Academic Study on Startup Performance
Data Collected from 2015-2018; Survey Administered in Q2 2000; Study Published Q4 2020
Build Survey and survey methodology, summarize key findings, highlight methodological approach, and outline practical implications for both entrepreneurs and investors.
This study explores why some entrepreneurs succeed while others fail, examining survey data from 470 early-stage US-based startups. Each startup raised $500K-$3M in seed funding between 2015 and 2018. The paper employs multivariate regression to evaluate relationships between outcome valuations and factors such as founder attributes, lean startup practices, HR choices, and financial metrics.
Despite significant investment in early-stage startups, predicting which ventures will succeed remains challenging. This research addresses a critical question: Which factors truly determine startup performance?
While traditional venture capital relies heavily on intuition and pattern-matching, this research sought to establish empirical evidence for factors that truly predict startup success or failure.
The study examines whether methodology (lean startup practices), team construction, market opportunity, or founder attributes better predict startup valuation outcomes.
Exploring fundamental questions about startup performance determinants
Do founder characteristics predict success?
Does "pivoting" really improve outcomes?
How do market conditions affect success?
Which financial indicators matter most?
Which factors actually drive early-stage startup performance, and how much of success is predictable versus due to random variation?
This question challenges conventional wisdom about what makes startups succeed, offering empirical evidence to guide better investment decisions and founder strategies.
A rigorous empirical approach uncovered patterns in startup success factors
CEO survey of 470 startups
Collected data from US-based startups that raised $500K-$3M in first seed round between 2015-2018. Each founder provided detailed operational information.
Comprehensive data on management practices, founder backgrounds, financing details, and product positioning
Regression & correlation analysis
Used multinomial logistic regression to test which factors predict high, medium, or low valuation outcomes. Correlation analyses revealed strength of relationships.
Model achieved 69% classification accuracy with Cox & Snell pseudo R² = 0.356
Valuation-based success metrics
Performance measured as changes in seed equity value by end of 2019, classified into three tiers to enable statistical analysis.
Time-series data collection
Tracked 214 startups over 24 months with quarterly data collection points, enabling analysis of how strategy changes affect performance trajectories over time.
Applied growth curve modeling to identify inflection points and critical periods in startup development
From raw startup data to academic insights: our systematic research approach
Research Design
Survey Development
Data Collection
Statistical Analysis
Academic Publication
Formulated the key research questions and designed methodology to test the relative impact of founder attributes vs. market factors.
Created comprehensive CEO survey to gather detailed data on 470 startups that raised initial seed funding between 2015-2018.
Distributed surveys to CEOs and gathered extensive financial data, operational metrics, and team information from participating startups.
Applied multinomial regression and correlation analyses to identify statistically significant factors impacting startup performance.
Published findings in Harvard Business School Working Paper Series (Paper 21-057) with peer review and academic validation.
Statistical analysis of 200+ startups revealed these empirically validated determinants of success
Statistical analysis revealed strong significance for confidence in financial metrics. Confidence in TAM (p=0.020), LTV/CAC (p=0.027), and path to profits (p=0.002) were all highly significant predictors of startup success.
Data showed that too few pivots significantly impacts valuation (p=0.005). Startups need strategic adaptability, with those making appropriate pivots showing higher valuations compared to those that pivoted too little.
The analysis showed statistical significance for recruiting overemphasis on skills (p=0.021) and having fired a head of sales (p=0.006). Team management decisions directly impact valuation outcomes.
The expanded model found that risk aversion in founders was statistically significant (p=0.035), with a coefficient of 0.366. Traditional credentials like prestigious education showed minimal impact on success.
How our statistical analysis of startup performance influenced investment strategies and business education
Confidence in path to profits strongest predictor (p=0.002)
Strategic pivoting significantly impacts success (p=0.005)
Risk aversion trait statistically significant (p=0.035)
Created new venture capital evaluation framework
Integrated findings into HBS entrepreneurship curriculum
Developed startup financial metrics assessment tool
Published in Harvard Business School Working Paper Series
Study methodology adopted by other institutions
Presented at major entrepreneurship conferences
Capital raised vs. goal highly significant (p=0.004) with coefficient of -1.087
Likelihood ratio test significance of 0.027 in expanded model vs. 0.241 in reduced model
Cox and Snell Pseudo R² of 0.178 in expanded model, showing better fit
Investors prioritizing startups with clear path to profitability
Accelerators developing strategic pivot frameworks for portfolio companies
Startups using research for more effective fundraising strategies
Interactive data visualizations from the Harvard Business School startup performance study
Statistical comparison across valuation categories
Statistical significance of key predictors
Comparing predictive power of models
Impact of opportunity vs execution quality
Our analysis of 200+ startups revealed these critical factors that significantly impact success (p<0.05):
Path to Profits Confidence(p=0.002) was the strongest predictor of success
Too Few Pivots(p=0.005) significantly reduced valuation outcomes
Capital Raised vs Goal(p=0.004) strongly correlated with success
CEO Risk Aversion(p=0.035) was the only personality trait with significance
Paper 21-057: "Determinants of Early-Stage Startup Performance"
This study explores determinants of new venture performance by surveying CEOs of 470 early-stage startups.
View on HBS Website