Q3 2025 Startup Benchmarking Report: AI’s Split Growth Story and Fintech’s Steady Comeback

TLDR from Q3 2025

• AI outpaced fintech and SaaS in revenue growth in Q3. However, growth slowed last quarter for later stage AI companies.
• Fintech continued to outpace SaaS in revenue growth, though the category has yet to return to earlier highs.

Call it a tale of two industries. In our Q3 2025 Startup Benchmarking Report, we dive into the most interesting startup performance trends in fintech and AI. This report is built from our Global Benchmarking data, an anonymized set of financial metrics from over 10,000 venture-backed startups on Standard Metrics.

In our Q1 2025 Startup Benchmarking Report we saw evidence that the anecdotal “fintech is back” mantra going around the startup world was true, with mid-sized, upper-quartile companies in the industry surpassing SaaS in revenue growth and EBIDTA. In our Q2 2025 Startup Benchmarking Report, we saw fintech continue to outperform SaaS in revenue growth and even surpass AI in parts of 2024.

In Q3, fintech continued to improve. While early stage AI (companies between $1-20M in annualized revenue) continued to drive outsized growth, later stage AI (companies with $20M+ in annualized revenue) saw growth slow in Q3 2025. With that said, AI companies are still outperforming SaaS and Fintech, which we demonstrate in a new cohort analysis.

How are the best fintech companies doing?

Fintech companies of all sizes improved their quarterly revenue growth in Q3 2025, with early, mid, and late stage fintech companies up 7, 4, and 5 percentage points quarter-over-quarter and 7, 5, and 5 percentage points year-over-year. Fintech companies outpaced the “all sector” benchmark for revenue growth across all revenue bands. However, the category has moderated in growth since 2021 highs.

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Fintech companies also continued to display strong gross margins. Early-stage fintech reached 91% in Q3 2025. Mid-stage fintech rebounded to 2021 gross margin highs of 85% in Q3 2025. Late-stage fintech has significantly improved gross margins since 2021 and 2022 lows and reached 86% in Q3 2025. Moreover, fintech companies of all stages slightly outpaced their “all sector” peers for gross margin in Q3 2025.

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How are the best AI companies doing?

So what does performance look like for AI companies? (Note: We have a more robust set of data for fintech than AI, so we had to create less granular revenue buckets for the AI sector.)

At both the $1-20M and $20M+ revenue ranges, the best AI companies outpaced the “all sector” average. Earlier-stage, upper-quartile AI growers saw improvements in quarterly revenue growth — up 7 percentage points between Q2 and Q3 — and were growing significantly faster than their all-sector peers.

However, late-stage, upper quartile AI companies have declined in quarterly revenue growth since their Q1 2025 high of 44% to 21% in Q3 2025.

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As we did in our Q2 2025 Startup Benchmarking Report, we compared AI to SaaS (instead of all sectors) for our gross margin analysis. Like last quarter, AI companies outperformed SaaS in Q3 2025 in the $1-20M category.

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However, later stage AI continued to operate at a lower gross margin (at 77% in Q3 2025 vs. a SaaS gross margin of 80%). Later-stage companies in both sectors reported lower gross margins than their early stage counterparts. However, we hypothesize that the difference here may be largely driven by reporting standards becoming more rigorous as companies mature.

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In our Q2 2025 Startup Benchmarking Report, we saw AI companies in the upper quartile of quarterly revenue per FTE efficiency lag in the metric compared to all sector peers (who’ve generally trended upwards in efficiency since 2022 lows). But after two quarters of significant efficiency gains, late-stage AI has reached $133k quarterly revenue per FTE and surpassed the all sector benchmark.

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Early-stage AI, however, still trails behind the all sector average in Q3 2025.

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How are the best AI, fintech, and SaaS companies performing over time?

In this new cohort analysis, we examined companies across three sectors — AI, SaaS (excluding those with AI overlap), and fintech — that reported between $1 and $500,000 in annualized revenue as of Q1 2024. We established fixed cohorts based on these criteria and tracked their performance over time, aggregating results within each group, each quarter. While some companies naturally dropped out due to acquisitions or shutdowns, no new companies were added after the initial cohort was defined.

Tracking the upper quartile of each cohort in terms of quarterly revenue, we see the fintech and SaaS cohorts perform quite similarly each quarter. However, the AI cohort accelerated, and the upper quartile AI company grew to $421k in quarterly revenue by Q3 2025, compared to Fintech’s $263k and SaaS’s $247k.

This analysis directionally matches the observation we made in our Q2 2025 Startup Benchmarking Report looking at relative growth rates between the three sectors and noting that the best AI startups are able to reach $100M in revenue more quickly than fintech or SaaS companies.

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We also see this trend at the 90th percentile. As of Q3 2025, the 90th percentile AI company reached $922k in quarterly revenue compared to Fintech’s $644k and SaaS’s $514k.

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A struggle at the beginning across industries

While we found the AI and fintech industries particularly interesting this quarter, Standard Metrics’ data spans the entire private market, allowing us to explore trends in aggregate across industries.

One we found interesting: for companies at the $0-1M revenue ranges across all sectors, we saw flat revenue (no growth) for the second quarter in a row. For a cohort that saw the highest median quarterly revenue growth across all revenue buckets in Q2 and Q3 2021, this marks a dark spot in what already is a long-term downward trend (a trend experienced by a significant n-count of over 1,000 startups of this size on platform).

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Conclusion

In Q3 2025, fintech continued its steady climb, with companies across all revenue bands improving in both quarterly revenue growth and gross margins, as well as outperforming the broader market.

AI, meanwhile, remained one of the strongest categories in the private markets ecosystem, but its story is beginning to bifurcate: early-stage companies still generated outsized growth, while later-stage companies saw momentum cool for the first time in several quarters. Even with this deceleration, AI continued to outpace SaaS and fintech over longer time horizons, particularly among top performers.

At the same time, early-stage companies across all sectors faced a tough environment this quarter, with the median startup in the $0-1M revenue range posting flat revenue with no growth.

As we head into next quarter, we’ll be curious whether fintech’s momentum holds, how AI’s late-stage companies evolve, and whether the early-stage segment begins to rebound.

Methodology

This report summarizes data from over 10,000 anonymized startups who report data to their investors on Standard Metrics. Companies are included in an annualized revenue bucket based on their annualized revenue value calculated in each respective quarterly period. Companies self-identify their sector at time of onboarding onto our platform and can update their sector at any time afterwards. Companies are in control of their data on Standard Metrics and can opt out of their data being included in anonymous benchmarking anytime. The underlying composition of company data on Standard Metrics can shift over time as new companies join or leave (e.g. IPO, M&A, shut down) our platform.

For graphs that represent multiple revenue buckets of companies [e.g. “Median Quarterly Revenue Growth (All Companies)” chart above], our N count is the minimum number of companies across the smallest cohort in the most recent period (e.g. “Q3 2025”). For graphs analyzing a single revenue bucket on one metric, N is the number of companies in the most recent period in the chart. Data points must have at least 30 companies to be shown.

We are excited to release our Q2 2025 startup benchmarking report, highlighting the most interesting stories from the quarter in the private markets. The reports are built from our Global Benchmarking data, an anonymized set of financial metrics from over 9,000 venture-backed startups on Standard Metrics.

In Q2 2025, we focus in on AI startups, calling early stage AI startups with $1-20M in annualized revenue at the quarter end Rocks and AI startups with $20M+ in annualized revenue at the quarter end Boulders.

Some highlights found in the report:

• Upper quartile AI Rocks have outpaced all sectors by roughly 10 to 30 percentage points in quarterly revenue growth since late 2023, but lag in revenue per FTE efficiency.
• Upper quartile AI Boulders consistently outpace upper quartile companies across all sectors in quarterly revenue growth (around 10 to 20 percentage points higher each quarter). In Q2 2025, they pair that growth with remarkable progress in revenue per FTE efficiency.
• Upper quartile AI Boulders outpace fintech and SaaS in revenue growth in 2025, while top-quartile fintech wins out in profitability and low burn.

Check out the data, the details, and more of our findings below.

AI Rocks ($1-20M in annualized revenue): A push towards operational efficiency

Note: This section was refreshed on 12/4/25 following a review of the underlying calculations.

In terms of quarterly revenue growth, upper quartile AI Rocks (AI startups with $1-20M in annualized revenue) have outpaced the all sector average since Q4 2023. While revenue growth has moderated from the extraordinary peaks of late 2024, these AI companies are still growing far faster than all sectors (an 11 percentage point difference in Q2 2025), underscoring ongoing strong demand.

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Upper quartile AI Rocks also maintain relatively high gross margins (84-91%). In this analysis we focused on AI vs. SaaS (instead of all sectors) as we wanted more comparable one-to-one analysis in the software space. In the past, early stage, upper quartile AI and SaaS companies followed relatively similar patterns, peaking and falling at similar times, but with the AI cohort .5 to 5 percentage points higher than SaaS. In Q2 2025 (as we saw in Q2 2024), however, AI had slightly lower gross margins than SaaS.

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However, upper quartile AI Rocks lag in revenue per FTE efficiency. All sectors, meanwhile, steadily improved efficiency, reflecting a trend (that we discussed in our previous benchmarking report) of scaling operations with controlled headcount growth.

This perhaps highlights that upper quartile, early stage AI companies have been hiring aggressively, potentially ahead of revenue, as they race to capture market share during the growth surge.

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With moderately high margins, relatively strong growth, and increasing operational discipline, early-stage, upper quartile AI remains one of the most attractive sectors for long-term investment, poised to produce outsized returns.

AI Boulders ($20M+ in annual revenue): The best keep winning

Top quartile AI Boulders (AI startups with $20M+ in annualized revenue) largely outpaced all-sector peers in quarterly revenue growth, spiking at 41% in Q1 2025 compared to the broader market’s 17%. While revenue growth has converged closer to all sectors the last quarter, AI Boulders still greatly outpace in growth: in Q2 we still saw a 10 percentage point difference.

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Top quartile AI Boulders also closed the revenue per FTE efficiency gap with their all-sector counterparts. Quarterly revenue per full time employee (FTE) in Q2 2025 reached $124K, a YoY improvement of 39%. AI Boulders are proving that they are able to reach workforce productivity while scaling.

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Gross margin trends reveal some cracks in AI Boulders (though certainly not a true fault line). As we did with AI Rocks, we will also be comparing AI Boulders to SaaS in this gross margin analysis for better one-to-one comparison. Top-quartile AI Boulders saw margins above SaaS peers in parts of 2023 and 2024, but those levels slightly eroded into 2025.

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At $20M+ in revenue, top quartile AI Boulders are demonstrating that they can achieve revenue per FTE efficiency, outsized growth, and average margins.

So how does AI stack up to fintech and SaaS?

Note: This section was refreshed on 12/4/25 following a review of the underlying calculations.

Last quarter, we looked at how fintech and SaaS compared to one another. Here we bring AI into the mix.

At the $20M+ annualized revenue band, upper quartile AI companies are growing significantly faster than peers in SaaS and fintech in 2025. While there have been periods in the past where fintech startups have surpassed AI companies’ quarterly revenue growth, SaaS companies have lagged consistently behind since early 2023.

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At the $100M+ annualized revenue band, fintech companies have once again consistently outpaced SaaS companies in revenue growth. We do not have adequate data on AI companies with $100M+ in annualized revenue to present results here.

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Due to these outsized growth rates, the average upper quartile AI startup is able to reach $100M in revenue much more quickly than fintech and SaaS companies.

The chart below showcases how quickly a hypothetical upper quartile grower in each sector scales from $1M to $100M. This chart uses Standard Metrics’ latest benchmarked revenue growth rates from upper quartile companies to model growth starting from $1M in revenue. Growth compounds each quarter, with rates granularly adjusted by revenue range: $1–5M, $5–20M, and $20–100M.

As further guidance for interpreting the chart below: the upper growth quartile of AI companies at $1M in annualized revenue that stay in the upper quartile (which is a significantly smaller population than start in that quartile) will make it to $100M in 3.5 years.

Upper quartile AI companies look largely like their SaaS counterparts in terms of their EBITDA profile outside of temporary spikes in Q4 2023 and Q1 2025. Fintech companies are consistently more profitable.

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When it comes to burn, fintech is also the clear leader.

Upper quartile, $20M+ annualized revenue fintech companies reached zero burn in early 2023 and have continued to generate more cash and outperform AI and SaaS since then. Upper quartile, $20M+ AI and SaaS companies have also seen improvements in burn, reaching zero in late 2024. However, they trail fintech.

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The late-stage AI sector has moved from hyper-growth to sustainable, efficient scaling over the past two years, making it a true competitor for the more mature fintech and SaaS industries. Late stage AI companies are growing rapidly, and now doing so with slightly lower burn and improved EBITDA. SaaS sees similar EBITDA and burn, and much lower growth than AI. Fintech drives stronger EBITDA performance and burns significantly less than AI, however can’t grow quite as quickly.

And what about IPO readiness for all companies?

In Q4 2024, we discussed how late-stage startups were building towards IPO and M&A readiness with a focus on better EBITDA and reduced burn. In Q1 2025, we saw stability in these metrics (rather than further growth) and, in Q2 2025, we see this stability continue. This points to a long-term new normal of efficiency for median mid- and late-stage startups. However, the startups that are most likely to IPO may be those who can beat peers in revenue growth (while maintaining efficiency).

While median companies in the $5-20M and $20-100M annualized revenue ranges are burning slightly more than last quarter, $100M+ companies are burning slightly less (once again inching closer to zero burn).

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In median EBIDTA, too, we see relative consistency for mid-stage and late-stage companies, with the previous year’s upward trend flattening into a steady state for each revenue band.

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In workforce productivity we also see a consistent ability for companies to do more with less. Median quarterly revenue per FTE ticked up for mid-stage companies and stayed flat for late-stage companies.

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The median mid-stage and late-stage company reached a steady state of efficiency in Q2 2025. After a bump in efficiency in Q4 2024, we see companies holding on to their gains quarter-after-quarter despite some analysts pointing to macroeconomic headwinds.

However, this efficiency comes in a time of lowered revenue growth across industries. Growth has been relatively flat since 2023, significantly below 2021 peaks.

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Even at the upper quartile, we see a decline and now flattening in the curve of quarterly revenue growth for mid- and late-stage companies.

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While the median mid- and late-stage startup is showing efficiency, the startups that pair that efficiency with outsized growth will emerge the victors in the race to going public.

Methodology

This report summarizes data from over 9,000 anonymized startups who report data to their investors on Standard Metrics. Companies are included in an annualized revenue bucket based on their annualized revenue value calculated in each respective quarterly period. Companies self-identify their sector at time of onboarding onto our platform and can update their sector at any time afterwards. Companies are in control of their data on Standard Metrics and can opt out of their data being included in anonymous benchmarking anytime. The underlying composition of company data on Standard Metrics can shift over time as new companies join or leave (e.g. IPO, M&A, shut down) our platform.

For graphs that represent multiple revenue buckets of companies [e.g. “Median Net Burn (All Companies)” chart above], our N count is the minimum number of companies across the smallest cohort in the most recent period (e.g. “Q2 2025”). For graphs analyzing a single revenue bucket on one metric, N is the number of companies in the most recent period in the chart. Data points must have at least 30 companies to be shown.

We’re excited to announce our latest Standard Metrics startup benchmarking report, covering the most interesting stories that we saw in the private markets in Q1 2025.

Q1 2025 was a quarter with huge headlines: a presidential inauguration, shifting trade policies, and continued improvements to and growth in AI.

The latest Global Benchmarking data from Standard Metrics — the portfolio data management platform for over 100 VC firms and 9,000 startups — highlights how startups performed amid these changes. We dive into Q1 revenue growth trends, explore how the SaaS vs. fintech industries are faring under an AI explosion, and look back to see if our Q4 2024 narrative of startups gearing up for IPOs still is accurate a quarter later.

We hope these insights provide helpful context into the previously-opaque private markets with real, anonymized data from over 9,000 startups across industries, sizes, and geographies. Whether you’re preparing for a portfolio review, heading into a board meeting, or putting finishing touches on a forward-looking financial plan, we want to give you the fresh benchmarks you need.

A consistently tough first quarter

Standard Metrics’ Global Benchmarking data highlighted a significant drop in quarterly revenue growth across companies of all sizes in Q1 this year — with median quarterly revenue growth dropping between 3 and 5 percentage points quarter-over-quarter. Interestingly, this isn’t just a point-in-time trend: since 2021, we’ve seen companies consistently increase in quarterly revenue growth in Q4, then decline in quarterly revenue growth in Q1.

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This is even true for top performing companies: upper quartile revenue growth dropped by 8 to 10 percentage points quarter-over-quarter in Q1 2025.

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We were curious if this trend was being influenced by industry biases. Consumer startups, for example, have an almost unavoidable December holiday boost and January post-holiday slump. While consumer startups were most affected by this trend, we saw the same results in other industries like fintech, SaaS, and healthcare.

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We were also curious if this performance decrease was affected by macroeconomic headwinds. However, given the cyclical nature of this trend, Standard Metrics didn’t necessarily see tariffs wildly affecting startups in Q1 2025, though future effects remain to be seen. (While President Trump began to research tariffs in February, more active implementation began in April, after the quarter closed.)

Instead, some combination of holiday sales, lower household discretionary income, and startups pushing big deals to close before the holidays may have all been more likely explanations for this consistent Q1 drop.

A tale of two industries: how SaaS and fintech are faring in 2025

Looking at our benchmarking data over time, we’ve seen that fintech companies have slowly regained their footing, while SaaS companies stumble across various performance metrics.

As we covered in more depth above, everyone had a tough first quarter this year in revenue growth. So while midsize fintech (with $20 to 100 million in annualized revenue) did see a large drop in median quarterly revenue growth in Q1 2025, the cohort has consistently been outpacing SaaS in the metric since Q3 2022, hovering between ~7% to ~14% quarterly revenue growth.(1) Over the past three quarters, the cohort has also consistently surpassed hardware, health, and consumer in median quarterly revenue growth.

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The best growth stage SaaS and Fintech companies, however, are showing that profitability is possible. It took 3 years for this cohort to pivot from the free money era of 2021 to now (and top mid-size performers in fintech have outpaced those in SaaS most quarters), but EBITDA for upper quartile companies in both industries looks very impressive where we stand in Q1 2025.

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However, a gap in EBITDA performance between the two industries becomes striking at the upper quartile of late-stage companies. While upper quartile fintech companies with $100M+ in revenue have remained profitable since Q1 2022, they reached a new high at $14.2 million this quarter. Upper quartile SaaS companies at this revenue band, meanwhile dropped from $2.8 million in Q4 2024 to $0.4 million in Q1 2025.

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Standard Metrics’ data aligns with many sources spelling out trouble for traditional SaaS. Jamin Ball, a partner at Altimeter and the author of the popular Substack Clouded Judgment reported a 30% decline YoY in Q1 2025 in aggregate net new ARR in cloud software, for example. Factors driving this performance likely include market saturation, economic uncertainty, and budget shifts towards AI.

Standard Metrics data also corresponds to positive industry chatter that fintech is back. While fintech faces the same problems of market saturation and economic uncertainty as SaaS, many fintech companies seem to be using AI to their benefit — integrating it into their products — rather than letting AI competitors steal their consumers’ dollars. A prime example of this trend? Mobile banking app Chime saw shares increase as much as 59% above their initial offering price in their IPO last week.

Checking in: how IPO-readiness looks in the first quarter

In our report last quarter, our narrative focused on how mid-stage and late-stage startups seemed to be gearing up for IPOs or M&As by focusing on EBITDA multiples, growing revenue, and reducing burn. This quarter, we’ve seen startups hold on to these gains, though continued momentum has slowed amid a tough first quarter.

After a significant reduction in median quarterly net burn across companies of different sizes in Q4 2024, performance decreased slightly in Q1 2025, with companies across revenue ranges burning more than the quarter prior.(2) However, all revenue band cohorts maintained an improved performance compared to Q3 2024.

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These same startups fared better in EBITDA this quarter. Companies with $5 to $20 million in annualized revenue saw a slight uptick in the metric, while companies in range of $20 to $100 million in annualized revenue saw a slight downtick. Companies with $100 million or more saw a steeper decline in EBITDA, but the decline comes after a huge performance boost in the metric in Q4 2024.

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Last quarter we also saw improvement in workforce productivity, highlighting companies’ abilities to do more with less in tightened economic conditions. Amid Q1 2025 struggles, this efficiency declined slightly.

Startups with more than $100 million in annual revenue declined in quarterly net burn per FTE (Full-Time Employee), though continued to improve in median revenue per FTE in Q1 2025. Midsize startups struggled a bit more — with middling or declining performance in both metrics in Q1 2025.

But — as we saw in burn and EBITDA — startups as a whole were still much better positioned for IPO than they were just a year prior.

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The relatively consistent performance in a tough quarter shows that mid and late stage private startups are still attempting to project an image of public-market-readiness. However, while companies have continued to make a strong case for IPOs or acquisitions this quarter, they may hold off on going public amid macroeconomic concerns like tariffs.

Want to dive deeper?

We had a blast diving into how — and why — Q1 can be difficult for startups, how SaaS and fintech are faring in 2025, and how mid- and late-stage companies are continuing to show strength for IPOs and M&As. But our Global Benchmarking product can do so much more, helping you compare your portfolio against 9,000+ startups, segmented by revenue, size, and sector. Have questions? Reach out via the form below.

Thanks for reading, and please reach out if there are any particular topics you’d like for us to cover in the future!

Methodology

This report summarizes data from over 9,000 anonymized startups who report data to their investors on Standard Metrics. Companies are included in an annualized revenue bucket based on their annualized revenue value calculated in each respective quarterly period. Companies self-identify their sector at time of onboarding onto our platform and can update their sector at any time afterwards. Companies are in control of their data on Standard Metrics and can opt out of their data being included in anonymous benchmarking anytime.

For graphs that represent multiple revenue buckets of companies [e.g. “Median Net Burn (All Companies)” chart above], our N count is the minimum number of companies across the smallest cohort in the most recent period (e.g. “Q4 2024”). For graphs analyzing a single revenue bucket on one metric (e.g. “Revenue Growth Trends Across Late Stage Startups” chart above), N is the number of companies in the most recent period in the chart. Data points must have at least 30 companies to be shown.

Footnotes

(1) Benchmarking data is inherently subject to cohort effects, particularly when comparing different time periods. Shifts in the composition of participating companies—such as the onboarding of higher-performing firms or changes in sector representation—may influence aggregate trends. While these benchmarks offer valuable directional insights, they should be interpreted with the understanding that cohort dynamics can impact quarter-over-quarter comparisons.

(2) Net Burn is equal to Cash Flow from Operating Activities on the Cash Flow Statement. Decreases in Cash are marked as negative values.

The last few years have been a whirlwind for venture-backed startups. In 2021, the market was awash with capital, fueling unchecked spending, sky-high valuations, and aggressive expansion. Growth-at-all-costs was the mantra. But in 2022, the music stopped. Market conditions shifted amid inflation, rising interest rates, and recession fears; capital dried up, and startups found themselves in survival mode.

Now, the latest Global Benchmarking data from Standard Metrics—the portfolio data management platform for over 100 VC firms and 8,000 startups—reveals a fundamental transformation for late-stage startups leading up to Q4 2024.¹ Late-stage startups ($100M+ annualized revenue) aren’t just waiting for private capital to return to previous levels—they’re taking matters into their own hands, recalibrating their strategies, and reshaping their financial plans for a new market reality. 

While some reports highlight a handful of companies readying to IPO in 2025, our data tells a broader story: Late-stage startups across the board are getting financially fit for what’s next. But amid a volatile market, what are companies’ next moves? Are late-stage companies gearing up for IPOs in a volatile equity market? Preparing for long-awaited M&A after years of high private valuations and regulation constraints? Or is this just the inevitable shakeout, in which only the strongest businesses emerge with sustainable financials?

Regardless, across key financial metrics—net burn, EBITDA, revenue, headcount, operating expenses, gross margin, and more—the shift in financial strategy is unmistakable.

The era of efficiency

Over the last three years, late-stage startups have undergone a dramatic financial transformation toward efficiency. Net burn, EBITDA, net burn per FTE, and revenue per FTE provide the clearest early signals of this shift.

From spending sprees to sustainable burn

In 2021, companies were burning through cash at unprecedented rates, fueled by an explosion in venture capital investment. Global venture funding more-than-doubled year-over-year, growing 111% from 2020 levels as investors poured money into unicorns and high-growth startups. As funding flooded the market, valuations soared, and startups scaled aggressively—often without the unit economics to justify their burn rates.

At the peak of this era in Q1 2022, the median late-stage company burned $10.6M per quarter. Growth-at-all-costs was the dominant strategy, but the math wasn’t penciling out. Many startups were spending far more than they could sustainably generate in revenue, propped up by the expectation of continued investor support rather than underlying business fundamentals. 

But the tide turned in 2022 and 2023 amid surging interest rates, a volatile stock market, and recession fears. According to Crunchbase data and a CB Insights report, global venture funding declined 35% year over year in 2022. Investors kept more cash on the sidelines, forcing startups to shift from hyper-growth to survival mode. 

Companies made serious adjustments to this scarcity of cash. By Q4 2024, median quarterly net burn² was scratching at cash-flow-positive levels for late-stage companies—less than three years after the immense spend seen in Q1 2022. 

Late-stage companies burned cash at record rates in Q1 2022 (median burn was $10.6M in that quarter), but then improved net burn (burned less) from early 2022 onwards.

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Net burn is improving across the board for late-stage companies. Even the highest-burn late-stage companies have significantly tightened controls, with companies in the 25th percentile bucket of net burn improving by 82% from Q1 2022 to Q4 2024.

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What about earnings?

We see this shift reflected in EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) as well. In 2021, EBITDA fluctuated significantly—and late-stage startups hit rock bottom levels in the first quarter of 2022. 

Since then, eyes have clearly been trained on improving earnings: EBITDA has made a strong move toward positivity, a demand made by late-stage investors who value companies through the lens of the EBITDA multiple. In Q4 2024, median quarterly EBITDA for late-stage companies nearly broke even at -$338K, marking a 96% improvement from its Q1 2022 low of -$8.94M. Meanwhile, upper-quartile quarterly EBITDA for late-stage companies in Q4 2024 soared to a positive $6.33M—the highest level recorded in recent years. 

A negative EBITDA indicates that a company’s core operations are not generating enough profit to cover operating expenses, suggesting potential financial instability and unprofitability. Median quarterly EBITDA for late-stage companies hit a low of -$8.94M in Q1 2022 before rebounding to -$338K in Q4 2024.

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Upper-quartile EBITDA for late-stage companies reached its lowest point (-$920K) in Q1 2022 before surging to $6.33M in Q4 2024—the highest level recorded in recent years.

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The story of unit economics

In tandem with burn and EBITDA, another striking mark of this era of efficiency is the dramatic improvement in workforce productivity. As capital constraints tightened in 2022, companies were forced to do more with less. Quarterly net burn per FTE (Full-Time Employee) has steadily improved, a clear indication that startups are prioritizing lean, cost-effective operations. Teams are smaller but more productive, shedding non-mission-critical costs that were masked by the easy-money era.

Burn per FTE – measuring the net burn a company incurs for each full-time employee – has been improving across the private markets as companies have become more efficient.

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At the same time, quarterly revenue per FTE – albeit in a more modest fashion – has risen, reinforcing that this movement is about building stronger, more financially disciplined businesses. Median companies across revenue scales are demonstrating greater top-line output per employee, a testament to the operational rigor that has emerged in this new funding environment.

Revenue per FTE – measuring how much revenue a full time employee (or the equivalent) generates for a company – is improving across private companies indicating that companies are getting more output per employee. 

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The top quartile late-stage startups are the ones with the most pronounced gains in revenue per FTE over the past year. 

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What’s driving the efficiency?

The past couple of quarters have ushered in a new era of efficiency—lower burn, stronger earnings, and improved unit economics. This sparks the question: Where is this efficiency coming from? Are late-stage startups with 100M+ in annualized revenue accelerating growth, or are they simply cutting costs?

The speed of revenue growth

When studying the Global Benchmarking data across late-stage startups, we see that quarterly revenue growth declined from 2021 to 2022 then remained relatively flat from 2022 to 2024. While Q4 2024 showed a notable uptick in revenue growth, particularly in the upper quartile, this recent boost in isolation likely isn’t enough to explain the stronger earnings and improved burn metrics we’re seeing today. Instead, the broader trend of flattened revenue growth suggests that companies have been investing less aggressively in expansion, which, in turn, has reduced their near-term capital requirements. With fewer resources allocated to scaling rapidly, costs have come down, leading to the improved burn efficiency and stronger EBITDA performance that has signaled this era of efficiency.

Revenue growth for late-stage companies dropped following 2021 and has remained relatively flat since. A positive bump in Q4 2024 indicates companies are potentially revitalizing growth. 

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How about hiring?

Since revenue growth doesn’t appear to have been the sole driver of earnings and economic growth, a closer look at operational discipline and cost control is essential. During the venture capital boom of 2021 and early 2022, companies continued to expand their teams aggressively. However, hiring momentum slowed in late 2022, and by 2023, and companies began actively reducing workforce size.

Unlike previous downturns, where layoffs were reactive cost-cutting measures, the steady decline in headcount over multiple quarters signals a more deliberate, structural shift in hiring strategies. Companies are prioritizing leaner teams, greater productivity, and long-term sustainability over sheer growth. This shift has played a key role in strengthening financial resilience.

Median headcount for late-stage companies peaked at 741 employees in Q3 2022, before declining as late-stage startups prioritized efficiency and leaner teams.

N = 203

How is this reflected in operating expenses?

As one would expect, the steady decrease in headcount has also been reflected in operating expenses. While no dramatic shift has occurred, late-stage startups in the upper quartile have clearly focused on continual diligence around operational costs.

Upper-quartile operating expenses for late-stage companies peaked at $68.5M in Q1 2022 before gradually declining to $56.3M by Q4 2024, reflecting a shift to more disciplined financial management.

N = 261

A growing gross margin

With headcount mildly tightening and operating expenses gradually declining, the sharp gains in efficiency remain slightly unclear — until we look at gross margin. Late-stage startups, in particular, have made gross margin a key focus, improving the metric by over ten percentage points since the end of 2021. Better pricing power, higher-margin revenue streams, and AI automation (e.g. in support functions) are a few factors that could be at play in this efficiency unlock. 

Gross margin – the percentage of revenue that remains after deducting cost of goods sold (COGS) – has improved dramatically across revenue buckets.

N = 259

The “get-real” moment: What comes next?

The narrative that emerges from these numbers is powerful. Over the past four years, late-stage startups have undergone a dramatic three-phase transformation:

• 2021 (“The party”): Venture-backed startups raised record amounts of capital and spent aggressively to drive growth and win their markets at all costs.
• 2022-2023 (“The wake-up call”): Market conditions shifted, capital dried up, and companies had to get serious about spending, earnings, and economics. 
• 2024 (“The ‘get-real’ year”): Late-stage startups are now fully embracing a push toward profitability, making them prime candidates for IPOs or strategic acquisitions.

This shift isn’t just theoretical—it’s happening in real time, and it has massive implications for investors, founders, and broader market participants. The data indicates that a significant number of late-stage startups are preparing for public and acquisitive scrutiny. Across the median and upper quartile, companies are taking deliberate steps to tighten operations, improve margins, and pay attention to the market’s request for profitability. But it raises the obvious next question: Where do these companies go from here?

The question is not if these companies will seek exit events, but how—and whether market conditions will accommodate them:

• All eyes on the IPO window. The fastest-growing category leaders are getting IPO-fit, watching the market closely for the right timing. Public market volatility, geopolitical instability, and trade tensions have injected fresh uncertainty into investor sentiment. At the same time, a pro-growth, deregulation-focused administration could create favorable conditions for companies considering an IPO.
• M&A momentum is building. After years of holding onto 2021 valuations, many companies who aren’t growing fast enough are finally adjusting expectations and considering acquisition opportunities. With a new administration, regulatory hurdles for M&A could be diminished. 
• Some will choose to stay private and independent. With cost structures now aligned for long-term sustainability, late-stage startups can have a level of strategic flexibility they lacked in the past. Staying private and independent may be a preferred option.

The market is turbulent, but the data is undeniable: Late-stage private companies are building businesses in a new era of efficiency. The calls for fitness in the public markets are finally seeping into the private markets. The IPO and M&A windows may not be wide open, but they surely aren’t closed. The strongest companies—those that have successfully transitioned from hemorrhaging cash to profitability—are in a position to take the leap.

Interested in diving deeper? Explore our Global Benchmarking product to compare your portfolio against 8,000+ startups, segmented by revenue scale and sector. Have questions? Reach out via the form below.

Methodology

This report summarizes data from over 8,000 anonymized startups who report data to their investors on Standard Metrics. Companies are included in an annualized revenue bucket based on their annualized revenue value calculated in each respective quarterly period. Companies self-identify their sector at time of onboarding onto our platform and can update their sector at any time afterwards. Companies are in control of their data on Standard Metrics and can opt out of their data being included in anonymous benchmarking anytime.

For graphs that represent multiple revenue buckets of companies [e.g. “Median Net Burn (All Companies)” chart above], our N count is the minimum number of companies across the smallest cohort in the most recent period (e.g. “Q4 2024”). For graphs analyzing a single revenue bucket on one metric (e.g. “Revenue Growth Trends Across Late Stage Startups” chart above), N is the number of companies in the most recent period in the chart. Data points must have at least 30 companies to be shown.

Footnotes

¹ Benchmarking data is inherently subject to cohort effects, particularly when comparing different time periods. Shifts in the composition of participating companies—such as the onboarding of higher-performing firms or changes in sector representation—may influence aggregate trends. While these benchmarks offer valuable directional insights, they should be interpreted with the understanding that cohort dynamics can impact quarter-over-quarter comparisons. 

² Net Burn is equal to Cash Flow from Operating Activities on the Cash Flow Statement. Decreases in Cash are marked as negative values.

Welcome to our inaugural quarterly benchmarking report!

We’re proud to support the financial reporting needs of over 100 VC firms and 8,000 portfolio companies at Standard Metrics. Our Global Benchmarking product delivers insights from aggregated and anonymized startup performance data to help investors and companies make better, forward-facing decisions. We’ll be sharing some of the most interesting benchmarking insights with our community each quarter.

Whether you’re preparing for a portfolio review, heading into a board meeting, or putting finishing touches on a forward-looking financial plan, fresh benchmarks will be at your fingertips with Standard Metrics.

The Latest Insights From Q3

We’re startup data nerds just like you, and we’ve dug into the last 12 quarters of benchmarking data to share interesting startup performance trends. Here are the four key highlights to share from last quarter.

1 — Top AI companies are now growing significantly faster than their non-AI counterparts.

• With eye-popping fundraising numbers, it’s clear that AI has captured the spotlight in the venture capital markets.
• We analyzed quarterly revenue growth rates of AI companies and found the upper-quartile growth rate benchmark strongly supports this enthusiasm.
• Q3 2024 showed a significant uptick in growth rates for early-stage AI startups in the $1–5M and $5–20M annualized revenue ranges. AI companies now outpace their peers in the broader innovation economy with 2X+ faster growth.

n = 50+ AI companies, 700+ companies from All Sectors

n = 40+ AI companies, 700+ companies from All Sectors

2 — Startups burn what they can, when they can

• Despite macroeconomic uncertainty over the last few years, startups continue to report similar aggregate runway levels over time.
• This consistency persists even as median burn and headcount have declined across revenue scales. When startups are flush with cash, they burn more. When markets cool down, startups significantly reduce burn to preserve runway.
• Since the fundraising peak of 2021, companies have seen cash balances decline across the board as spending has outpaced new funding rounds.
• Shrinking cash balances have driven companies to uniformly reduce their burn rates in a bid to preserve runway.

n = 2,500+ companies

n = 2,000+ companies

n = 2,000+ companies

3 — Later-stage companies are seeing hiring rebound while maintaining revenue per FTE efficiency

• For companies in the $20–100M and $100M+ annualized revenue brackets, median revenue growth has remained flat over the last 6 quarters.
• Those same later-stage companies are showing a change in their median headcount over the last two quarters; with an uptick off the bottom in H1 2024.
• This hiring trend highlights a continued focus on efficiency gains as the same later stage companies in both buckets steadily improve median revenue per FTE amidst the rise in headcount.
• The narrative is that AI is replacing jobs, but we’re not seeing it in our data yet.

n = 300+ companies

n = 100+ companies

n = 200+ companies

4 — Early-stage companies continue to reduce headcount and focus on “lean growth”

• Q3 benchmarks reveal that companies between $1-5M and $5-20M in annualized revenue are continuing to decrease their headcount from 2022 peaks.
• Revenue growth rates spiked in Q2 for this cohort but came back down in Q3; growth rates have largely been flat since Q1 2023.
• These early companies are generating more revenue per FTE than they have historically as their teams have slimmed down.

n = 500+ companies

n = 500+ companies

n = 500+ companies

In Case You Missed It

We launched our Global Benchmarking product publicly in September, and 8VC — a long-time partner of Standard Metrics — published a case study on the insights they gained from this tool. Read the case study here.

Thanks for reading, and please reach out if there are any particular topics you’d like for us to cover in the future!

– Ethan Finkel and Nathan Kapjian-Pitt, with special thanks to Jackson Gress

Methodology

• What we display for “N”:
  • For graphs that represent multiple cohorts of companies (e.g. “Median Revenue per FTE” chart above), N is the total number of companies across all cohorts in the most recent period (e.g. “Q3 2024”).
  • For graphs analyzing a single cohort (e.g. “Median Revenue Growth and Headcount over Time, Companies with $1-5 Million Annualized Revenue”), N is the number of companies in the most recent period in the chart.
  • If there are two metrics being benchmarked on the same chart (e.g. headcount and revenue growth), we display the lower N.
  • We do not display benchmarks with N < 30.
• Annualized revenue bins:
  • Companies are included in an annualized revenue bin based on their annualized revenue value calculated in each respective quarterly period.
• Sector:
  • Companies self-identify their sector at time of onboarding onto our platform and can update their sector at any time afterwards.
• Company data:
  • Companies are in control of their data on Standard Metrics. They are notified about our benchmarking product during onboarding and can get access to benchmarking insights on our platform. They can opt out of their data being included in benchmarking anytime.
  • Only companies who have completed onboarding onto Standard Metrics are eligible for inclusion in our benchmarking dataset.

In The Rise of the Data-Driven Investor Part 1, we reviewed how firms are responding to increasing competition to drive returns through data, yet how many miss the mark on using data effectively to differentiate from peers. In Part 2 of the series, we analyzed ways in which technology can be integrated into deal sourcing workflows, enabling firms to find true opportunities within their areas of specialty instead of running simple filters on millions of companies and doing grunt work on thousands of “targets.”

In the third and final installment of The Rise of the Data-Driven Investor series, we explore how smart use of data can affect firms not only during the initial capital deployment cycle, but also around reserves deployment and investor relations.

Post-deal operations matter for performance

Fund allocation strategies are set during initial fundraising, but how to deploy reserve capital depends mostly on “common sense” and high-level data on revenue or user growth. This problem is more evident in venture capital compared to private equity, and the situation only gets worse given VC reserves tend to be higher than those of PE.

Established funds have access to treasure troves of operational data (emerging funds less so, although sector specialization, robust data collection processes, and the use of external data providers typically bridge the gap.) By building sector-specific internal benchmarks and supplementing them with external benchmarks, GPs can get a better handle on how operational metrics can translate into growth levers, and how each lever’s effectiveness changes throughout the years.

Almost every fund-returning asset has had terrible quarters or years. Abandoning “struggling” companies (i.e. anyone not in hyper-growth) instead of helping fix their true growth levers is not something investors can afford, yet it happens every day. Additionally, nearly every investor laments missed opportunities within their own portfolio. According to the power law, missing the right opportunity can kill fund returns. If only we had tracked this seed-stage company more carefully and led their Series A…

Robust portfolio management can provide deeper visibility, and effective collaboration can meaningfully change reserve capital deployment. And even if we put all of this to the side, winning the best deals still comes down to founder references, especially back-channel ones. So there’s still the reputation aspect that can drive returns for the next fund. This is the long game.

LP relationships don’t build themselves

As always, investments aren’t the only building block of a successful investment firm. Limited partners are the other piece: along with entrepreneurs, they are a firm’s customers after all! Private capital markets revolve around trust, and trust is built over time. Clearly returns trump relationships, but “top decile” means that 90% of funds (do the math) don’t hit the mark.

When firms collect granular data, make actual sense of it, and then use it to support their portfolio companies properly, they can create real value for their portfolio. How powerful is sending an annual LP update where 15% of total investments previously deemed “0” are now expected to bring 3x returns, partly attributed to specific actions taken by the investor? Probably more powerful than playing around with custom fund performance benchmarks by filtering for vintages and strategies that show the firm in a good light. Power laws can return mega, but consistent support increases the chances of building long-lasting firms.

Besides hands-on portfolio support, firms that have deep awareness of their portfolio benefit during key IR tasks such as sending quarterly reports and responding to ad-hoc LP requests. This is not just a resource-efficiency matter, it’s also about instilling trust around fiscal responsibility. If firms are operationally proficient, they are also more likely to successfully source and win deals, and are likely good stewards of LP capital.

Final notes

Thank your CFO and finance team, the unsung heroes of every investment firm!

Don’t keep sourcing in the dark. Make better use of external market data and build proprietary deal sourcing channels with Grata.

Stop flying blind when it comes to portfolio management. Capture more proprietary internal data and effectively collaborate with your portfolio with Standard Metrics.

In The Rise of the Data-Driven Investor Part 1, we explored how firms are responding to increasing competition and pressure to drive returns through data. Since every firm subscribes to a data provider and collects basic financials from their portfolio companies, we reached the conclusion that data isn’t a check-in-the-box exercise to stand out from the crowd anymore.

Differentiation through data can only be achieved by making use of various proprietary and third party datasets in tandem and building robust processes to supplement the team’s area of expertise, strengths, network, and track record. Data isn’t a magic pill, it’s more like lifting weights– without proper planning and laser-focus, it stops being useful and instead can cause chronic issues / operational inefficiencies.

In Part 2 of The Rise of The Data-Driven Investor Series, we will explore how firms can build effective workflows to optimize deal sourcing and due diligence.

Setting an effective strategy

A common piece of advice founders receive from investors is to “maintain focus.” The same is true with investors as well, in virtually every aspect of their business. Raising capital from LPs requires differentiation through focus, as does finding and winning deals — not to mention growing the portfolio and delivering consistent returns.

Before thinking about data, the first question fund managers should ask is: What is the team’s area of expertise? An obvious but important note: your firm cannot be an expert in everything. Though the term generalist investor gets thrown around, according to Grata, less than 5% of PE firms market themselves as generalists. 40% are thesis-driven, with the remaining mostly falling under the thematic investors umbrella.

Once the high-level strategy is set, investors can then start leveraging internal data from their portfolio to understand which strategies and sub-sectors the team has performed well in, and where the team had issues and why. Of course, the past isn’t indicative of the future, as markets change and companies evolve. Firms need to put their internal data into context with historical market data and examine the trends in deal performance across sub-sectors, changes in the competitive landscape, and emerging technologies that can be leveraged to deliver outsized returns.

Only by merging internal and external data, while analyzing the market not just through quantitative data but also qualitative factors, can investors set a truly differentiated strategy and execute on it.

Generating inbound leads

The dream of any investor is to have high-quality inbound leads to drop into their lap. Content marketing, whether it be thought leadership on LinkedIn (the PE way) or sh*tposting on Twitter (the VC way) can provide some visibility and credibility to individual investors, although it’s usually not enough.

Jack Vawdrey at Vista Point Advisors says, “99% of firms look exactly the same from the outside. Even though every investor says they take a “differentiated approach,” just saying so doesn’t mean anyone will believe you.”

Founders, especially within the same industries, tend to know and speak to each other. There is no better inbound lead generation channel than word of mouth and that can only be achieved by providing real support to existing portfolio companies, once again highlighting the importance of building effective collaboration and data sharing processes through purpose-built systems.

Even though inbound can play a critical role in a firm’s deal pipeline, more often than not investors need to go outbound to find companies that aren’t active deals but could be through some convincing.

Qualifying and prioritizing outbound leads

The first step is finding the right long list of potential opportunities. Most data providers boast about the millions of companies they track. Of course it’s important to cover a large surface area, but filtering by a certain sub-sector and receiving 10,000 results isn’t very helpful for most investors. What data providers should really be focusing on is data depth and how well firms can filter down to 500 good opportunities instead of 10,000 decent ones.

There are underlying reasons for this issue. Most providers offer static relational databases and there are usually missing fields that significantly limit the quality of filtering tools. On top of that, since everyone has access to the same dataset and basic filtering options, data is once again table stakes and not the path to differentiation– although this is starting to change.

Some investors have started training machine learning algorithms to identify opportunities that best fit their focus and expertise. This creates a unique list of opportunities for each firm which can then be qualified more effectively. Only so much information about a company will be publicly available, and there comes a point where dealmakers need to reach out to the business owners to progress with qualification and prioritization. Without direct communication, it’s impossible to know if a company is in conversations with other investors, what their financial statements look like and whether they’re willing to make a deal. By cutting down the list of 10,000 to 500, firms are able to allocate analyst resources more efficiently and focus on the right opportunities.

Where sourcing ends and diligence begins

While there was never a clear cutoff point for what piece of information is sourcing or due diligence, technology has blurred the line between the two steps even more. Effective due diligence relies on a strong sourcing strategy, but as always, there is more.

As much as the investors evaluate the company, the founders evaluate the investors. There are also lots of emotions involved, especially in M&A. This is where investors can really shine and differentiate through their track record. There have been many founders who regretted selling their company, even if it made them generational money, because the acquirers ran the company to the ground. When investors can back up their claims about post-investment support and collaboration through data and references, founders usually respond positively.

This is the long game. Differentiation boils down to not only choosing the right providers, but building adaptable and integrated workflows that fit each firm’s focus and expertise to leverage internal and external data to the greatest extent possible.

What’s next

In the third and final installment of The Rise of the Data-Driven Investor, we will be covering how the right internal and external data can support effective portfolio management and investor relations.

Don’t keep sourcing in the dark. Make better use of external market data and build proprietary deal sourcing channels with Grata.

Stop flying blind when it comes to portfolio management. Capture more proprietary internal data and effectively collaborate with your portfolio with Standard Metrics.

Data collection has become a hair-on-fire problem for investors during this current market cycle.

We’ve written before about how startup investor relations is broken; it’s frustrating for founders, inefficient for investors, and fraught with operational headaches and inaccuracies that erode trust.

Startups are chaotic. While growing their businesses, founders need to communicate progress consistently to maintain strong relationships with their investors and get help from them. Founders spend a lot of time every month and quarter compiling and sharing data, not to mention manually tracking tasks, asks, and engagement from their investor-base.

For investors, tracking data from portfolio companies (often dozens or even hundreds at a time) is even more challenging. Most investors receive data through email instead of a centralized system, which limits their ability to easily track data and follow up with portfolio companies. Compounding this issue, most founders send data that is unstructured, incomplete, and tucked within investor updates in text format, requiring investors to standardize and centralize data manually (if at all).

Now, with the market rapidly shifting, the stakes are getting higher not only for companies, who are suddenly feeling a push to focus on their metrics and business fundamentals, but also for investors, who are feeling valuation methodology pressure from their LPs and regulatory groups.

It’s been common practice for VCs to value a portfolio company based on the preferred share price of their last round of investment. However, companies raised at high valuations over the past couple of years, and with more sober market conditions today and compressed multiples for public tech stocks, those historical valuations are now mostly considered over-priced. Regulators and lobbying groups are also pushing for more transparent, auditable valuations– Cayman Islands Private Funds Law, SEC Fund Valuation Practices Rule 2a-5, ASC 820 in GAAP, IPEV and ILPA guidelines…. the list goes on and is only getting longer over time. Given that the wider industry is moving towards level-headed and auditable valuations fueled by both LPs and regulators, VC firms need to re-evaluate and proactively mark down valuations in a consistent way, as Fred Wilson from USV recently wrote.

Complete and accurate portfolio data is needed to drive this kind of rigorous reporting process. It also helps to power other key internal workflows. For example, investors can leverage proprietary data to make more informed investment decisions and closely support portfolio companies by helping them understand their performance in a broader context. But assembling and updating this portfolio company data set is difficult, especially without technology. We discussed this topic in our Modern CFO Series and came to the conclusion that “most firms that do not aggressively pivot to data-centric, streamlined, and software-driven operations are NGMI — not going to make it.”

How can firms collaborate more closely with their portfolio companies around data and performance?

Believe it or not, most private investors in 2022 don’t use software to track their performance of their portfolio companies. (If you fall into this camp, don’t worry — you aren’t alone.) This is rapidly changing, and we’re in the early days of a digital transformation of the venture space.

But it’s a missed opportunity to not adopt a system that supports both investors and portfolio companies. Simply put: founders today expect more from their investors than a purely extractively process to collect their data. That’s why we believe that portfolio collaboration is the future, and are tacking away from the industry term “portfolio monitoring.”

Here are a couple simple principles firms should consider as they are building out internal data systems:

Make sharing information easy for founders

Founders need to manually compile data from multiple systems when responding to information requests from investors, which is a huge blocker for frequent data sharing. In an ideal world, companies should be able to pull in key metrics through pre-built integrations with accounting systems.

Another issue founders and CFOs encounter is the inefficiency around reporting similar data points to multiple investors, and manually re-entering them for each one. We know first-hand how time consuming and annoying it can be to fill out different Excel templates provided by investors, especially when the metrics requested overlap significantly. Data centralization isn’t enough — there needs to be a structured data mapping process that is compatible with both founder and investor systems to avoid re-keying data multiple times.

The problems don’t end there. No matter how efficient, investor relations is a full time job for founders, and tracking a never-ending stream of information requests and relationships is draining. A central platform for founders to track all of their historical stakeholder updates and information request responses for each of their investors is key to alleviating the risk of overlooking key external stakeholders.

Any portfolio data collection system that doesn’t address these key issues is not going to get enthusiastic adoption, severely limiting venture investors’ ability to collect comprehensive data and get value out of the system.

Make data sharing worth it

A better company data sharing experience means a better VC data collection experience, but it doesn’t stop there. Once data is effectively collected and centralized, investors need to analyze it and support their portfolio companies before trends turn into irreversible problems. Understanding “at risk” companies through real-time burn and runway data to better prioritize and offer proactive support is key to running a large portfolio. In the VC world of power laws, not taking proactive action may be a risk too great to take. So many unicorns have stories about almost shutting down… what if your next unicorn is running out of cash and you can actually help them reach their true potential?

Proactive action can sometimes be tough. Founders will always have more visibility into their business compared to their investors, so VCs typically say “Let me know how I can be helpful,” just to make sure they prioritize only the highest-value tasks. Of course, the quote has turned into a running joke in the founder ecosystem. The right platform can potentially provide a solution to this problem, allowing companies to make direct “asks” to their stakeholders as well as to enable conversations around the metrics that matter.

Finally, in a world where investors are tracking their portfolio data efficiently, how can they turn that around and help their portfolio companies to have better insights into their own performance? This is where things start to get really exciting, and we believe that the data-driven investors of the future can help their companies take their growth to the next level.

The common theme so far is that founders and investors need a purpose-built platform that is designed for both sides and covers the relationship between them end-to-end, both quantitatively and qualitatively. The right platform needs to be more than an investor relations tool for companies — it needs to be a stakeholder collaboration platform.

Portfolio management is a collaboration opportunity

Centralized collaboration between venture capital firms and their portfolio companies is exactly why we decided to build Standard Metrics. Instead of re-creating the same portfolio monitoring experience as others, focusing only on the investors, we’ve focused on the underlying problems that make the data collection process clunky. It turns out that the problem starts on the portfolio company side.

Our platform streamlines data centralization through pre-built integrations with company source systems and provides founders with a single-source-of-truth to manage their data. Founders can easily track and respond to information requests from multiple investors by mapping requested metrics to their centralized metrics repository. In addition, founders can group stakeholders such as advisors, existing investors, potential future investors, etc., to proactively push updates with varying levels of information.

By creating both value and efficiency for companies, Standard Metrics significantly improves the data collection and collaboration process for investors. By solving the issues around data collection, accuracy, and completeness, our platform sets the stage for powerful analytics and reporting capabilities for investors, all under a single solution.

Industry Pressure is Real

Private markets have historically run on personal connections and spreadsheets. Simultaneously, private markets performance has been better than that of public markets over the last two decades.

So, an old-school industry that is over-performing — why would there be any need for technology? Well, it’s the classic “success begets competition” scenario seen in rapidly growing industries:

1. Due to out-performance, more LP capital is flowing into private markets, leading to more funds raising capital.
2. With the inflow of new capital, established firms are raising more money, leading to an increase in fund size and complexity at the upper end of the market.
3. With growing fund sizes, LPs are allocating a significant percentage of their private markets allocations to existing GP relationships.
4. Some LPs are starting to conduct operational due diligence in earlier phases to better prioritize potential fund investment opportunities.
5. With high competition and limited networks, emerging managers are using technology to differentiate and convince LPs that they can return more capital through smart use of internal and external data.
6. Increasing dry powder and the number of funds competing for the same deals are leading to larger valuations and deal sizes. Especially with the recent public market downturn, returns are diminishing and investors are looking for proprietary opportunities at lower price points to make up for diminishing returns.
7. With legacy data providers (such as CapIQ and PitchBook) becoming staples in every fund, GPs are looking to find new data sources, both internal and external, to build truly proprietary deal flow engines.

These trends are resulting in the need for robust data and analytics platforms to gain an edge against competition, improve operational efficiency, and make better decisions.

Strategic Use of Data

Deal Sourcing

There are more funds and capital chasing deals than ever before. Even though valuations have begun contracting, the best companies still attract swarms of investors at high valuations. Firms need a differentiated strategy to not only identify the best opportunities that might have gotten overlooked by other investors, but also build strong relationships with the potential breakout winners before they grow into their target investment range. This is easier said than done, although certainly not impossible. With the right processes and data, firms can surface the right opportunities within their allocation strategy, and pre-qualify and prioritize their investment targets efficiently.

Due Diligence

Although data should never be blindly followed, investors are flying blind without it. Firms should leverage both market and portfolio data to support decision-making. Historical data from portfolio companies can provide proprietary insight into how companies with similar metrics and trajectories have turned out, which can be especially useful for sector-specific investors. That being said, times change, and each company is uniquely positioned in their dynamically changing industries. Supporting historical portfolio data with the latest market data can provide investors with an overarching picture of the current state of the market. Armed with both internal and external data, investors can effectively conduct deal due diligence and invest in companies that are best positioned for success.

Portfolio Management

Deal-making doesn’t end after the initial investment. Many investors reserve capital for future rounds (common in venture capital) or add-on acquisitions (common in mid-market buyouts). To effectively deploy reserve capital, investors need granular, accurate data on both their portfolio and the wider market. While portfolio company metrics are the starting point to understanding growth levers and opportunities, having wider visibility into fund cash flows and external market opportunities alongside portfolio company data is key to making informed decisions that ultimately maximize fund performance.

Data is the backbone of portfolio management, but it’s not a silver bullet. Investors need to build collaborative, mutually beneficial relationships with their portfolio companies. Data sharing needs to be seamless, and companies should be receiving value out of the process as well – investors should provide aggregated benchmarks back to their portfolio and offer day-to-day support in their areas of expertise, specifically around leveraging their networks for hiring and lead introduction purposes.

Investor Relations

Some may think that private markets run on numbers, but in reality, it runs on relationships. Investing is inherently a risky business, and the best capital allocators know that. What matters is good processes, a differentiated edge, and exceptional communication. A good track record helps immensely, of course, but nothing is more important than relationships that are formed through open communication, as even the best investors can post terrible losses, but whether they can stand back up will depend on the trust they have built with their LPs.

GPs try to predict market trends to take advantage of shifting tides, which usually ties into raising funds with new strategies and segments, requiring an LP base with strong trust. Once again, every GP’s future depends on their past– performance is a baseline, but trust through consistent communication makes the world go round, and for that, every investor needs best practice processes to collect, analyze, share, and act on data.

What’s Next

There is a lot more nuance to each of these areas. In today’s world, “everyone” is using data and technology; it’s really about how you use it to gain a competitive advantage.

In the next installment of our blog series, we will explore how to leverage internal and external data to optimize your deal sourcing processes.

No more flying blind. Contact us today to learn more about supercharging your operations through data and technology.

Capture more proprietary internal data and collaborate with your portfolio via Standard Metrics. Make better use of external market data and build proprietary deal sourcing channels via Grata.

In The Modern Venture Capital CFO Part 1, we highlighted how venture capital CFOs are perfectly positioned to lead the evolution of today’s VC firms, and we discussed the importance of data transparency to the industry.

Since then, the public markets have taken a nosedive, and the VC industry is in limbo. Teams are scrambling to understand how LPs and entrepreneurs — their key stakeholders — will behave in the months and years to come. Is this the dot-com crash, March of 2020, or something else entirely?

The VC CFO role is more crucial now than ever in this downturn. In Part 2 of this series, we’ll dive deeper into how VC firms can survive, and ultimately thrive, in the current market. Strong stakeholder communication and excellent portfolio management are necessary to make it to the other side.

The bar has gone up for the industry overnight, and we’re here to share how firms need to plan for the future. This starts with the rituals and tools that firms use to collect and utilize their critical data.

Today’s VC stakeholders are data hungry

All of a sudden, VCs are now subjected to greater LP scrutiny around their valuations and more rigorous reporting needs. Faced with difficult decisions and market uncertainty, deal teams are also increasingly demanding data to inform investment decisions and portfolio-company analysis.

To meet these demands and create greater transparency across the portfolio, VC CFOs need to rethink their data collection and reporting practices. Many firms still handle their critical business data by hand, copying and pasting data points in spreadsheets. In this environment, VC firms need to immediately focus on scalability of their data collection and visibility through software. Better data leads to a stronger narrative around a firm’s strategy and more satisfied LPs. It also empowers internal deal teams to make better investment decisions as well as opportunities to provide critical portfolio support — like identifying and helping a company that’s burning too much cash — which positively impact fund performance.

Every quarter is an opportunity to structure, aggregate, and generate actionable information on the performance of the firm and its portfolio. To professionalize this process and stay ahead of never-ending, overlapping deadlines, CFOs must develop systematic quarterly timelines that begin with portfolio company metrics and end with LP reporting and structured, standardized data designed to inform decision-making.

When a CFO perfects their quarterly timeline, it runs in the background like a well-oiled machine delivering data and insights to all relevant stakeholders.

The venture capital CFO timeline

Every VC firm operates on a timeline that dictates their approach and timing for data collection, portfolio review, valuations, closing the financial books, and LP reporting. Many of these steps take place across disparate tools and platforms in a VC’s tech stack. Bringing them all together into one centralized workflow is part of Standard Metrics’ mission.

This is a timeline developed by early Standard Metrics clients. A group of CFOs refined an ideal timeline for the quarterly cadence of VC finance and have since optimized it with the help of Standard Metrics’ tools.

Data collection: 60 days before quarter-end

Understanding the health of a venture capital portfolio starts with data collection. At approximately 60 days before the upcoming quarter-end, CFOs should be collecting data from their portfolio companies for the prior quarter: financials, key performance indicators (KPIs), product development milestones, et cetera.

Transparency is especially important at this step of the process. It’s vital that VCs communicate clearly to their portfolio companies the rationale for requesting this data. In addition to being financial backers, VCs are also strategic partners. Without full clarity into a company’s data, investors cannot provide the right advice and guidance, which is why they need to communicate the importance of transparency with their portfolio and mitigate any fear or discomfort that companies may have when sharing underwhelming data.

Portfolio review: 30 days before quarter-end

At this point, the firm should have collected all the data it needs and can proceed to a full portfolio review. This review, covering all active companies in the portfolio, should be much more thorough than the type of review that happens during a typical Monday morning meeting. Presently, the industry standard is to conduct full portfolio reviews on an annual or semi-annual basis. With the amount of data firms need to collect today, a full portfolio review should be a quarterly practice.

Our point of view is that quarterly company data is the bare minimum, and VC CFOs should endeavor to collect monthly data through automated tools. In fact, with the right systems and processes, investors can review the most relevant companies even weekly without sacrificing efficiency.

Valuations: 5 days before quarter-end

Using the financials and cap table data gathered during the data collection period, CFOs can begin developing valuations for portfolio companies.

With the recent volatility in the public markets, in addition to the increased focus on fair value and reporting regulations, blanket applications of last round valuations are not sufficient anymore. A consistently applied, auditable valuation policy, either developed in-house or with support from valuation firms such as Derivatas, is key for regulatory compliance and trusting LP relationships.

Fund accounting: 0-30 days after quarter-end

Portfolio company metrics aren’t the only important data points. In addition to calculating valuations for the portfolio, CFOs must track cash flows and calculate fund performance. In an ideal quarterly cadence, cash flows are monitored and collected throughout the quarter and not punted until a week before quarter-end, and typically managed either by fund administrators or in-house back office teams with the use of fund accounting systems.

With approved valuations and fund accounting complete, CFOs can move on to closing the financials for the quarter.

The final step in the quarterly cadence is to report finalized data to LPs and update internal stakeholders. While each LP may have unique reporting needs, creating a detailed, standardized report with granular data may solve for all unique requests while also providing LPs with additional information and an opportunity to make their own data ingestion easier. LP relationships are key to long-term success, and they are built on trust and transparency. Going the extra mile today can pay outsized dividends in turbulent times.

Preparing LP reporting materials can take substantial internal resources when done by hand; using software to deliver data-rich and compelling LP materials will become the industry norm.

The key is transparency

When you deliver transparent and timely data to your LPs, you’re not only providing an update on the portfolio and fund performance, but you’re also cultivating closer relationships with your important partners. When your internal team has access to your latest portfolio data, they can make informed decisions around how they spend time supporting portfolio companies, and they have better tools to evaluate new and follow-on investments.

To be “in the game” in today’s choppy market means having full access and control of your firm’s data. Firms that master their data will be a step ahead of their competitors both in how they make decisions at the portfolio company-level and how they service LP needs.

Most firms that do not aggressively pivot to data-centric, streamlined, and software-driven operations are NGMI — not going to make it.