How to Sell to Insurance Companies

The global insurtech market is projected to exceed $23B in 2026, growing at 24% CAGR. But insurance is one of the hardest verticals to sell into: 50-state regulatory fragmentation, 18-month procurement cycles at top carriers, and a buyer ecosystem split across carriers, MGAs, and brokers who each evaluate technology through completely different lenses. These 15 playbooks demonstrate how to use publicly available regulatory filings, complaint databases, and financial disclosures to build pain-qualified outbound that cuts through the noise.

12Playbooks
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Last updated: March 2026

Data Foundation

Intelligence Built on 1 Public Data Source

12 Insurance playbooks powered by freely available government databases and industry registries

Insurance is one of the most data-rich industries for GTM intelligence

The NAIC Financial Data Repository warehouses quarterly and annual statutory filings from every multi-state insurer, including Schedule P lo...

Detectable Pain Signals

Insurance generates unusually rich public signals of operational distress.

Insurance generates unusually rich public signals of operational distress. Combined ratios above 100% in specific lines (visible in statutory annual statements) indicate underwriting losses that force technology investment. Rising NAIC complaint ratios relative to premium volume signal claims handli

Public Data Sources

Insurance is one of the most data-rich industries for GTM intelligence

The NAIC Financial Data Repository warehouses quarterly and annual statutory filings from every multi-state insurer, including Schedule P loss reserve development data that reveals deteriorating loss ratios before they hit earnings calls. The SERFF (System for Electronic Rates and Forms Filing) p...

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Buyer Personas: Who Actually Signs Checks

Insurance technology buying involves distinct personas with fundamentally different priorities. The VP of Underwriting cares about risk selection accuracy, speed-to-quote, and combined ratio improvement — they respond to data showing their loss ratios diverging from peers in specific lines. The Chief Actuary evaluates reserve adequacy, pricing model sophistication, and regulatory capital requirements — they need actuarial credibility, not marketing language. The Claims Director focuses on cycle time, litigation expense, fraud detection rates, and customer satisfaction scores — NAIC complaint spikes in their book are a direct trigger. The CTO or CIO owns system modernization, API strategy, and legacy platform migration — they are activated by technology debt signals like failed SERFF filing integrations or manual processes exposed in market conduct exams. At brokerages, the decision-maker is often the agency principal or VP of Operations who feels the pain of producer license compliance across multiple states. Each of these playbooks maps specific public data signals to the persona most likely to act on them...

The Carrier vs. MGA vs. Broker Distinction

A fatal mistake in insurance GTM is treating carriers, MGAs, and brokers as interchangeable. Carriers (like Hartford, Travelers, or Liberty Mutual) are the risk-bearing entities with statutory capital requirements, state-by-state rate filing obligations, and 12-24 month procurement cycles driven by actuarial validation requirements. Managing General Agents (MGAs) have delegated underwriting authority from carriers and move faster — they can evaluate and implement technology in 3-6 months because they are built for speed and typically lack legacy infrastructure. Brokerages range from global firms like Marsh (multi-year sales cycles, committee-based decisions) to independent agencies where a single principal makes all purchasing decisions. The GTM motion is completely different for each: carriers require regulatory proof points and peer benchmarking, MGAs want speed-to-market and competitive differentiation, and brokers need workflow efficiency and compliance automation. These playbooks use entity-type-specific data sources — statutory filings for carriers, delegation agreement signals for MGAs, and NIPR license data for brokerages...

Why Generic Outbound Fails in Insurance

Insurance professionals receive hundreds of cold emails per quarter from vendors who do not understand the regulatory environment they operate in. Messaging that references 'digital transformation' or 'AI-powered solutions' without connecting to a specific regulatory filing, complaint trend, or financial metric gets deleted immediately. Insurance buyers are trained actuaries, seasoned underwriters, and compliance professionals — they evaluate claims with quantitative rigor. The playbooks in this collection show how to reference a carrier's actual Schedule P reserve development, a specific state's complaint ratio trend for their book, or a SERFF filing rejection pattern to demonstrate you understand their world. This is the difference between a 2% and a 15% reply rate in insurance outbound. The regulated nature of the industry means there is more public data available about each prospect's specific pain than in almost any other vertical — but only if you know where to find it and how to interpret it...

Browse 12 Insurance Playbooks

Showing 12 of 12 playbooks

Deep Analysis

Dent Wizard

dentwizard.com

Automotive Reconditioning & PDRInstall Base Detection

Playbook cross-references internal repair cost data by vehicle make and model with NAIC auto insurance claims data and state vehicle regi...

Playbook cross-references internal repair cost data by vehicle make and model with NAIC auto insurance claims data and state vehicle registration counts to surface model-specific PDR cost variance anomalies and proactive hail event inventory planning for rental fleets.

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HUB International

hubinternational.com

Insurance BrokerageMulti-Signal Composite

Playbook combines internal historical claims patterns

Playbook combines internal historical claims patterns, OSHA establishment data, and EPA ECHO records to deliver predictive injury forecasts and violation replication risk analysis across multi-site operator portfolios.

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Insurity

insurity.com

P&C Insurance SoftwareCustom Research

Playbook outlines the Blueprint GTM methodology for insurance software using government databases and regulatory filings

Playbook outlines the Blueprint GTM methodology for insurance software using government databases and regulatory filings to surface specific pain situations for P&C carriers.

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Deep Analysis

Origami Risk

origamirisk.com

Risk & Safety SoftwareRegulatory Triggers

Playbook targets multi-violation manufacturing facilities approaching OSHA willful classification thresholds

Playbook targets multi-violation manufacturing facilities approaching OSHA willful classification thresholds, motor carriers nearing CSA BASIC intervention scores, and SNFs on declining CMS quality trajectories toward Special Focus Facility designation.

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Paris Re

paris-re.com

ReinsuranceCustom Research

Playbook cross-references NAIC statutory state filings with public 10-Q geographic disclosures

Playbook cross-references NAIC statutory state filings with public 10-Q geographic disclosures to identify loss ratio deterioration buried in regional rollups, and models coastal ZIP code concentrations against validated hurricane loss data.

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Safe-Guard Products

safe-guardproducts.com

F&I Protection ProductsRegulatory Triggers

Playbook uses state health department food inspection records

Playbook uses state health department food inspection records to identify restaurants approaching mandatory closure on repeat pest violations, and internal seasonal sales data to warn pest control operators of equipment gaps before peak demand.

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Sedgwick

sedgwick.com

Claims ManagementMulti-Signal Composite

Playbook uses FDA warning letters and internal recall outcome data from 7,000+ managed recalls

Playbook uses FDA warning letters and internal recall outcome data from 7,000+ managed recalls to build pre-completed customer notification trees, and uses MSHA mine safety data to surface multi-facility POV threshold risk.

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Sixfold

sixfold.ai

AI for Insurance UnderwritersMulti-Signal Composite

Playbook uses internal pharmaceutical cold chain carrier performance data combined with FDA inspection records

Playbook uses internal pharmaceutical cold chain carrier performance data combined with FDA inspection records to deliver ready-to-submit audit documentation, and forecasts automotive shipment exceptions from historical exception patterns.

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Deep Analysis

Vantage Risk

vantagerisk.com

Specialty InsuranceRegulatory Triggers

Identifies oil & gas facilities with unresolved EPA violations and expiring Title V air permits

The playbook identifies oil & gas facilities with unresolved EPA violations and expiring Title V air permits, and demolition contractors with repeat OSHA serious violations approaching willful reclassification deadlines, targeting risks that create uninsurable exposures.

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Deep Analysis

Vertafore

vertafore.com

Insurance Software & DistributionMulti-Signal Composite

Combines internal policy renewal calendar data with state insurance department rate filing records and DOI complaint records

The playbook combines internal policy renewal calendar data with state insurance department rate filing records and DOI complaint records to identify carrier rate increase collisions with renewal windows and trace integration failures to specific customer complaints.

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Deep Analysis

Warrantech

warrantech.com

Vehicle Extended Warranty & Protection PlansInstall Base Detection

Cross-references dealer inventory VINs against NHTSA recall databases and internal claims frequency data

The playbook cross-references dealer inventory VINs against NHTSA recall databases and internal claims frequency data to identify open powertrain recalls that void warranty coverage and vehicle models with significantly higher-than-average transmission claim rates.

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Zywave

zywave.com

Insurance TechnologyRegulatory Triggers

Playbook uses NIPR producer license records and job posting velocity

Playbook uses NIPR producer license records and job posting velocity to identify multi-state insurance agencies facing simultaneous license renewal pressure and hiring surges that create compliance coordination risk.

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Frequently Asked Questions

A GTM playbook is a company-specific sales intelligence brief built from public data analysis. Each of the 12 Insurance playbooks identifies buyer personas, detectable pain signals, and messaging strategies tailored to that company's market position and regulatory environment.

Blueprint GTM uses freely available government databases, regulatory filings, licensing records, and industry-specific registries to identify companies in provable pain situations. The specific sources vary by subcategory — the intelligence sections above detail the most valuable databases for Insurance sales.

Generic research tells you the market size and buyer titles. These playbooks tell you which specific public data signal indicates a company is about to buy, what language their buyers use to describe their pain, and how to construct a message they would actually respond to.

Yes. Blueprint GTM builds custom playbooks for $50 each at playbooks.blueprintgtm.com. You provide your company domain and the system delivers a complete GTM intelligence brief with buyer personas, pain signals, and messaging.

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