Founder of Blueprint. I help companies stop sending emails nobody wants to read.
The problem with outbound isn't the message. It's the list. When you know WHO to target and WHY they need you right now, the message writes itself.
I built this system using government databases, public records, and 25 million job posts to find pain signals most companies miss. Predictable Revenue is dead. Data-driven intelligence is what works now.
Your GTM team is buying lists from ZoomInfo, adding "personalization" like mentioning a LinkedIn post, then blasting generic messages about features. Here's what it actually looks like:
The Typical Exagen Inc. SDR Email:
Why this fails: The prospect is an expert rheumatologist who sees dozens of diagnostic vendor pitches monthly. There's zero indication you understand their specific patient population, diagnostic challenges, or practice workflow. This is a generic feature dump. Delete.
Blueprint flips the approach. Instead of interrupting prospects with pitches, you deliver insights so valuable they'd pay consulting fees to receive them.
Stop: "I see you're hiring rheumatologists" (job postings - everyone sees this)
Start: "Your site screened 74 patients to enroll 23 in NCT05847621 - that's 31% screen failure vs peer average of 18%" (ClinicalTrials.gov with specific trial ID and enrollment metrics)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, trial IDs, facility details.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - competitive intelligence already analyzed, enrollment patterns already identified, biomarker trends already tracked - whether they buy or not.
These messages are ordered by quality score. The best plays come first, regardless of whether they use public, private, or hybrid data sources.
Alert pharma sponsors and CROs when their active lupus/CTD trials are running at sites with proven high enrollment velocity based on historical performance. Predict enrollment timeline acceleration or delays before they occur by cross-referencing public trial data with internal site performance metrics.
Trial directors are constantly worried about enrollment delays. When you tell them "your site enrolled 47% faster than national average" with specific numbers, you're providing intelligence they can't get from ClinicalTrials.gov alone. The actionable lead with contact info makes this immediately useful.
This play requires enrollment velocity data from sites using your AVISE tests in clinical trials, aggregated across 50+ trial sites with enrollment timelines.
Cross-referenced with ClinicalTrials.gov public data. This synthesis is unique to your business - competitors cannot replicate this velocity benchmarking.Show rheumatology practices their diagnostic utilization patterns compared to similar-size practices in their region. Reveal when they're under-ordering prognostic tests on new lupus diagnoses and missing early organ involvement prediction opportunities.
Practice efficiency directly impacts patient outcomes and revenue. When you tell them "your patients see you 4.7x before diagnosis vs top quartile of 2.8 visits" with dollar impact, you're surfacing a problem they didn't realize existed. The specificity proves this isn't generic benchmarking.
This play requires aggregated ordering patterns across 100+ rheumatology practices showing visit frequency, test type mix, and diagnostic timelines, segmented by practice size and geography.
This is proprietary data only you have - competitors cannot replicate this practice-specific benchmarking.Show rheumatology practices their test utilization efficiency compared to benchmarks. Reveal when they're ordering multiple redundant tests before reaching definitive diagnosis, wasting both time and money.
Healthcare efficiency is under constant scrutiny. When you tell them "you're testing 3.4x per lupus diagnosis vs top quartile of 1.8 tests - that's $890 extra per patient," you're quantifying waste they can immediately address. The concrete dollar amount makes this impossible to ignore.
This play requires test utilization data and diagnostic outcomes across customer practices, with cost analysis per diagnostic pathway.
This is proprietary data only you have - competitors cannot replicate this efficiency benchmarking.Alert trial site directors when their enrollment performance puts them in premium pricing territory. Show them they're leaving money on the table by not negotiating premium site fees based on velocity.
Site directors know they perform well, but rarely have benchmarking data to prove it. When you tell them "you enrolled 47% faster than national average - pharma sponsors pay 15-20% site fee premiums for enrollment velocity above 40%," you're handing them negotiating leverage.
This play requires enrollment velocity benchmarks from trial sites and industry knowledge of site fee pricing structures.
Combined with ClinicalTrials.gov public data to validate performance metrics. This synthesis is unique to your business.Show rheumatology practices their time-to-diagnosis performance compared to benchmarks. Reveal when they're taking significantly longer than peers to reach confirmed diagnosis, delaying treatment initiation.
Patient outcomes directly correlate with diagnostic speed in autoimmune disease. When you tell them "your lupus diagnosis takes 9.2 months vs 5.1 months for top quartile," you're highlighting a patient care issue they can immediately address. The protocol comparison offers a concrete solution.
This play requires time-to-diagnosis data from practices using your tests, aggregated anonymously across customer base with protocol analysis.
This is proprietary data only you have - competitors cannot replicate this diagnostic timeline benchmarking.Track FDA regulatory events and subsequent competitive response patterns. Alert pharma companies when major regulatory milestones (like biomarker qualification) trigger rapid competitive adoption, indicating they may need to update their trial protocols.
Pharma trial directors need to stay ahead of regulatory and competitive shifts. When you tell them "FDA granted CB-CAPs biomarker qualification on October 3rd, 2024 - five pharma companies updated trial protocols within 30 days," you're providing competitive intelligence with specific dates that helps them evaluate whether they're behind.
Track specific protocol amendments on ClinicalTrials.gov showing when competitors add advanced biomarker endpoints. Alert pharma sponsors when major players update trial designs, indicating potential competitive disadvantage if they don't follow suit.
Trial directors monitor competitors but may miss granular protocol changes. When you tell them "Janssen updated NCT05847621 protocol on November 14th to include CB-CAPs as secondary endpoint - that's the third major pharma this quarter," you're providing specific competitive intelligence with dates and trial IDs that helps them evaluate their own biomarker strategy.
Show rheumatology practices their referral conversion efficiency compared to benchmarks. Reveal when their referral-to-diagnosis timeline is significantly longer than peers, correlating with increased emergency department utilization by patients.
Referral conversion is both a patient care and revenue metric. When you tell them "your practice takes 8.7 months from referral to confirmed diagnosis vs top quartile of 5.2 months - patients waiting longer than 6 months have 2.3x higher ED utilization," you're connecting diagnostic delays to downstream healthcare costs and patient suffering.
This play requires referral timeline data and outcome tracking from customer practices, with correlation to downstream healthcare utilization.
This is proprietary data only you have - competitors cannot replicate this referral conversion benchmarking.Show rheumatology practices their connective tissue disease diagnostic timeline performance compared to benchmarks. Reveal when they're taking significantly longer than peers to diagnose CTD, delaying appropriate treatment.
CTD diagnosis is often more complex than lupus due to overlapping symptoms. When you tell them "your CTD patients wait 11.3 months for diagnosis vs top quartile of 6.8 months - that's a 4.5 month gap using early biomarker testing," you're highlighting a specific patient population where they're underperforming and offering a clear solution path.
This play requires anonymized time-to-diagnosis data from practices using your testing platform, segmented by condition type.
This is proprietary data only you have - competitors cannot replicate this condition-specific diagnostic timeline benchmarking.Track correlation between biomarker adoption and enrollment performance across active trials. Show pharma sponsors when trials that added advanced biomarker endpoints achieve faster enrollment, suggesting biomarker adoption may improve patient selection.
Enrollment speed is the #1 concern for trial sponsors. When you tell them "7 lupus trials added AVISE biomarker endpoints between September 15 and November 15, 2024 - your NCT05923456 enrollment is slower than 5 of those 7 trials," you're providing data-driven correlation between biomarker use and enrollment success that's hard to ignore.
Track competitive trial portfolio composition on ClinicalTrials.gov. Alert pharma companies when their entire trial portfolio lacks advanced biomarker endpoints that competitors are rapidly adopting, indicating potential competitive disadvantage in trial design.
Portfolio-level competitive analysis is harder to track than individual trials. When you tell them "11 active lupus trials using CB-CAPs as endpoints (up from 4 in January 2024) - your trial portfolio has zero CB-CAPs endpoints," you're highlighting a systematic gap in their biomarker strategy that requires attention at the program level.
Track specific competitor trial protocol updates showing biomarker endpoint additions. Alert pharma sponsors when multiple major competitors adopt the same biomarker strategy, indicating potential shift in regulatory expectations or competitive standards.
Competitive intelligence requires constant monitoring that most teams don't have bandwidth for. When you tell them "AbbVie, Bristol Myers, and Janssen all added AVISE biomarkers to lupus trial protocols between August-November 2024 - your NCT05923456 trial still uses traditional ANA-only enrollment criteria," you're providing specific competitive gap analysis that helps them evaluate whether they're falling behind industry standards.
Show rheumatology practices their ANA test ordering efficiency compared to benchmarks. Reveal when they're ordering multiple ANA tests per confirmed diagnosis, indicating inefficient diagnostic algorithms that delay definitive testing.
Test ordering efficiency directly impacts both costs and diagnostic speed. When you tell them "your practice orders 2.1 ANA tests per confirmed diagnosis vs top quartile of 1.3 tests - that's 62% more testing cycles," you're quantifying diagnostic inefficiency with a concrete percentage that practice administrators care about.
This play requires test ordering data and diagnostic outcome tracking from customer practices, with efficiency ratio calculation.
This is proprietary data only you have - competitors cannot replicate this test ordering efficiency benchmarking.Track IND filing activity and biomarker endpoint patterns across competitors. Alert pharma companies when multiple competitors file INDs with advanced biomarker endpoints while their program uses older response criteria, indicating potential competitive disadvantage.
IND filings signal serious program investment. When you tell them "AbbVie, Janssen, GSK, Bristol Myers, and three biotechs filed lupus INDs in Q4 2024 - all using advanced biomarker endpoints - your program is using 2019-era SLE response criteria," you're highlighting that their trial design may be outdated compared to where the field is moving.
Alert trial site directors when their enrollment performance qualifies them for higher-value Phase III trials. Show them how velocity percentile ranking translates to business development opportunities.
Site directors want to grow revenue but may not know how their performance compares. When you tell them "your 4.2-month enrollment rate puts you in top 8% of lupus trial sites nationally - fast-enrolling sites get priority consideration for Phase III trials with larger budgets," you're connecting their clinical performance to business development opportunities.
This play requires enrollment velocity benchmarks from trial sites using your tests, with industry knowledge of trial phase budget allocation patterns.
Combined with ClinicalTrials.gov public data to validate percentile rankings. This synthesis is unique to your business.Alert trial site directors when their screen failure rate is significantly higher than peers, indicating potential enrollment process inefficiencies. Suggest pre-screening protocols that higher-performing sites use.
Screen failures waste site resources and delay enrollment. When you tell them "your site screened 74 patients to enroll 23 - that's 31% screen failure vs peer average of 18%," you're quantifying waste they can address. The protocol comparison offer provides a solution path, though it edges slightly toward product pitch.
This play requires screen failure data from trial sites and enrollment records from ClinicalTrials.gov, with pre-screening protocol analysis.
Combined with public trial data to benchmark performance. This synthesis is unique to your business.Alert trial site directors about multiple pharma sponsors launching trials that need high-velocity sites. Provide aggregated opportunity list with trial coordinator contacts to help sites expand trial portfolio.
Site directors want to maximize trial portfolio revenue. When you tell them "your 4.2-month enrollment rate puts you in the top 8% nationally - AbbVie, Janssen, and GSK all have lupus programs starting Q2 2025 seeking high-velocity sites," you're aggregating opportunities they'd otherwise have to discover individually. The main weakness is needing to provide actual contacts IN the email rather than promising them.
This play requires enrollment velocity data from sites using your tests and pharma sponsor contacts from business development relationships.
Combined with ClinicalTrials.gov public data to identify upcoming trial opportunities. This synthesis is unique to your business.Alert trial site directors about multiple pharma sponsors prioritizing high-velocity sites for Q1 2025 trial launches. Provide site selection manager contacts to facilitate business development connections.
Site directors want warm introductions to pharma business development teams. When you tell them "your 4.2-month enrollment rate puts you in top 8% - found 3 sponsors (Regeneron, AstraZeneca, UCB) prioritizing high-velocity sites," you're providing targeted leads. The main weakness is promising contacts but not providing them immediately - should include names/emails in the message itself.
This play requires trial site performance tracking and pharma business development relationships with site selection manager contacts.
Combined with ClinicalTrials.gov public data to identify upcoming opportunities. This synthesis is unique to your business.Old way: Spray generic messages at rheumatologists. Hope someone replies.
New way: Use ClinicalTrials.gov and internal enrollment data to find trial sites with proven performance. Then show them specific benchmarking data about their velocity with verifiable metrics.
Why this works: When you lead with "Your site enrolled 23 patients in 4.2 months - that's 47% faster than the 7.9-month national average" instead of "I see you're conducting lupus trials," you're not another sales email. You're the person who analyzed 200+ trials to benchmark their performance.
The messages above aren't templates. They're examples of what happens when you combine real data sources with specific situations. Your team can replicate this using the data recipes in each play.
Every play traces back to verifiable data. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| ClinicalTrials.gov | trial_id, sponsor, trial_sites, enrollment_numbers, recruitment_status, trial_phase, inclusion_criteria, protocol_updates | Identifying active trials, tracking enrollment velocity, monitoring protocol amendments, competitive biomarker adoption |
| Internal Site Performance Metrics | patient_screening_velocity, specimen_turnaround_times, historical_enrollment_rates, screen_failure_rates | Benchmarking trial site performance, identifying high-velocity sites, pre-screening protocol analysis |
| Internal Customer Ordering Database | ordering_provider, practice_name, test_type, order_frequency, visit_patterns, diagnostic_timeline, test_utilization_ratios | Practice efficiency benchmarking, diagnostic protocol optimization, referral conversion tracking |
| FDA Biomarker Qualification Database | qualification_date, biomarker_name, indication | Tracking regulatory milestones, competitive biomarker adoption timing |
| FDA IND Database | filing_date, sponsor, indication, trial_design | Monitoring competitive trial program activity, biomarker endpoint trends |
| Internal Business Development Data | pharma_sponsor_contacts, trial_coordinator_details, site_selection_manager_contacts | Facilitating business development introductions, trial opportunity aggregation |