How do you benchmark intangible assets across portfolio companies?
Short Answer
Benchmarking compares standardised intangible asset metrics (NRR, R&D intensity, brand strength, employee retention) across portfolio companies and against industry peers to identify leaders, laggards, and investment priorities.
Full Explanation
Portfolio-level intangible asset benchmarking requires a standardised framework applied consistently across diverse businesses. The process involves: defining a common set of intangible asset categories (typically brand, customers, technology, human capital, and data), selecting 2-4 measurable KPIs per category that are applicable across sectors, collecting data quarterly from each portfolio company, and presenting normalised comparisons that account for sector differences. Effective benchmarking KPIs include: customer capital (NRR, LTV:CAC ratio, customer concentration), brand capital (NPS, organic traffic share, brand search volume), technology capital (R&D as % of revenue, release frequency, patent count), human capital (key person retention, revenue per employee, time-to-hire), and data capital (proprietary data volume, data-driven revenue %). The benchmarking output should identify: leaders (companies outperforming peers in specific intangible categories), laggards (companies underperforming, indicating investment opportunities or risks), sector patterns (which intangible categories matter most in each industry), and trends (improving or deteriorating intangible health over time). For PE firms managing 10-30 portfolio companies, this benchmarking framework transforms intangible assets from qualitative narratives into quantitative comparisons that guide capital allocation and value creation planning. Opagio's growth platform automates this benchmarking process, ingesting financial and operational data from portfolio companies and producing standardised intangible asset scorecards with peer comparisons.
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Further Reading
AI in Private Equity: Due Diligence for AI-Enabled Targets
Private equity firms are acquiring AI-enabled targets at record rates, but traditional due diligence frameworks miss critical AI-specific risks. This article provides a comprehensive AI due diligence methodology covering technical, commercial, regulatory, and talent dimensions.
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