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 Sola Payments SDR Email:
Why this fails: The prospect is an expert. 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 FinCEN MSB #1234567 registered November 15th - 4 of 6 MSBs from that week already switched processors due to chargeback handling" (government database with record number + pattern detection)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, record numbers, registration IDs.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, counterparty information already pulled, patterns already identified - whether they buy or not.
Company: Sola Payments
Core Problem: Merchants struggle with impersonal, complex payment processing that lacks strategic guidance and personalized support, limiting their ability to optimize transactions, resolve disputes, and monetize payments effectively.
Target ICP: Mid-market to enterprise ISVs, software platforms, and high-risk merchants (50-5000+ employees) who need PayFac solutions for payment monetization, have complex integration requirements, or face high chargeback rates in verticals like iGaming, forex, alcohol, and e-commerce.
Primary Persona: VP of Product / Head of Monetization (for platforms), with secondary buyers including CRO, CFO, Director of E-commerce, and Platform Product Manager - decision-makers responsible for payment processing revenue, transaction cost optimization, and merchant/customer experience in payment flows.
These messages demonstrate precise understanding of the prospect's situation (PQS) or deliver immediate actionable value (PVP). Every claim traces to specific data sources with verifiable record numbers.
Cross-reference TTB alcohol permit holders with court payment dispute filings, then pull the counterparty's litigation history to identify settlement patterns and provide attorney contact information for expedited resolution.
You're surfacing information that dramatically accelerates dispute resolution. Knowing the counterparty settles 67% of cases at 73% of original amount within 45 days transforms a potentially 180+ day legal battle into a quick negotiation. The settlement attorney contact is immediately actionable.
This play requires pulling multiple court records for the counterparty, calculating settlement patterns, and finding attorney contact information through legal databases or public court filings.
This synthesis is proprietary - competitors cannot replicate without doing extensive legal record research per prospect.Pull court records for alcohol beverage permittees with payment disputes, then provide detailed counterparty information including contact details and transaction specifics to enable direct settlement conversations.
Providing the actual counterparty names, contact information, and transaction dates eliminates weeks of legal discovery. The prospect can immediately reach out for direct settlement, potentially saving thousands in legal fees and months of litigation time.
Requires pulling court dispute records, identifying counterparty, cross-referencing their litigation history, and finding settlement patterns with attorney contact information.
This level of legal record synthesis is unique and cannot be replicated by competitors without extensive research per individual prospect.Cross-reference NFA forex platform registrations with court chargeback records to identify repeat filers who've filed multiple disputes against the same platform, then provide their contact information and transaction details for direct settlement outreach.
Repeat filers represent the highest-cost dispute scenarios. Identifying the actual customer names (Sarah Chen, Michael Torres, David Kim) with their chargeback counts enables immediate direct settlement conversations, potentially saving tens of thousands in legal fees per dispute.
Requires synthesis of court/chargeback records with customer transaction data to identify repeat filers, extract contact information, and map filing patterns.
This synthesis of legal records with transaction data is proprietary and cannot be replicated without extensive court record research.Pull court records for NFA-registered forex platforms with multiple chargeback disputes, then analyze patterns by state, transaction size, and timing to provide actionable fraud prevention insights.
Showing the actual pattern (9 of 12 chargebacks from 3 states within 48 hours of $5K+ withdrawals) provides immediate fraud rule tuning opportunities. The prospect can implement state-based holds or withdrawal thresholds based on their own historical patterns.
Requires pulling court chargeback records, extracting transaction details, and mapping patterns by state, amount, and timing for the specific prospect.
This pattern analysis synthesis is proprietary and requires real data analysis that competitors cannot replicate without extensive court record research.Pull court records for NFA-registered forex platforms with chargeback disputes, then provide the full counterparty list with names, amounts, filing dates, and contact information for repeat filers who might settle directly.
Providing the actual list of 12 disputes with counterparty details eliminates legal discovery costs. Identifying the 3 repeat filers who might settle directly provides immediate action items that could save tens of thousands in legal fees.
Requires pulling and synthesizing court records with chargeback dispute filings, extracting counterparty information, and identifying repeat filers for the specific prospect.
This legal record synthesis is proprietary and cannot be replicated by competitors without extensive court research per prospect.Pull SEC Form Funding Portal filings for crowdfunding platforms, extract their transaction volumes and fee structures, then model 4 different payment monetization approaches based on how similar platforms actually charge, with exact compliance language and projected revenue ranges.
The prospect gets a ready-to-implement spreadsheet comparing 4 real-world fee structures with compliance language pulled from actual SEC filings. The revenue projections ($28K-46K) are specific to their volume and provide clear ROI justification.
Requires pulling fee disclosure language from multiple SEC Form Funding Portal filings for similar-sized portals and building a comparison model with compliance considerations.
This synthesis of multiple SEC filings into a ready-to-use model is proprietary and demonstrates real work done on the prospect's behalf.Cross-reference TTB alcohol permit expiration dates with court payment dispute filings for the same entity to identify permit holders facing renewal with active financial disputes that could be flagged by TTB.
Creating urgency by connecting two unrelated data points (permit renewal + active disputes) that the prospect may not have connected themselves. TTB flagging during renewal could delay or jeopardize their license, making dispute resolution suddenly time-sensitive.
Requires tracking TTB permit expiration dates (calculated from issue dates) and cross-referencing with court payment dispute filings for the same legal entity.
This synthesis of regulatory timelines with litigation records is proprietary and demonstrates deep research into the prospect's specific situation.Pull SEC Form Funding Portal filings for similar-volume crowdfunding platforms to extract their exact fee structures, compliance language, and transaction splits, then provide a comparison document showing how peers monetize payments.
The prospect gets ready-to-use compliance language and fee structure templates from actual SEC filings of peers. They can model their own fees after proven structures without hiring expensive compliance consultants.
Requires pulling and comparing fee structures from multiple SEC Form Funding Portal filings for similar-sized portals.
This synthesis of regulatory filings is somewhat proprietary but could be replicated by competitors with research effort.Track FinCEN MSB registrations to identify newly registered MSBs in their first 180 days, then provide a fraud detection checklist based on the 23 most common fraud patterns observed across MSB customers during this critical window.
The "23 most common patterns" is concrete and suggests real data collection across multiple MSBs. Providing a ready-to-use checklist delivers immediate value even if they don't respond or buy.
Assumes internal data tracking fraud patterns across new MSB customers in their first 180 days of operation, with pattern identification and categorization.
This aggregated fraud pattern data from your MSB customer base is proprietary, though the checklist itself could be generic fraud prevention advice.Track FinCEN MSB registrations by cohort (same week) and monitor payment processor changes through website checkout flows, Terms of Service updates, or payment badges to identify which processors the cohort switched FROM and why.
Peer comparison is powerful. Learning that 4 of 6 MSBs from the same registration week already switched processors creates FOMO and suggests the prospect might face similar issues. The "why they switched" information is valuable competitive intelligence.
Requires tracking MSB registrations plus monitoring payment processor changes through website payment badges, Terms of Service updates, or checkout flow changes over time.
This longitudinal tracking of processor switching is moderately proprietary but the "why they switched" claim might be hard to verify without direct conversations.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use public data to find companies in specific painful situations. Then mirror that situation back to them with evidence.
Why this works: When you lead with "Your FinCEN MSB #1234567 registered November 15th - 9 of 12 Q4 chargebacks came from 3 states within 48 hours of $5K+ withdrawals" instead of "I see you're in the payments space," 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. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| FinCEN MSB Registrant Search | legal_name, dba_name, registration_date, msb_activities, states_of_operation, number_of_branches | Identifying newly registered Money Services Businesses and cryptocurrency exchanges entering chargeback spike windows |
| TTB Alcohol Beverage Permittees List | permittee_name, permit_type, permit_number, address, state, permit_issue_date | Identifying alcohol producers/distributors with high-value transaction disputes and permit renewal pressures |
| NFA BASIC Database | firm_name, nfa_id, registration_status, disciplinary_history, city, state | Identifying forex trading platforms with extreme chargeback exposure and regulatory complexity |
| SEC Regulation Crowdfunding - Funding Portals List | portal_name, sec_file_number, website, registration_date, finra_membership_status | Identifying crowdfunding platforms monetizing payments sub-optimally |
| Court Payment Dispute Records | disputed_amount, filing_date, counterparty_identification, resolution_status | Tracking chargeback disputes, identifying repeat filers, and analyzing settlement patterns |
| Internal Chargeback Pattern Data | chargeback_rates_by_vertical, dispute_timelines, fraud_patterns_post_onboarding | Providing aggregated benchmarks and fraud detection patterns across customer base |