The Metrics that Matter for Growth Stage Startups

Originally published: January 30, 2020
Updated: Sept 25, 2023

Early lessons on SaaS metrics

Early in my career in growth equity, I was (willingly) pulled into the rabbit hole of SaaS metrics.

As strange as it might sound, relative to multi-variable financial modeling, the straightforward nature of SaaS metrics was actually refreshing to me. No complex discounted cash flow (DCF) or leveraged buyout (LBO) models and only a handful of inputs to worry about? As a former investment banker, I considered this a win.

I was also excited when it dawned on me that SaaS metrics don’t typically change much. David Skok published his widely-read SaaS Metrics 2.0 framework over a decade ago in 2008. That article remains highly relevant to this day.

As I developed a deeper understanding of SaaS metrics, I read many blogs and frameworks on the web. If you’ve done any Googling in this realm, you’ve probably reached the same conclusion I did: everything that could possibly be said already has been. So, once I had found my favorite articles and formed my perspective, I stopped pushing myself to learn more on the subject. In other words, I thought I had it covered.

In hindsight: boy, was I wrong.

Fast-forward a few years, I can tell you that SaaS metrics are anything but straightforward. There are important variances in the way that each company calculates and perceives them. Board members often disagree on which metrics are most important and provide mixed guidance to entrepreneurs. And even if you get the calculations right, knowing what to do with the data is a problem in its own right. Finally, business models are perpetually changing, and digitally-enabled platforms, for instance when Uber and Peloton IPO’d in 2019 threw into question how we measure technology company financials altogether.

Today, I know that the devil is in the details when it comes to understanding SaaS metrics. There is no one-size-fits-all, and because of that, the learning never stops. In short, I’m still deep in the rabbit hole of learning, and that’s probably the way it should be.

Why are we writing this?

The purpose of this article is to shed some light on my learnings, as well as Georgian’s overall perspective on SaaS metrics. As an organization, Georgian is dedicated to supporting and adding value to entrepreneurs throughout their growth journey. With this article, we hope to provide insight into the way we think about measuring efficiency and predictability, how we assess company performance and how we make investment decisions.

We will also cover some less frequently discussed nuances, including where SaaS metrics may fail in practice. Finally, we’ll see how SaaS metrics change (or not) in the context of software companies leveraging AI.

Note: We’ve shared links to some of the leadingSaaS metrics sources throughout this article and in the accompanying infographic because as mentioned, there’s a lot of great content out there. The metrics we deep dive into are the ones we deem the most important.

Measuring what counts

When companies hit the growth stage — usually around their Series A or B financing — investors expect a level of consistency in the company’s growth plans. The product, team and company vision are no less important, but the evaluation of metrics alongside those factors becomes critical. The addition of a standardized set of metrics allows investors to connect the numbers with the founders’ vision, as well as provide a potential measure for the quality of the company as it scales.

At Georgian, we begin this measurement by asking two important questions: the first is “is this business efficient?” and the second is “is this business predictable?”.

Is this business efficient?

Some companies spend more money to acquire customers than others. The science behind measuring this is called sales efficiency. In layman’s terms, it measures a business’s ability to repay an additional dollar invested in sales and marketing.

Sales efficiency is critical because companies with longer, more costly sales cycles require more capital to grow. Raising additional capital dilutes existing investors and employees, limiting the economic upside. As a result, more efficient businesses are typically viewed as better vehicles for investment.

To assess sales efficiency, our metric of choice is Gross Margin Customer Acquisition Cost (CAC) Payback. This metric calculates the number of months required to repay the initial cost of acquiring a customer accounting for the business’s gross margin. We pay close attention to Gross Margin CAC Payback because it accounts for the velocity of customers (revenue) being acquired, in addition to their profitability. Benchmarks for the metric range between 12 and 24 months, with longer paybacks being more common for enterprise deployments (based on the aggregate G7 benchmark metrics).

Other payback metrics such as the SaaS Magic Number or Payback Ratio measure sales efficiency based on the recurring revenue dollars being acquired. While we do track these metrics, we place less weight on them because they suffer from a drawback: they assume all revenue is equal, regardless of profit margin. In practice, we know this isn’t true, and companies with lower gross profit require more capital to grow.

In our view, sales efficiency is a science in and of itself and goes beyond the metrics mentioned above. Many companies find it useful to build a sales dashboard in order to measure levers such as lead velocity, conversion ratios and quota attainment (each of which impacts sales efficiency). Ultimately, from an investor’s standpoint, we typically want to see is a repeatable go-to-market playbook that remains efficient as the business scales.

Is this business predictable?

Predictability is a two-part question, broken down into retaining and monetizing customers.

We would argue that retaining customers is, in the vast majority of cases, one of the most important indicators of health in a software business. This is because churn management becomes exponentially more challenging to offset as companies scale. Backfilling for $20K in lost revenue is one thing, backfilling for $20M is another—and can be the difference between massive growth and failure. As a software business grows, churn becomes a determining factor for the maximum size a business can reach.

Three other commonly cited metrics to measure retention are Gross Logo Retention, Gross Dollar Retention and Net Dollar Retention. Of these, Net Dollar Retention is the leading SaaS metric.

Net Dollar Retention answers the question, “at what rate would your business continue to grow (or shrink) if you were to rely only on sales from your existing customer base?”. It measures what percentage of revenue from current customers a business retains from the prior period, after accounting for upsell, downsell and churn.

Software companies targeting the enterprise should aim for Net Dollar Retention of greater than 100%, or at least in-line with the average for public company Net Dollar Retention at the time of IPO. In our experience, the top-quartile enterprise software companies have Net Dollar Retention in excess of 120%, with 110% being our benchmark.

Up For Renewal Analysis is another retention metric we rely on frequently. It measures the number of customers that renew as a percentage of the number of customers that are up for renewal during the period (either in dollars or number of customers). The benefit of the calculation is that it strips out the noise of multi-year contracts and customers who are not “up for renewal” during that period. These factors can artificially inflate the true retention characteristics of a business, particularly in the early stages of growth.

The second piece of predictability comes in the form of monetizing customers, commonly measured by the Lifetime Value (LTV) to Customer Acquisition Cost (CAC) calculations. These metrics test how much a customer is worth (in contribution margin) in comparison to what it costs to acquire them. An LTV/CAC of greater than 3x is considered good; however, earlier stage investors often target figures in excess of 4x or 5x. The crux of it is that 3x LTV/CAC allows a business, operating under normal software margins, to be operating profit break-even in year four.

We sanity check LTV/CAC calculations in a variety of different ways. For example, we often normalize for shorter customer lifetimes (between 3 to 7 years), rather than relying on the lifetime as calculated. This approach is designed to allow us to test whether unit economics would still make sense if retention assumptions are overstated, as they can be early on in a company’s lifecycle. It is also possible to switch between using Gross Logo Retention or Gross Dollar Retention to calculate a customer’s average lifetime.

It can’t be that easy.

While the calculations themselves often appear to be basic, issues often arise in one of two areas.

1. Lack of data availability: For example, calculating customer-level revenue is often an issue, as is the inability to allocate sales and marketing expenses properly.
2. Leveraging the metrics: Even if you calculate the metrics properly, it’s difficult to know what to do with the numbers (i.e., what tangible insights can be taken from analysis?).

In our experience, some common pitfalls occur in using SaaS metrics.

• Use a KPI dashboard: SaaS metrics should be measured in relation to one another. Typically, metrics are not useful in isolation. A helpful way to track SaaS metrics is to build a KPI dashboard and measure performance over multiple periods. Once a dashboard is created, it should be updated regularly and make the information available to key decision-makers. Two starting points are these ones by Redpoint Ventures (Tomasz Tunguz) and forentrepreneurs (David Skok).
• Benchmark against the best: Comparing company performance against median and top quartile benchmarks can help understand where you’re ahead or behind versus peers and adjust accordingly. Openview and Keybanc and Iconiq annually publish SaaS benchmarks for private software companies.
• Build metrics into your financial forecasts: One of the biggest mistakes we see companies make is failing to understand what their forecasts suggest in terms of SaaS metrics performance. Putting a plan in front of investors that suggests the Gross Margin CAC Payback will drop from 53 months to 5 months next quarter—without any significant changes to operations— can damage the credibility of your projections. Calculate your future SaaS metrics to tell the story of how your planned operational improvements will impact business performance.
• Align with the board: Every investor is different and will have an opinion on which metrics are most important. The key word here is opinion. Investors, including us, come with preconceived ideas of the importance of certain metrics based on their own experiences. Those lessons may not be as relevant to the company your team is building. Have a view of which metrics matter most for your business and focus on executing on those metrics as you scale. Getting your board aligned with your approach will help in the longer term.
• Permeate metrics across the organization: Too often, the discussion on SaaS metrics begins and ends with senior management (or the board). Leading companies speak openly about company metrics and use them as fuel to team drive performance.
• Align compensation with desired outcomes: Peter Drucker famously said: “If you can’t measure it, you can’t improve it.” The same goes for compensation. Once you’ve identified key SaaS metrics, set annual goals and base some portion of executive compensation on those metrics. (Actual goals are often based on higher-level figures such as ARR added, recognized revenue or EBITDA burn, but have trickle-down effects on SaaS metrics.)

So what about AI?

Georgian invests in companies that leverage disruptive technologies to gain a competitive advantage. Today, we focus on the intersection of applied AI, conversational interfaces and trust. Because of this, founders often ask how SaaS metrics change for AI companies.

In theory, the answer is more not that much. At the end of the day, the basic laws of software economics continue to apply to AI companies.

AI can help shape business outcomes, similar to cloud, mobile and other transformational technologies before it. Like all technologies, AI needs to be aligned with and positively impact organizational goals and metrics. In other words, AI is another lever that can be used to accelerate growth and reduce costs across the organization, ultimately improving SaaS metrics over the long-term.

A good example of this in practice is aggregating cross-customer data sets to augment machine learning models. Augmenting machine learning models with customer data in this way can improve the speed at which a company can draw inferences from customer data for new customers, in an effort to solve what’s commonly known as the “cold-start problem.” This problem means a company can onboard users to its products faster and reduce friction at the start of the customer journey. Shorter implementation cycles mean companies recognize revenue faster, which improves sales efficiency, LTV/CAC and a host of other metrics.

In practice, there’s more complexity to it than that. The board and leadership team must be aligned in shared understanding that AI-based investments require capital and time to play out. R&D spend is often higher for companies pursuing AI, as they require time and investment to scale data science efforts. Sales cycles for AI products can sometimes be longer as well, due to the inherent complexities of accessing customer data and the trust issues involved.

Being an AI company also fosters questions about product development, such as effective model quality assurance, ethical use of data, data moats and overall model accuracy. These factors have an impact on SaaS metrics over time.

In our view, B2B companies should leverage AI the same as any other company: understand the metrics in the context of traditional software metrics. Take SaaS metrics for what they’re worth, but also understand applicable differences and use them to your advantage in scaling your platform. In the end, it’s AI’s long-term impact on operating performance that counts.

In closing 

While there’s a lot of information out there on SaaS metrics, in our view, a company can keep it simple because efficient and predictable businesses are still important to sustained growth.

When it comes to measuring efficiency, companies should track their Gross Margin CAC Payback. When it comes to predictability, key indicators such as Net Dollar Retention, Up for Renewal Analysis and LTV/CAC should be considered. These measures work together to provide good insight into the health of a software business.

And as for AI? While it changes many things about building and scaling software companies —  in our experience — it doesn’t change the basic metrics for measuring strong companies. 

This piece was originally created for CoLab, which is Georgian’s pre-investment program designed to help growth-stage companies scale. The program offers resources for SaaS companies, meant to encourage their growth before we proceed with investment. To learn more about CoLab, connect with Conor Ross (conor@georgian.io).