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 GoodRx SDR Email:
Why this fails: The pharmacy manager already knows their abandonment rate. They've seen this pitch from every discount card company. There's zero indication you understand their specific market, insurance mix, or operational challenges. 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 focused on patient satisfaction" (generic assumption - everyone claims this)
Start: "Your pharmacy's prior-auth approval rate is 68% - nearby independents average 80%" (specific performance gap with peer benchmark)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use verifiable data with rates, comparisons, and geographic context.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, benchmarks already calculated, patterns already identified - whether they buy or not.
Company: GoodRx
Core Problem: Consumers face unpredictably high prescription drug costs at checkout, forcing them to choose between medication and other expenses. Healthcare providers cannot easily show patients affordable medication options, reducing treatment compliance and satisfaction.
Target ICP:
Primary Buyer Persona: Pharmacy Manager / Operations Lead OR Clinic Patient Services Manager responsible for managing prescription fill rates, patient satisfaction scores, claim rejections, and medication adherence metrics.
These messages provide actionable intelligence before asking for anything. The prospect can use this value today whether they respond or not.
Use aggregated prescription search and redemption data to tell specialty pharmacy managers the exact abandonment rate for specific biologics in specific ZIP codes, compared to regional specialty pharmacy benchmarks.
The specificity is stunning - their pharmacy, specific drug (Humira), specific ZIP code (75201), and a 19-point gap vs competitors. This level of precision proves you're not guessing. The copay assistance friction diagnosis is actionable and something they can fix immediately.
This play requires aggregated prescription search and redemption data showing abandonment rates by specialty pharmacy, drug NDC, and patient ZIP code across 1000+ anonymous patient lookups per specialty drug per state.
This is proprietary data only GoodRx has - competitors cannot replicate this play without similar platform visibility into prescription search behavior and abandonment patterns.Use aggregated prescription abandonment data to identify specialty pharmacies with significant geographic anomalies - specific ZIP codes where abandonment is dramatically higher than the pharmacy's average in other areas.
Calling out 3 exact ZIP codes with 47% abandonment vs 19% in other ZIPs is extraordinarily specific. This suggests a solvable operational problem (localized copay assistance or access issues) rather than a systemic failure. The question is reasonable and invites investigation.
This play requires prescription abandonment tracking by specialty pharmacy, specific biologic drug, and patient ZIP code to identify geographic patterns in abandonment behavior.
This synthesis of abandonment data by ZIP code is unique to GoodRx's platform visibility.Use aggregated prior-authorization outcome data to tell independent pharmacy managers their approval rate with specific PBMs compared to local peer pharmacies, identifying which PBM relationships are underperforming.
The precision is exceptional - their pharmacy, specific PBM (CVS Caremark), specific city (Dallas), and a 36-point gap vs local independents. This immediately suggests either documentation issues or specific drug restrictions. The question about tracking is reasonable and invites collaboration.
This play requires prior-authorization approval rate tracking by pharmacy, specific PBM, and comparison against local independent pharmacy benchmarks in the same geographic market.
This requires visibility into insurance workflow outcomes across multiple pharmacies - proprietary to GoodRx's platform.Use aggregated prior-authorization outcome data from partner pharmacies to benchmark individual pharmacy performance against local peers, identifying approval rate gaps that indicate operational inefficiencies.
This is specific to their pharmacy's performance vs peers (68% vs 80%). The 12-point gap is actionable - they can investigate which payers are causing the problem. The ask is low-commitment (just send me data) and the data is valuable whether they buy GoodRx or not.
This play assumes GoodRx has aggregated prior-auth success rates from partner pharmacies by geographic area and can benchmark individual pharmacies against local peers.
This benchmarking capability is unique to platforms with visibility across multiple pharmacy operations.Use aggregated medication adherence data from FQHC partners to benchmark individual facility performance against regional peers with identical patient demographics, identifying medication class-level gaps.
The 14-point adherence gap (61% vs 75%) is specific to their facility and comparable FQHCs. The UDS impact is a real concern for FQHC managers who are measured on these metrics. The medication class breakdown would be immediately actionable.
This play assumes GoodRx has aggregated medication adherence data from FQHC partners and can benchmark facilities against regional peers, possibly combining UDS reporting data with prescription fill patterns.
This benchmarking requires partnership data across multiple FQHCs - unique to GoodRx's healthcare provider integrations.Calculate lost revenue from high-cost biologic abandonment using aggregated abandonment rates and typical specialty pharmacy margins, showing the financial impact of cost barriers.
The $180K figure is attention-grabbing and tied to a specific abandonment rate (34%) vs competitor benchmark (20%). The biologic-level breakdown would be immediately actionable. The question is easy to answer.
This play assumes GoodRx can calculate abandonment rates for specialty pharmacies by biologic medication and estimate lost revenue based on typical specialty pharmacy margins and prescription volume.
This financial impact analysis requires both abandonment tracking and industry margin benchmarks.Use aggregated prior-authorization denial patterns to estimate monthly prescription fill losses and revenue impact for individual pharmacies, showing the financial cost of denial inefficiencies.
The specific number (32 fills) feels credible and grounded in data. The dollar impact ($2,720 monthly) is immediately relevant to their P&L. The medication breakdown would be actionable for addressing the highest-impact denials first.
This play assumes GoodRx can analyze prior-auth denial patterns from partner pharmacies and estimate lost prescription volume based on denial rates and typical fill patterns.
This financial impact modeling requires both denial tracking and prescription volume estimation capabilities.Use aggregated refill timing data to identify FQHCs where diabetic patients refill insulin significantly later than peer facilities, indicating cost barriers that impact HEDIS diabetes measures.
The specificity to their facility and specific medication class (insulin) makes this highly relevant. HEDIS impact is a direct concern for FQHCs measured on diabetes outcomes. The 7-day difference is concerning and actionable.
This play assumes GoodRx can analyze refill timing patterns for specific medication classes at individual FQHCs and compare against peer facility benchmarks.
This therapeutic-area benchmarking requires partnership data across multiple FQHCs with medication class visibility.Use aggregated prescription refill timing data to identify FQHCs where patients refill chronic medications significantly later than peer facilities, suggesting cost barriers are causing medication rationing.
The comparison (8 days vs 3 days) is specific and alarming. The cost barrier insight explains a problem they likely observe but can't quantify. The breakdown by medication class and payer mix would be immediately actionable.
This play assumes GoodRx can analyze prescription refill timing patterns from partner FQHCs and calculate average refill delays by facility, comparing against peer benchmarks.
This refill delay analysis requires prescription fill tracking across multiple facilities with timing precision.Old way: Spray generic messages at job titles from ZoomInfo. Hope someone replies.
New way: Use internal data to benchmark prospects against peers. Then show them the gap with specific numbers.
Why this works: When you lead with "Your prior-auth approval rate is 68% - nearby independents average 80%" instead of "Have you considered price transparency?", you're not another sales email. You're the person who has the data.
The messages above aren't templates. They're examples of what happens when you combine proprietary data sources with peer benchmarking. 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 |
|---|---|---|
| GoodRx Internal Prescription Data | abandonment_rate, prior_auth_success_rate, refill_timing, patient_zip, drug_ndc, pharmacy_id | Peer benchmarking, abandonment scoring, adherence analysis |
| State Board of Pharmacy Licenses | pharmacy_name, pharmacy_address, license_number, pharmacy_type, license_status | Pharmacy identification and verification |
| HRSA FQHC Database | facility_name, patients_served, uninsured_percentage, medicaid_percentage | FQHC patient demographics and peer grouping |
| URAC Specialty Pharmacy Directory | organization_name, specialties, accreditation_status, accreditation_expiration_date | Specialty pharmacy identification and verification |
| 340B OPAIS | covered_entity_name, contract_pharmacy_name, program_participation_status | 340B pharmacy identification |
| CMS Rural Health Clinic Dataset | facility_name, certification_date, rural_classification, enrollment_status | RHC identification and timing |