Startup Failure & Postmortem Data
Buy and sell startup failure & postmortem data data. Failure reasons, runway consumed, and pivot attempts — the startup mortality data.
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Find Me This Data →Overview
What Is Startup Failure & Postmortem Data?
Startup failure and postmortem data captures the detailed circumstances surrounding company shutdowns, including failure reasons, runway consumption, funding history, and pivot attempts. This dataset documents the patterns and causes behind startup mortality, providing critical insights into why companies fail despite raising capital. Buyers use this data to understand market dynamics, identify risk factors, and learn from documented failures across industries and funding stages. The data is compiled from founder interviews, public shutdown announcements, regulatory filings, and proprietary analysis of company health metrics, covering hundreds of documented cases with billions in aggregate funding lost.
Market Data
431 companies
VC-Backed Startups Analyzed (Post-2023)
Source: CB Insights
70% of closures
Top Failure Reason: Ran Out of Capital
Source: CB Insights
43% of analyzed startups
Poor Product-Market Fit Failures
Source: CB Insights
$17.5B across 431 companies
Combined Equity Raised Before Failure
Source: CB Insights
22 months
Median Time from Last Fundraise to Death
Source: CB Insights
48.4%
U.S. Private Business Failure Rate (5-Year)
Source: U.S. Bureau of Labor Statistics
Who Uses This Data
What AI models do with it.do with it.
Venture Capital Firms & Investors
Identify warning signals and deterioration patterns in portfolio companies. Use Mosaic score declines, partnership fall-offs, and runway metrics to predict and manage downside risk before capital is deployed further.
Startup Founders & Leadership
Learn from documented failure case studies across industries and funding stages. Understand common pitfalls like product-market fit failures, bad timing, and unsustainable unit economics to avoid repeating historical mistakes.
Market Researchers & Business Intelligence Teams
Analyze failure rates by industry, location, and funding stage. Track macroeconomic impacts on startup survival, including funding cliff patterns and the 'Valley of Death' between Seed and Series A rounds.
Corporate Strategy & M&A Teams
Benchmark acquisition targets and market timing decisions. Evaluate which industries and cohorts carry higher mortality risk and which historical failures present learning opportunities for internal innovation initiatives.
What Can You Earn?
What it's worth.worth.
Individual Case Studies & Post-Mortems
Varies
Per-document pricing depends on depth of analysis, funding data, and founder interview content included.
Bulk Dataset Licenses (100+ Cases)
Varies
Pricing scales with cohort size, time period covered, and access to proprietary metrics like Mosaic scores and partnership tracking data.
Real-Time Failure Tracking & Alerts
Varies
Subscription models for ongoing failure data updates, quarterly trend reports, and industry-specific shutdown monitoring.
Industry & Geographic Failure Reports
Varies
Segmented reports analyzing failure rates by sector, funding stage, and country. Pricing reflects custom filtering and comparative benchmarking.
What Buyers Expect
What makes it valuable.valuable.
Documented Failure Reasons with Multiple Sources
Data must cite founder interviews, shutdown announcements, and public filings. Startups typically fail for multiple, interconnected reasons—buyers expect granular tagging (capital depletion, product-market fit, timing, unit economics, etc.) with supporting evidence.
Complete Funding & Runway History
Total capital raised, round-by-round breakdown, time between raises, and months of runway remaining at shutdown. Median raise size ($11M) and average ($48M) vary widely, so detailed histories are essential for benchmarking.
Operational & Health Metrics Pre-Shutdown
Headcount trends (two-thirds were shrinking 6 months before death), partnership activity timelines, and proprietary health scores showing deterioration patterns. These early warning signals differentiate high-quality datasets from narrative-only postmortems.
Industry, Stage, and Geographic Classification
Failure rates vary dramatically by sector (Food & Restaurant vs. AI/ML), funding stage (Valley of Death between Seed and Series A), and country (21.5% first-year failure in U.S. vs. 90% in Canada). Proper categorization is critical for segmented analysis.
Timeline & Recency
Post-2023 data preferred for current macroeconomic relevance. Quarterly updates tracking funding contractions (68% below 2021 peak in Q1'24) and evolving failure patterns show data currency and freshness.
Companies Active Here
Who's buying.buying.
Portfolio risk management, due diligence on new investments, and comparative analysis of company health trajectories. Use postmortem data to identify red flags and benchmark against failed peers.
License bulk failure datasets to publish industry reports, failure rate benchmarks, and trend analyses. Integrate data into subscription products for founders and investors.
Train portfolio companies on common failure patterns and survival strategies. Customize case study curricula for specific industries and funding stages.
Evaluate market timing, competitive landscape shifts, and acquisition opportunities. Study failed competitors to identify gaps or pivots their own internal ventures should pursue.
Incorporate failure pattern data into risk models and early-warning systems for private company lending and venture debt.
FAQ
Common questions.questions.
What's the most common reason startups fail?
Running out of capital ranks first at 70%, but it's typically the final cause of death rather than the root problem. The more telling underlying reasons are poor product-market fit (43%), bad timing (29%), and unsustainable unit economics (19%). Most startups cite multiple failure reasons.
How much runway do failed startups typically have?
The median failed startup raised $11M (average $48M) and survived 22 months from their last fundraise to shutdown. Nearly a quarter of startups were 'walking dead' for over 3 years since their final raise. Funding is often not the root problem—it's the symptom of deeper issues like weak product-market fit or misaligned unit economics.
Are there early warning signals before a startup fails?
Yes. CB Insights tracks several predictive indicators: Mosaic health scores (proprietary 0-1000 scale) declined 15% on average in the year before death across 72% of tracked companies. Partnership activity also drops significantly, declining 44% in the final 12 months compared to the prior year. Headcount shrinkage 6 months before shutdown is common—two-thirds of failed startups were shrinking at that point.
Do failure rates vary by industry and funding stage?
Yes, significantly. The 'Valley of Death' between Seed and Series A shows the sharpest drop in survival rates. First-year failure rates in the U.S. are 21.5%, rising to 48.4% by year five and 65.1% by year ten. Failure rates by industry range from food & restaurant (highest) to software and AI/ML (lower). International variation is also substantial—Canada reports ~90% failure rates compared to ~48% in the U.S. over five years.
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