signal real-time patient need (e.g., a new mutation result or biomarker finding), not retrospective utilization patterns.
Use case:
A leading oncology brand saw engagement rates rise from 22% (claims triggers) to 41% (genomic mutation triggers) when using NGS panel results to inform rep activity.
2. Time-to-action (TTA)
What it measures: The time from the data signal to the first sales or marketing touch.
Typical benchmarks:
• Lab/Genomic: <2 days
• Claims: 5–10 days
Why it matters: Lab and genomic feeds are often real-time or near-real-time, allowing for faster response to clinically relevant events. Claims data can lag by weeks.
Use case:
A cardiovascular therapy leveraging real-time troponin lab data cut TTA to 1.8 days. The same program using claims triggers averaged 7.2 days - missing critical windows for engagement.
3. Rx lift or treatment initiation rate
What it measures: The increase in prescribing or therapy initiation following a triggered engagement.
Typical benchmarks:
• Lab/Genomic: 8–20% Rx lift
• Claims: 3–10%
Why it matters: The clinical specificity of lab/genomic data increases the likelihood that the HCP is actively managing a patient who qualifies now, leading to higher conversion.
Use case:
A rare disease brand using enzyme assay results as triggers saw a 17% lift in treatment starts, versus just 6% when using diagnosis codes in claims data.
4. Trigger-to-conversion rate
What it measures: The % of triggered HCPs who prescribe or initiate treatment within a defined follow-up window.
Typical benchmarks:
• Lab/Genomic: 15–30%
• Claims: 5–12%
Why it matters: Lab/genomic signals typically reflect active clinical workups or molecular confirmations - strong predictors of near-term prescribing behavior.
Use case:
A lung cancer franchise using PD-L1 expression results achieved a 28% conversion rate. Claims-based triggers (e.g., imaging codes) delivered just 10%.
5. HCP targeting precision
What it measures
The percentage of HCPs whose activity is driven by data signals that align with verified therapy eligibility criteria.
Why it matters
Traditional targeting often relies on claims-based proxies that lack clinical precision. By leveraging real-world diagnostic signals-like lab or genomic data-brands can more accurately identify HCPs involved in treating patients who meet therapy eligibility criteria. This approach aligns better with label indications and reduces inefficiencies.
Use case
An immunotherapy brand used biomarker-based triggers to improve targeting, reducing false positives by 40% compared to a claims-only approach. This helped focus engagement on HCPs likely treating therapy-appropriate patients, cutting down on wasted outreach and improving relevance.
Bottom line
While claims data still plays a role in longitudinal insights and retrospective targeting, lab and genomic data are rapidly becoming the gold standard for precision engagement. Proximity to the point of clinical decision is critical. Lab and genomic signals provide that proximity - enabling more timely, relevant, and effective engagement.
If your brand strategy relies on timing, clinical relevance, and high-value patient targeting, you need to prioritize KPIs that reflect those advantages - and benchmark them by data type.
How are you measuring success in your trigger-based targeting programs - and are you getting the most out of your lab and genomic investments?
Let’s talk at ASCO. Schedule a meeting with our team here
