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 Pillr Health SDR Email:
Why this fails: The prospect manages 340B compliance daily. They've seen this template 1,000 times. There's zero indication you understand their specific situation. 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 compliance people" (job postings - everyone sees this)
Start: "Your 340B entity was approved March 2024 and you're managing 4 contract pharmacy locations in month 8" (340B OPAIS database with exact approval date and site count)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, record numbers, facility addresses.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, deadlines already pulled, patterns already identified - whether they buy or not.
These messages demonstrate precise understanding of the prospect's current situation and deliver immediate actionable value. Every claim traces to specific government databases or proprietary benchmarks.
Monitor 340B OPAIS daily reports for contract pharmacy registrations, then proactively deliver integration requirements, deadlines, and direct contact information for the pharmacy chain's 340B liaison.
You're surfacing critical information the prospect needs immediately: the integration deadline they might have missed, plus direct contact details that save them hours of research. This is genuinely helpful whether they buy or not.
This play requires monitoring 340B OPAIS daily reports for contract pharmacy additions, plus knowledge of CVS integration requirements and liaison contact information.
The synthesis of public registration data with chain-specific integration timelines and contacts is unique to your business.Monitor 340B OPAIS for contract pharmacy registrations and immediately reach out with specific details (exact date, locations) plus full contact information for the pharmacy chain's 340B liaison.
The exact date and specific CVS locations prove you're tracking their program in real-time. The complete contact information means they can act immediately - this is genuinely useful intelligence.
This play requires monitoring 340B OPAIS for contract pharmacy additions plus CVS integration requirement knowledge and liaison contact information.
Providing complete contact information and timeline creates immediate value.Monitor manufacturer 340B policy announcements and compile a comprehensive tracker with effective dates, affected drugs, manufacturer liaison contacts, and workaround strategies.
Policy changes directly impact 340B savings. A forward-looking tracker with specific counts (17 manufacturers, 5 affecting contract pharmacies) plus actionable contacts and workarounds provides immediate operational value.
This play requires monitoring of manufacturer 340B policy announcements combined with analysis of which policies affect contract pharmacy operations.
The synthesis of policy changes with contact information and workaround strategies creates unique value.Cross-reference manufacturer 340B policy changes with the entity's contract pharmacy network from OPAIS to identify which specific manufacturers now restrict their pharmacies, then provide contact list and alternative strategies.
You've done the synthesis work: tracking manufacturer policies across 23 manufacturers and matching them to their specific CVS and Walgreens locations. Alternative strategies make this immediately actionable.
This play requires monitoring manufacturer 340B policy databases cross-referenced with entity's contract pharmacy registrations from HRSA OPAIS.
The cross-referencing of manufacturer policies with the prospect's specific pharmacy network creates unique targeting precision.Use aggregated audit deficiency data from your customer base to show DSH hospitals (200-400 beds) exactly what the median facility experiences, broken down by violation type.
Benchmarking is incredibly valuable for compliance teams. Knowing that 34% of deficiencies are duplicate discount violations helps them prioritize audit prep. This is proprietary intelligence they can't get elsewhere.
This play requires aggregated audit deficiency data from your customer base, categorized by entity type and violation category (minimum 127+ DSH hospitals analyzed).
This is proprietary data only you have - competitors cannot replicate this insight without their own customer audit database.Identify newly registered 340B entities with multi-site operations (from OPAIS), calculate their timeline (months since approval), and offer a month-by-month compliance calendar covering the critical HRSA review window.
You're providing a practical tool they can use immediately to stay ahead of HRSA's review. The specificity to their timeline (8 months in, 4 sites) makes this feel custom-built for their situation.
Monitor 340B OPAIS for contract pharmacy additions, then immediately deliver a customized 90-day compliance checklist specific to the pharmacy chain's billing systems.
The exact date and locations show real-time tracking. A checklist specific to CVS's billing systems is immediately useful - they can act on this today whether they respond or not.
This play requires monitoring HRSA 340B OPAIS for contract pharmacy additions plus internal knowledge of CVS billing integration requirements.
The checklist customized to CVS systems provides immediate actionable value.Use HRSA UDS data to identify FQHCs with high patient volume but low grant funding per patient, then offer a customized 340B optimization model specific to their patient demographics.
You're using their specific patient count and funding ratio (verifiable from HRSA reports), then offering a model tailored to their exact situation. The analysis helps them identify revenue optimization opportunities.
Use aggregated audit deficiency data from your customer base to provide DSH hospitals with specific benchmarks (median deficiencies, breakdown by violation type) for facilities matching their profile.
Benchmarking against peer hospitals helps compliance teams prioritize. The 34% duplicate discount stat is specific and actionable - they know exactly where to focus remediation efforts.
This play requires aggregated audit deficiency data from your customer base, categorized by entity type and common violation patterns (minimum 127+ facilities analyzed).
This is proprietary data only you have - competitors cannot replicate this benchmark without their own customer audit database.Use HRSA UDS data to identify FQHCs with high patient volume but low grant funding per patient (bottom quartile), demonstrating they're stretched thin and need 340B optimization to close the gap.
The specificity of their exact patient count and grant funding proves you've done research. The comparison to regional median ($133 vs $187 per patient) makes the financial pressure concrete and verifiable.
Monitor 340B OPAIS for contract pharmacy additions, then reach out highlighting the exact pharmacies added (with locations) and the compliance checkpoints each new pharmacy relationship adds.
Real-time tracking of their contract pharmacy additions proves you're monitoring their program closely. The "47 checkpoints" number (while oddly specific) highlights the compliance complexity of expansion.
This play requires monitoring of HRSA 340B OPAIS database for contract pharmacy registration changes, cross-referenced with timing data.
Real-time monitoring enables timely outreach when integration support is most needed.Use HRSA UDS data to show FQHCs their exact patient volume and grant funding, compare to state median, and position 340B savings as a way to close the funding gap.
The data points are verifiable from HRSA reports. The 40% gap analysis provides useful context. The question is straightforward and easy to answer.
Identify newly registered 340B entities (from OPAIS) with multi-site operations, calculate their timeline since approval, and highlight the upcoming HRSA compliance review window (months 12-18).
The specific approval date (March 2024) and site count (4) prove research. The 12-18 month review timeline is actionable. The question about monthly audits is clear and easy to answer.
Identify newly registered 340B entities with multi-site operations (from OPAIS), calculate their timeline, and cite the higher audit deficiency rate for first-year multi-site entities.
The exact approval date and site count show specific research. The routing question about split-billing coordination is clear and relevant to multi-site operations.
Monitor manufacturer 340B policy changes, cross-reference with entity's contract pharmacy network (from OPAIS), and alert when a major manufacturer restricts access to high-value drugs affecting their pharmacies.
The specific manufacturer (AbbVie), drug (Humira), and effective date (October 1st) are verifiable. Identifying the 3 affected locations shows you've done the cross-referencing work.
This play requires tracking of manufacturer 340B policy changes cross-referenced with the entity's contract pharmacy network from HRSA OPAIS.
The volume estimate ($840K) should be removed unless you have actual drug utilization data - it undermines credibility.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use public data to find 340B entities in specific situations (new registrations, contract pharmacy expansions, manufacturer restrictions). Then mirror that situation back with evidence.
Why this works: When you lead with "Your 340B entity was approved March 2024 and you're managing 4 contract pharmacies in month 8" instead of "I see you participate in 340B," you're not another sales email. You're the person who did the homework.
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 public data or proprietary benchmarks. Here are the key sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| 340B OPAIS (Office of Pharmacy Affairs Information System) | covered_entity_name, entity_classification, registration_date, approval_date, contract_pharmacy_daily_report | Identifying all 340B participants, tracking registration dates, monitoring contract pharmacy additions, manufacturer policy changes |
| HRSA Data Warehouse - Health Center Program Data | patient_volume, grant_award, awardee_number, location_address, payor_mix | Identifying FQHCs with high patient volume but low grant funding per patient |
| Internal Audit Deficiency Database | aggregated_deficiency_counts_by_entity_type, violation_category, median_deficiencies | Providing proprietary benchmarks for audit deficiency rates by hospital type and size |
| Internal CVS Integration Requirements | split_billing_requirements, integration_timeline, 340B_liaison_contacts | Delivering actionable integration checklists and contact information for contract pharmacy onboarding |
| Internal Manufacturer Policy Analysis | manufacturer_restrictions, affected_drugs, workaround_strategies, liaison_contacts | Cross-referencing manufacturer policy changes with entity's pharmacy network to identify impacts |