Q3 2024 Startup Benchmarking Report: AI Companies Are Breaking Out (& More)!

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 banking, accounting, and payroll 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.

VC is growing up

The landscape of venture capital investing is changing irreversibly. As non-traditional competitors enter the market offering funding at dizzying velocity and scale, deal-making has become increasingly competitive and commoditized. These challenges are further compounded by the amount of institutional capital flooding into the market from LPs chasing the outsized returns that have made the VC industry what it is today.

Competition from these new players and growing pressure from LPs means that VC firms must source and diligence more deals faster than they ever have before. Despite their best efforts, many VCs will be at a structural disadvantage in this new landscape as their existing best practices were not designed to scale at such a rapid pace.

Traditional VC deal sourcing and due diligence suffer from labored processes and time-pressured deal teams. The resulting investments are made with less information, at less favorable terms, and for less control of companies. Tried and true strategies for compounding returns, such as follow-on investing, suffer from unsophisticated analytics tools that fail to provide full portfolio transparency.

While high-reward investment opportunities still exist, VCs must “level-up” and evolve to successfully deploy capital at the new speed with which the industry is moving. Leading these efforts and working to provide a solid backbone of data, software, and people processes across the firm will be the CFO.

 

CFOs: a guiding force for VCs

VC firms simply cannot function without their CFOs, who manage one of the most challenging and demanding roles at their organizations. On top of administering the financials of the firm, executing deals, and tracking performance of rapidly scaling companies, venture CFOs must also rationally value the portfolio in a frothy market where valuations are at record levels across every stage — all at the breakneck pace of today’s industry.

Venture CFOs have the unenviable role of “speed limiter,” grounding their deal team’s desire to make new investments in the reality of what capital remains to be called in a given fund. By properly pacing the fund’s dealmaking activity, the CFO avoids potentially over stretching the fund’s ability to pay fees and make new investments.

Through the execution of their wide-ranging responsibilities, venture CFOs are acutely aware of the challenges that are created and exacerbated for their firms by current processes and technology tools. This insight makes them the ideal leaders for driving the changes and improvements needed to keep their firms competitive in today’s market.

 

Data transparency, a new challenge and a new opportunity for VC CFOs

As firms deploy capital at record pace, data is driving decision-making. While data has always been integral for VCs, their investment processes are often rooted more in art than science. As part of their evolution to stay competitive, VCs must re-examine their current practices and understand where and how a bigger focus on data can have a positive impact.

The most critical component to a successful evolution is ensuring data transparency across the organization.

Data transparency means that a VC is running like a well-oiled machine where all stakeholders have access to the data they need and are working in unison. It gives a VC the opportunity to operate more like a prudent fiduciary and reduce their reliance on ad hoc decision-making.

As a nexus of the investment and operations teams, CFOs understand the unique data needs of the various teams within a VC firm. This means that they are perfectly positioned to be the strategic leaders who deliver data transparency across their organizations.

On the deal-making side of the business, data transparency facilitates data-driven portfolio management: more informed decision-making on new investments, more strategic sizing of follow-on investments in best performers of the fund, increased scrutiny for follow-on allocations, and better advice for portfolio companies.

Operationally, data transparency means more effective quarterly reporting to investors, better reserve planning, and a more strategic approach to capital raise timing.

 

 

When thinking about how data transparency can impact your organization, consider this Hierarchy of Needs pyramid for portfolio data. Now consider how different it would look if the data needed at each level was readily accessible to anyone across the organization who needs it. When the deal team, seeing outperformance in their outcome modeling for a promising investment, has access to pacing and reserve planning data, they know exactly how much they can offer that company in follow-on funding. The CFO can green light the transaction knowing the deal team’s decision was made with full visibility into the firm’s financial position, record the transaction, and wire the capital. That is the promise of data transparency.

 

Incremental changes that drive compounding impact

As non-traditional investors continue to put pressure on the industry with their high velocity deal-making, data transparency helps VC firms level-up to stay competitive. To evolve, CFOs drive incremental changes that have compounding impact across the organization. Data transparency is an important first step for VCs in this process and the CFO, as the connector between the front and back office of the firm, is the ideal leader for this evolution.

In Part II of this blog post, we will explore tactically how venture CFOs can leverage data transparency to re-imagine and revolutionize their financial operations.

You did the hard work getting great investors into your company. Why wouldn’t you spend just as much time putting them to work for you?

Investors want to prove that they can add value—in fact, they need to. Investors that last for decades win based on relationships and reputation. Positive interactions snowball, leading to more connections and more deal flow. Use this to your advantage.

Not all asks are created equal.  Learning how to level up your investor asks will not only save you time, it will unlock the full power of all your key stakeholders.

 

Help Them Help You

For better or for worse, how you ask for help goes a long way in determining what type of response you’ll get. Your investors want to help, but they’re constantly context switching from one opportunity or portfolio company to the next. Consider how you can help them take action in the five minutes of downtime they have before their next Zoom meeting. Be specific and make it turnkey.

 

Be Specific

You want to make your ask as specific as possible. Remember: your individual ask is likely one of many responsibilities that an investor has, so the more you can empower them with specific information, the quicker they can assess whether they—or someone at their firm or in their network—can help. If an investor has to clarify what you meant by an ask, it’s probably a sign you didn’t appropriately context-load them ahead of time. Avoid back-and-forth. Take the time to include everything necessary in the first outreach so your investors can get right to work.

Consider a common example of asking for potential customer intros.

1. Don’t: Ask for an intro to Acme Corp.
2. Do: Ask for an intro to the VP, Digital Marketing Analytics at Acme Corp, who you happen to know your investor is connected to on LinkedIn.

As a B2B entrepreneur, you’re not selling into a company — you’re selling into a specific org, department, segment, or persona at a company. If you can’t identify who that person is, then it’s unlikely your investors will be able to. Tell your investors why you’re targeting a specific person at a certain company and give them the background information necessary to be useful.

 

Make it Turnkey

Turn-key means actionable with all the necessary information at hand. In other words, it’s “forward-able”—and while it may seem silly, details make the difference. You want to reduce the work required on the other end as much as possible and while trivial, re-formatting an email is still work.

 

What does this mean in practice? Let’s use an example of a talent ask, something almost all founders need help with at some point.

1. Don’t: Ask for intros to full-stack engineers.
2. Do: Ask for intros to full-stack engineers with a specific background. Provide a forwardable link to a job description.
3. Extra credit: Find 2-3 Linkedin profiles of dream candidates in your investor’s network. Ask the investor if they’d be willing to reach out to those candidates on your behalf, and then send individual, forwardable emails for each of them including context on the company, role, and why you’re interested in meeting.

Doing this right requires much more upfront work, but that extra effort is the difference between getting something done and having another ask die on the vine.

 

Competitive Spirits

Investors are competitive. Keep that in the back of your mind as you structure how and where you make your asks. Give credit where credit is due—give shout-outs to investors who have responded and were helpful to asks. There’s a difference between needlessly creating competition and sparking investor’s natural desire to go above and beyond; choose the latter.

Standard Metrics’ Investor Update tool allows you to centralize all of your updates and asks by bringing your investors into a single, shared space where you can manage who is actually being helpful from a simplified vantage point. This tool helps you track and monitor your asks, but you still need to make sure the asks are effective.

If you’re sending investor updates with email today, book a time here for a 1:1 onboarding session for your next investor update. We’ll discuss best practices, help you craft your narrative, and share templates!