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 Hub International 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 Dallas facility at 1250 Industrial Blvd has 4 open OSHA serious violations from March 3rd" (government database with record number)
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.
Company: Hub International (hubinternational.com)
Core Problem: Mid-market and enterprise organizations struggle to manage complex, multi-line insurance programs across distributed locations while ensuring regulatory compliance and minimizing coverage gaps that lead to uninsured losses.
Target ICP: Multi-location operators in highly regulated industries (healthcare, manufacturing, construction, transportation) with 100+ employees and $50M-$1B+ revenue. Organizations facing complex risk exposure, compliance requirements, and multi-carrier program management challenges.
Primary Buyer Persona: Chief Financial Officer, Risk Manager, VP of Operations, or Chief Legal Officer responsible for managing multi-location insurance programs, ensuring regulatory compliance, controlling total cost of risk, and eliminating coverage gaps across distributed operations.
These messages demonstrate precise understanding of prospect situations using verifiable government data and proprietary insights. Ordered by quality score (highest first).
Use Hub's historical claims data on the customer's own facilities to identify current locations showing identical risk patterns to past locations where major claims occurred. This creates a "time machine" effect - showing them their future based on their own past.
You're using their own historical data as the evidence. This is irrefutable - they can't dismiss it as "not applicable to us" because it IS them. The specific cost figure from their past claim makes it tangible. You're helping them learn from their own mistakes, which is genuinely valuable.
This play requires the recipient's historical claims data and facility patterns from your system. Only works for existing customers with multi-year history.
Only works for upselling existing customers, not cold acquisition.Combine the prospect's public near-miss and safety data with Hub's proprietary predictive model (built on 60+ comparable facilities) to deliver a specific probability forecast with a 45-day timeline. This makes the prediction testable and urgent.
The 45-day window creates urgency while giving them time to act. The 68% probability with sample size (60 warehouses) makes the prediction credible, not sensational. You're offering recommended interventions, making this immediately actionable. The pattern explanation shows expertise.
This play requires Hub's proprietary predictive model built on aggregated near-miss data, first-aid incidents, and training records across 60+ comparable customer facilities. Must track which risk signatures correlate with recordable injuries.
Combined with public OSHA data to verify facility compliance history. This synthesis is unique to Hub's business.Use Hub's historical claims data to match current risk patterns at one facility to past incidents at another of their facilities. Show them they're about to repeat a costly mistake with specific dollar figures from their own history.
Uses their own historical data as proof. They can't dismiss this as "not relevant to us" - it IS them. The exact cost from Kansas City ($94K) makes the risk tangible. Pattern details are verifiable in their own records. Helps them prevent repeating costly mistakes.
This play requires the recipient's multi-year facility data and claims history from your system. Only works for existing customers with historical claims in your database.
Only works for upselling existing customers, not cold acquisition.Combine facility-specific public safety data with Hub's predictive model to deliver a specific date-based injury forecast. Multiple risk factors (near-misses, maintenance delays, training gaps) create urgency and show thorough analysis.
Specific address and exact metrics show real research. Multiple risk factors identified (near-misses, maintenance, training) demonstrate thorough analysis. 78% probability with specific date (March 15th) is testable and urgent. Maintenance and training gaps are immediately actionable. Offers practical intervention checklist.
This play requires Hub's system to track near-miss incidents, equipment maintenance schedules, and training records across customer facilities, with predictive modeling to correlate these factors to injury likelihood.
Combined with public OSHA data for facility compliance history. This synthesis is unique to Hub's risk management platform.Use Hub's internal pattern recognition (built on data from similar warehouses) combined with the facility's public OSHA data to identify pre-claims risk indicators. Offer to extend the analysis across their entire 12-location portfolio.
Specific address and exact incident counts show real work. The predictive insight is genuinely valuable - catching issues before they become claims. 60-day and 90-day timeframes are precise and urgent. Pattern recognition demonstrates expertise. Offering analysis across all 12 locations creates broader value.
This play requires Hub to aggregate workers' comp claims data, OSHA reports, and near-miss data across customers to identify pre-claims patterns that predict recordable injuries 90 days out.
Combined with public OSHA near-miss reports to create predictive insights. This pattern recognition is proprietary to Hub.Use Hub's proprietary risk scoring model (built on claims history, inspection data, and facility characteristics) to identify the highest-risk locations in a multi-site operator's portfolio. Show concentration risk - 3 locations driving 60% of total exposure.
Naming specific neighborhoods (River North, Wicker Park, Lincoln Park) is verifiable and shows local knowledge. Risk scores are precise and location-specific (8.2, 7.9, 8.4 out of 10). The 60% concentration insight is immediately actionable for risk allocation. Identifies portfolio-wide risk concentration. Easy yes/no to get site-specific factors.
This play requires Hub to develop proprietary risk scoring models using claims history, inspection data, and facility characteristics across customer locations. Must calculate exposure concentration by location.
This is proprietary data only Hub has - competitors cannot replicate this play without similar risk modeling infrastructure.Analyze Hub's internal near-miss and incident data across the prospect's full 23-facility portfolio to identify concentration risk - 5 locations with 67% of near-miss volume but only 35% of workforce. This reveals where to focus safety resources.
Specific city names are verifiable against their actual locations. The percentage analysis (67% near-misses from 35% of workforce) shows real analytical work. Concentration risk insight is immediately valuable for resource allocation. Covers their full portfolio (23 facilities) showing comprehensive analysis. Easy yes to get full breakdown.
This play requires Hub to aggregate near-miss data, workforce size, and risk indicators across all customer locations to identify concentration patterns and allocate safety resources optimally.
Combined with facility-level workforce data to calculate concentration risk. This analysis is unique to Hub's risk management platform.Target manufacturing facilities with concurrent OSHA serious violations AND EPA enforcement actions in the past 12 months. These companies face compounding regulatory scrutiny, multiplied penalty exposure, and insurance carriers re-evaluating coverage - indicating systemic safety and environmental management failures.
Exact dollar amounts and dates prove you did real research. Management system failure insight shows expertise - this is why penalties escalate with concurrent violations. The penalty escalation implication is specific and valuable. Coordination question addresses a real organizational challenge. Shows understanding of cross-agency enforcement complexity.
Combine facility-specific public near-miss data with Hub's predictive model (built on 40 similar facilities) to deliver a 73% probability forecast with 120-day timeline. Multiple actionable risk factors create urgency.
Specific location and equipment type (forklift) shows focus. Exact count (5 near-misses) and date (since November 1st) add precision. Predictive model with sample size (40 facilities) makes the 73% probability credible. 120-day window gives them time to intervene. One-word answer CTA lowers friction.
This play requires Hub to track near-miss incidents, corrective actions, and use predictive modeling across 40+ customer facilities to correlate forklift near-misses without intervention to recordable injuries.
Combined with public OSHA near-miss data to create predictive risk scores. This modeling is proprietary to Hub.Target manufacturing facilities showing compound compliance failures across both OSHA and EPA. Concurrent enforcement triggers cross-agency scrutiny and stacked penalties, creating urgent need for comprehensive insurance review and risk management intervention.
Exact address and specific violation counts prove you did real research on this facility. Dates (March 3rd, March 18th) show this is recent and current. Cross-agency risk is a non-obvious insight most brokers miss. Simple routing question makes it easy to respond. 'Stacked penalties' is a concrete implication of dual enforcement.
Use Hub's proprietary slip-and-fall risk scoring model (built on facility characteristics, claims history, and environmental factors) to identify the highest-risk location in a multi-unit restaurant group. Explain the scoring factors to demonstrate thoroughness.
Specific address and precise risk score (8.9 out of 10) show detailed analysis. Explaining scoring factors (floor, traffic, weather) demonstrates methodology transparency. Portfolio context (22 locations) proves comprehensive analysis. Highest risk designation creates urgency. Offers actionable mitigations.
This play requires Hub to develop proprietary slip-and-fall risk scoring using facility characteristics, claims history, and environmental factors. Must calculate scores across all locations to identify highest risk.
This is proprietary data only Hub has - competitors cannot replicate this play without similar risk scoring infrastructure.Target skilled nursing facilities with 1-2 star CMS ratings showing two consecutive quarters of low performance. These facilities are SFF candidates facing mandatory compliance intervention, increased liability exposure, and potential loss of Medicare/Medicaid reimbursement.
Specific facility name and exact rating progression show real research. Date precision (December 8th survey) adds credibility. SFF threat is immediate and concrete for this industry. Easy routing question lowers response friction. Address confirms you know which specific location is at risk.
Target manufacturing facilities with overlapping OSHA and EPA enforcement deadlines in the same month. This creates coordination challenges and compressed response timelines that most companies struggle to manage without broker support.
Specific violation types (lockout/tagout, machine guarding, stormwater) show detailed research. Exact dates (February 8th, February 14th) create timeline precision. Overlapping March 22nd deadline is the non-obvious problem requiring coordination. Coordination question is genuinely useful and shows understanding of compliance complexity.
Target ambulatory surgery centers showing quarter-over-quarter increases in surgical site infection rates above CMS quality thresholds. These facilities face imminent CMS quality penalties, medical malpractice claim exposure, and potential loss of Medicare certification.
Specific facility and exact infection percentage (3.2%) show real data work. Quarter-over-quarter comparison (1.1% to 3.2%) demonstrates trend analysis. CMS threshold (2.5%) and penalty effective date (April 2025) make it actionable and urgent. Easy question about investigation ownership creates simple next step.
Target facilities where both agencies cited management system failures - this pattern escalates penalties and triggers more aggressive enforcement. Shows understanding of how violations compound across agencies.
Exact dollar amounts ($152K + $137K = $289K) and dates show real research. Management system failure citation is a valuable insight - this is why penalties escalate. Penalty escalation implication is specific and concerning. Coordination question addresses real organizational challenge. Shows cross-agency enforcement expertise.
Target skilled nursing facilities that dropped from 2-star to 1.5-star rating after a recent survey, putting them in SFF candidate territory. CMS triggers enhanced oversight at under 2.0 stars for 2 consecutive quarters.
Specific facility name and exact rating drop (2.1 to 1.5 stars) show precise research. Address (456 Oak Street) confirms you know which location. SFF threshold explanation (under 2.0 stars for 2 quarters) demonstrates expertise. December 8th survey date is recent and verifiable. Easy routing question lowers friction.
Target ASCs showing quarter-over-quarter infection rate increases above CMS penalty thresholds. Calculate exact distance above threshold (1.6 percentage points) to create urgency around quality improvement initiatives.
Exact numbers (9 infections in 219 procedures = 4.1%) show real calculation work. Quarter comparison (1.2% to 4.1%) is precise trend analysis. Distance above threshold (1.6 percentage points) creates specific urgency. QI team question is appropriate for this industry. Root cause focus is helpful framing.
Target ASCs with recent infection spikes in the past 60 days showing significant elevation above CMS penalty thresholds. Create urgency by focusing on recent timeframe and asking about common factor investigation.
Exact date range (December 1st to January 30th) and procedure count (142) show real analysis. Calculated rate (3.5%) demonstrates data work. Distance above threshold (1.0 percentage point) is precise. 60-day timeframe creates urgency. Common factor question is appropriate for infection investigation.
Target ASCs showing dramatic infection rate increases (4 infections in December vs 0.9% in Q3). Identify potential root causes (protocol changes or patient mix shifts) to demonstrate analytical thinking.
Specific month (December) and procedure count (89) show precision. Percentage calculated (4.5%) demonstrates data work. Comparison to their own Q3 baseline (0.9%) shows trend analysis. Identifying potential root causes (protocols or patient mix) adds value. Direct question to medical director is appropriate routing.
Target ASCs with significant infection rate increases in Q4 showing elevation into CMS penalty zone. Focus on the sudden spike magnitude to create urgency around quality improvement.
Exact numbers (9 infections in 219 procedures) show real calculation. Quarter comparison is precise trend analysis. Distance above threshold (1.6 points) creates specific concern. 'Penalty zone' language is clear. Root cause focus is helpful framing for quality improvement.
Target facilities showing two-quarter persistence at 1.9 stars - just 0.1 points above SFF threshold. Use CMS review timing (March for April designation) to create urgency around February survey readiness.
Two-quarter persistence (Q3 and Q4 2024) shows pattern tracking over time. Specific margin (0.1 points above threshold) creates precision and urgency. March review timing is accurate CMS process knowledge. Suggesting February survey readiness push is practical and timely. Question implies reasonable action path.
Target facilities with recent January survey showing significant drop into SFF watch territory. Use the recent survey date and next survey timing to create urgency around follow-up preparation.
Recent survey date (January 18th) is very current and verifiable. Rating drop is significant (0.5 points from 2.2 to 1.7). 'SFF watch territory' is accurate terminology for this rating level. February follow-up timing is logical based on CMS inspection patterns. Follow-up survey expectation is reasonable given the rating drop.
Target facilities showing rating decline with downward momentum heading into Q1. Use the concept of 'trajectory' to emphasize the trend is as concerning as the current rating.
Specific rating drop (2.4 to 2.0) with timeframe (September to December) shows tracking. 'Downward momentum' captures the trend concern beyond just the current rating. SFF threshold language (exactly at 2.0) is accurate. Q1 2025 timing makes it current and urgent. Simple routing question creates easy next step.
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 Dallas facility has 3 open OSHA violations from March" instead of "I see you're hiring for safety roles," 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 Hub analytics. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| CMS Skilled Nursing Facility Quality Reporting Program | facility_name, facility_id, quality_measures, overall_rating, healthcare_associated_infections, readmission_rates, staffing_ratios | Identifying SNFs with declining quality ratings approaching SFF status |
| CMS Ambulatory Surgical Center Quality Reporting Dataset | facility_name, asc_id, quality_measures, surgical_site_infections, patient_safety_indicators | Tracking ASCs with infection rate increases above CMS thresholds |
| OSHA IMIS Enforcement Data | establishment_name, establishment_id, violation_type, citation_amount, citation_date, hazard_classification | Identifying facilities with serious OSHA violations and citation patterns |
| EPA ECHO - Enforcement and Compliance History Online | facility_name, facility_id, SIC_code, violations, enforcement_actions, permit_status, inspection_dates | Finding facilities with EPA violations and concurrent OSHA enforcement |
| Hub Internal Claims Database | facility_characteristics, historical_claims_patterns, near-miss data, training records, equipment maintenance, predictive risk scores | PVP plays: Pre-claims pattern recognition, location-specific risk alerts, predictive injury forecasting |
| Hub Internal Risk Scoring Model | proprietary_risk_scores, claims_history, facility_characteristics, environmental_factors | PVP plays: Location-specific risk scoring for multi-site operators |