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 Reinsurance Broker Email:
Why this fails: The prospect is a reinsurance expert. They've seen this template 1,000 times. There's zero indication you understand their specific market position or portfolio 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 hiring underwriters" (job postings - everyone sees this)
Start: "Your Schedule P filings show $12.3M adverse reserve development in medical malpractice that didn't appear in your 10-Q" (statutory data with specific dollar amounts)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use regulatory filings with specific dollar amounts, loss ratios, and geographic concentrations.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, comparisons already calculated, gaps already identified - whether they buy or not.
These plays combine Paris Re's internal claims data with public regulatory filings to create insights competitors cannot replicate. Each message demonstrates precise understanding backed by verifiable data.
Cross-reference primary insurers' statutory state filings against their public 10-Q regional disclosures to identify states where loss ratios spiked but got buried in consolidated reporting. Target Chief Underwriting Officers and Reinsurance Directors at carriers preparing for treaty renewals who need to understand geographic loss deterioration.
Reinsurance buyers live in the details. When you surface state-by-line loss deterioration they haven't yet disclosed publicly, you demonstrate deep research that goes beyond what their broker provides. The treaty layer impact analysis makes this immediately actionable for January 1st renewals.
This play requires the recipient's NAIC statutory filings by state and line of business, cross-referenced with their public 10-Q geographic segment reporting.
The synthesis work - identifying hidden deterioration in regional rollups - creates value even though both data sources are public.Model primary insurers' coastal Florida ZIP code concentrations against actual Hurricane Ian loss data by construction year and elevation. Compare realized losses to pre-event RMS estimates to identify systematic underestimation patterns. Target Property & Casualty underwriters preparing for June 1st catastrophe treaty renewals.
Post-event validation using real loss data creates credibility that pre-event models can't match. When you show specific ZIP codes where Ian losses exceeded RMS estimates by 40%+, you're providing intelligence that directly impacts attachment point decisions and treaty pricing for the upcoming hurricane season.
This play requires NAIC property exposure data showing the recipient's ZIP code concentration in coastal Florida, combined with Hurricane Ian actual loss data and RMS model comparisons.
The synthesis of exposure data + validated loss data + model error analysis creates actionable intelligence for treaty renewals.Map primary insurers' agent networks against NAIC premium data to calculate county-level concentration. Compare to named competitors (Allstate, Progressive) in the same ZIP codes to quantify relative concentration risk. Target Chief Risk Officers and Reinsurance Directors at carriers with 3x+ competitor concentration in high-CAT zones.
Geographic concentration risk is always top-of-mind for reinsurance buyers, but most analyses use peer averages. When you name specific competitors (Allstate, Progressive) and show exact multiples (3.2x, 4.1x) in the same ZIP codes, you provide concrete benchmarking that makes the risk tangible and actionable.
This play requires NAIC premium data by county/ZIP for the recipient and named competitors (Allstate, Progressive), combined with agent network analysis.
The competitive benchmarking with specific multiples (3.2x, 4.1x) elevates this beyond generic concentration warnings.Calculate primary insurers' Galveston County premium concentration as percentage of total Texas wind book. Compare to Hurricane Ike actual loss distribution to derive exposure-to-loss ratio. Benchmark against industry average to quantify how much worse the carrier's concentration performed. Target carriers with 1.5x+ worse exposure-to-loss ratios preparing for coastal Texas treaty renewals.
The exposure-to-loss ratio metric (1.74x worse than industry) translates premium concentration into realized loss concentration using actual historical event data. This provides concrete evidence that their geographic mix amplifies losses beyond what premium share alone suggests, directly informing treaty attachment point decisions.
This play requires NAIC Texas wind premium data by county for the recipient, Hurricane Ike loss distribution data, and industry aggregate market share for benchmarking.
The exposure-to-loss ratio calculation transforms premium concentration into loss concentration using validated historical event data.Compare primary insurers' California property statutory loss ratios from state-level NAIC filings against their consolidated 10-Q disclosures. Calculate the gap between statutory state-level performance and public investor reporting. Target carriers with 10+ point gaps preparing for January 1st treaty renewals.
Reinsurance buyers understand that statutory filings tell the real story while public disclosures smooth results for investors. When you identify the specific gap (12 points) between their California statutory loss ratio and their 10-Q combined ratio, you demonstrate forensic-level research that validates their internal concerns about geographic concentration risk.
This play requires the recipient's NAIC statutory California property loss ratio and their public 10-Q consolidated combined ratio or property segment disclosures.
The 12-point gap between statutory and public disclosures is the key insight that demonstrates concentration risk.Old way: Spray generic reinsurance messages at job titles. Hope someone replies.
New way: Use regulatory filings and claims data to find carriers with specific loss deterioration or concentration risks. Then mirror that situation back to them with precise dollar amounts and loss ratios.
Why this works: When you lead with "Your California property loss ratio hit 71% - 12 points worse than your 10-Q combined ratio" instead of "I see you're growing in P&C," you're not another broker. You're the person who did the forensic analysis.
The messages above aren't templates. They're examples of what happens when you combine regulatory data sources with claims intelligence. Your team can replicate this using the data recipes in each play.
Every play traces back to verifiable public data combined with Paris Re's internal claims intelligence. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| NAIC Financial Data Repository | premium_earned, losses_incurred, loss_reserves, schedule_p_analysis, risk_based_capital | Primary insurer loss ratios, reserve adequacy, underwriting profitability |
| FEMA NFIP Claims Dataset | damage_amount_building, claim_payment, loss_date, property_location, flood_zone | Historical flood loss patterns, geographic accumulation risk |
| USGS Earthquake Hazard Data | earthquake_magnitude, location_coordinates, shakemap_intensity, hazard_probability | Seismic risk assessment, multi-hazard zone identification |
| NOAA Hurricane Data | hurricane_track, wind_speed, storm_surge_height, damage_estimates, affected_areas | Hurricane loss modeling, seasonal forecasting, coastal exposure validation |
| Artemis CAT Bond Dashboard | cat_bond_issuance, ils_outstanding_capital, pricing_spreads, trigger_mechanisms | Alternative risk transfer market intelligence, capacity gap analysis |
| Paris Re Internal Claims Data | actual_claims_paid, loss_development_patterns, cedant_identifier, claims_by_geography | Reserve adequacy validation, geographic concentration risk, loss ratio benchmarking |