KPIs: How do you know what’s working?
The central analyses you need to master at the growth stage
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Measuring success
As the primary goal of the go-to-market fit phase is to achieve sustainable unit economics, it’s crucial to define which Key Performance Indicators (KPIs) and metrics to track. In this section, we outline a framework for the core SaaS unit economics that will help you and your key stakeholders understand what matters most at the GTMF stage.
Before diving into the specifics, it’s worth taking a step back. You’re likely already doing a fair bit of tracking, but it helps to clarify which metrics to use at what stage, how to use them, who they’re really for, and how you should interpret them. Building that shared understanding makes it easier to align your team, board, and investors around the same definition of success.
What KPIs are and how to use them
KPIs are the measurable signals that show whether your strategy is working and/or whether you are adhering to it. They’re how you translate your vision into data, turning your goals into something you can track, test, and improve. But KPIs are only powerful if they’re used as tools for learning and focus, not just reporting. It is important that each stakeholder working toward a KPI understands it and how it relates to the bigger picture. The best founders use KPIs to stay honest, spot friction early, and make quick, confident decisions. During the GTMF phase, the right KPIs will tell you whether your go-to-market engine is truly repeatable, scalable, and capital-efficient. In other words, it will tell you if you have the fundamentals of long-term, sustainable growth.
Different audiences lean on KPIs for different reasons:
- Founders and leadership teams use them to make trade-offs and focus attention where it matters most.
- Investors and boards look to them for proof that growth is both efficient and defensible.
- Functional leads and operators rely on them to know what “good” looks like in their domain — and how it impacts company goals.
Well-chosen KPIs create a shared language across the company. They make it easier to communicate progress and ensure everyone is pulling in the same direction.
How KPIs evolve with growth — and how AI is shifting the benchmark
As your company matures, the metrics that matter will shift. In the previous section we covered the various stages of growth, and alluded to the fact that at the PMF stage, you likely would have leaned on things such as Leading Indicators of Retention (LIRs) to guide you. As the company matures, you’ll have more customers and more data which will allow you to dive deeper into analysis of your performance.
The KPIs that matter most per stage are:
- At product–market fit, you focus on engagement, retention, and validating real customer value.
- Activation rate and user retention
- Usage frequency/depth of product engagement
- Net Promoter Score (NPS) or other satisfaction indicators
- Early retention stats and Leading Indicators of Retention
- Early revenue traction and cohort growth
- Customer feedback loops and product iteration velocity
- At go-to-market fit, efficiency takes centre stage: sales productivity, conversion, and payback.
- Revenue growth rate and quality of revenue (ARR mix, expansion vs. new)
- Retention and expansion metrics (NDR, GRR)
- Long-term gross margins
- Sales efficiency (magic number, sales cycles)
- Lead volumes per channel and conversion rates
- Emerging core unit economics (CAC payback period, LTV/CAC)
- Proportionate efficiency metrics vs growth rate (burn multiple)
- Clear patterns of consistent customer usage/engagement
- At growth & moat, it’s about scaling profitably and deepening defensibility.
- Established gross margins
- Proven unit economics at scale
- Operating leverage and EBITDA margin
- Capital efficiency and cash conversion
- Product adoption and feature-level engagement
- Brand awareness, share of voice, or category leadership signals
- Talent retention and productivity indicators
In the rest of this section, we’ll focus on the KPIs that matter most during GTMF, which are the metrics that show whether your commercial engine is working predictably and efficiently enough to scale, and tell you why. You will have seen benchmarks for these metrics in the past, but as AI has revolutionised how companies operate and at what speed, it is only natural to expect that this also is reflected on the metrics. While AI doesn’t necessarily change what you measure, it’s redefining what “good” looks like. When we go through each of the core metrics GTM leaders should track at this stage, we will share whether and how AI has affected the benchmarks you should be measuring yourself against.
We start by untangling some core questions around revenue, and distinguish between how different departments refer to revenue for their own purposes. Then, we get tactical and provide a comprehensive overview of the “core” metrics for a scaling software business. We review the foundational metrics that are used to calculate other, second-order KPIs, then cover the most popular KPIs – highlighting some pro’s and con’s for each.
Let’s talk about revenue
Thinking about revenue quality from an investor perspective
One of the superpowers of a scaling B2B software business is the highly predictable, recurring nature of customer revenues. If things are going well, and customers love your product, they will continue to use it year after year (low churn). But not all revenue is created equal (see for instance this article in Sifted). At the GTMF stage, it’s important to look beyond the top line and understand what kind of revenue you’re generating: how predictable, recurring, and scalable it really is.
Investors sometimes refer to the predictability of future revenue from existing customers as “quality of revenue”. While there are a number of quality indicators to be aware of (more on gross and net retention later) there are some tiers of revenue quality that can result from your business model:
TIER 1 – CONTRACTED RECURRING REVENUES
Contracted recurring revenues, where your customers agree to pay for your product over a one or multi-year time period.
Example:
Annual software subscriptions for platforms like Salesforce, HubSpot, or Workday, where customers commit to a defined term under contract.
TIER 2 – UNCONTRACTED REPEATING REVENUES
Uncontracted repeating (not recurring) revenues, where your customers agree to pay for your product on a non-subscription basis (perhaps individual transactions in a payments processing business, or usage credits for a telephony software business). While they may lack the security of contracted revenues, they still demonstrate a level of customer loyalty and provide a certain degree of predictability.
Example:
Usage-based models like Twilio (charged per API call) or Stripe (per transaction). These revenues are repeating in practice but not contractually guaranteed.
TIER 3 – “ONE-OFF” REVENUES
“One-off” revenues, such as implementation, training and consulting fees, which are not recurring in nature. These are typically generated through additional services provided to customers, beyond the core subscription offering. While these revenues can be excellent cash flow generators, they are typically not recurring (and therefore less predictive).
Example:
Professional services work during onboarding for products like NetSuite or ServiceNow, or bespoke consulting and integration projects. These can be valuable cash flow generators but don’t contribute to predictable, long-term growth.
As AI permeates the world of software, it has had a material impact on revenue speed and quality. Relevant for this section is the propagation of extended trials or “Proof of Concept”-type contracts. As many individuals and companies explore new AI software tooling, there has been a lot of new revenue created that might appear to be part of ‘Tier One’ above; but in reality, the revenue and contract is more experimental in nature and better thought of either as ‘Tier Two’ or ‘Tier Three’ revenue. It’s important to distinguish which of your customer deployments you believe to be truly recurring long-term partnerships, or something more exploratory.
INVESTOR COMMENTARY
Investors will typically value software businesses based on a multiple of revenue, and will attribute higher value to higher quality, more predictable revenue streams (like the Tier 1 described above). A key indicator of go-to-market fit is knowing when to say no to custom or off-strategy work that may drive short-term (and therefore lower tier) revenue but reduces focus and predictability over time. As a company matures, it can be helpful to develop clear decision-making criteria for these ad-hoc client requests, aligning GTM and product leaders on which opportunities truly support your long-term strategy and reflect the KPIs you’re optimising for, such as gross margin, net retention, or payback period.
From a sales leadership perspective, this can be hard to navigate. Targets are ambitious, sales teams are motivated by commissions, and the instinct to close every deal is strong. But the best leaders connect these day-to-day decisions back to the company’s core KPIs — the metrics that underpin value creation and investor confidence. Deals that align with your Ideal Customer Profile, strengthen retention, and drive efficient growth improve those KPIs and, ultimately, your equity story. Those that don’t may hit the number this quarter but dilute the company’s long-term performance. Keeping revenue quality front of mind ensures your growth metrics tell the right story – to your team, your board, and the market.
What different departments look at in terms of revenue
To expand further on the complexity of revenue, we sometimes encounter confusion within businesses where different departments will use different terms to describe a company’s revenue. To clarify, here’s an easy way to remember the distinction:
Recognised Revenue
Recognised revenue includes both recurring and non-recurring revenue. For the recurring part, revenue is recognised over the course of a customer’s contract. In a typical 12-month contract, the revenue would be a segmentation of the contract value over the contract length.
Example:
If a customer contract was annual, at a total contract value of $100,000, the monthly revenue recognised would be $8,333.33
Within the organisation, revenue is often the focal metric for the finance department.
Monthly Recurring Revenue (MRR) or Annual Recurring Revenue (ARR)
Monthly or Annual Recurring Revenue (MRR or ARR) is a KPI for the ongoing recurring revenue at the end of any period. This KPI is central to B2B software contracts as a measure of the recurring revenue base of a business.
Unlike recognised revenue, MRR or ARR do not include “one off” activities, and instead only include the recurring value of the active customer subscriptions at the end of a period.
The team responsible for interfacing with investors is typically most focused on MRR and ARR, as this is a core metric for investors to evaluate the business’ future revenue.
Bookings
Bookings, on the other hand, may well take place before the customer’s contract period has started, and represent the value of all the contracts or sales orders secured by the company within a specific period, regardless of whether the revenue has been recognised yet.
Example:
If two customer contracts were signed in March, with customers due to “go live” in April, each with an annual contract value of $100,000, the March bookings KPI would be $200,000.
The sales department is typically most concerned with tracking and maximising bookings, as bookings provide visibility into the future revenue potential and serve as an indicator of the business’s sales performance.
Billings
Billings refer to the actual invoiced amount for the services or products delivered to customers within a given period. The billings timing for a contract can differ significantly from the revenue recognition timing. For example, a customer contract might have revenue recognised linearly across a 12-month period, with billings being quarterly, or annually in advance.
The accounting and finance teams are typically responsible for tracking and managing billings, as billings represents the cash collection activity of the business and is often used to assess the predictability of cash flow.
CARR vs ARR – what is the difference between these metrics and why should you care?
The terms and definitions above highlight a clear challenge: customer contracts can be “booked” (i.e. included in bookings), but may not yet be live with the product or recognised in revenue or ARR terms. But evaluating a business’ future revenues without including these contracts would understate the business’ potential.
To state the value of these contracts, companies will sometimes add an additional KPI: Contracted Annual Recurring Revenue (CARR), where CARR includes the contract value of active subscriptions (as with ARR) and adds the value of subscriptions that have been booked (i.e. contract signed) but have not started.
As a result, in a growing business, CARR exceeds ARR with a lag. There is a natural health warning to a focus on CARR, which is that focusing only on CARR can mask the true growth profile of businesses with long delays between booking and “go live”, or long implementation/onboarding periods.
Tip! Software investors will often look closely at CARR, but should expect to understand the bridge between CARR, ARR, recognised revenue and billings.
With the overall concepts within revenue clarified, let’s move on to look at the actual unit economics you should concern yourself with at the go-to-market fit stage.
FURTHER READING / REVENUE / IVP
FURTHER READING / REVENUE / TOMASZ TUNGUZ
Foundational unit economics for a B2B software business
Which metrics should a B2B software business be reporting on when they hit the scale-up stage? A business’ foundational unit economics describe the economic tradeoff between the benefit of a customer’s revenues through the customer lifetime. In essence, it tells you whether acquiring this customer is “worth it” long term. Central to understanding this are “Customer lifetime value” (LTV) and the cost of acquiring that customer in the first place, “Customer Acquisition Cost” (CAC). Alongside the Gross Margin (GM), these three metrics form the foundation from which multiple other metrics are derived. We’ll get into these KPIs in more detail shortly, but first, let’s understand the foundational unit economics LTV, CAC and GM.
FORMULA CHEAT SHEET
LTV = [ $ Average Revenue Per Account x % Gross Margin] / % Churn
CAC = $ Total Sales & Marketing related expenses / # new clients
GM = [Net sales – Cost of Goods Sold / Net Sales
Lifetime Value (LTV)
[$ AVERAGE REVENUE PER ACCOUNT x % GROSS MARGIN] / % CHURN
ARPA = Average Revenue Per Account
GM = Gross Margin (see below)
Churn = Gross Churn (often calculated on a volume or logo basis) over the same period of time as your ARPA
The lifetime value is a central metric for software businesses, as it shows how much economic value a customer adds to your business through the customer lifecycle. As you might expect, higher LTVs are better! So, what reference points should you use to assess whether your LTV is good or not? Unsurprisingly, this depends on your target customer profile: if you’re selling higher volume/lower priced products to SMBs a “good LTV” will differ considerably from a “good LTV” selling lower volume/higher priced products to enterprises.
LTV is calculated as [$ ARPA * % GM] / %Churn. As an example, a business with $40k ARPA and 75% GM with 10% annual logo churn means clients on average have a 10y lifetime, and the LTV is $300k.
Choosing the right churn metric
Using net churn, rather than gross churn, as the fraction’s denominator can result in infinite customer lifetimes, which is an unrealistic assumption. You can read more about the differences between the different kinds of churn here. There are several ways to solve this problem. Some companies calculate LTV on the basis of a 3 or 5 year fixed customer lifetime (rather than using “churn” as the fraction’s denominator). A more precise calculation is possible using cohorted gross logo churn to accurately predict lifetime length and incorporate the impact of net upsell on ARPA over time.
Be mindful of the difference between your ARPA and latest selling price (ASP)
One assumption implicit in the LTV calculation method above is that ARPA is stable during the customer lifetime. But that’s not true for all businesses: often your Average Selling Price (ASP) and year two or year three ARPA can be quite different. Be careful here if your ASP significantly differs from your customer’s mature ARPA: this can dramatically bias your LTV calculation.
Customer Acquisition Cost (CAC)
TOTAL S&M RELATED EXPENSES / #NEW CLIENTS
To understand the fundamental unit economics of a B2B SaaS business, we need to consider both the value of a customer, and the cost of acquiring and serving that customer. So, what are the right costs to include in this calculation?
The primary cost of acquiring customers is Sales and Marketing spending. To fully account for the cost of acquisition, Total Sales and Marketing (S&M) related expenses should include all S&M costs along with selected operating costs as they are used by the S&M team.
Some companies will exclude some parts of senior resource or experimental/brand spend from the total S&M calculation, as these activities are not directly customer acquisition costs. For example, a company might exclude the CMO and CRO costs from CAC, on the basis that these personas are central management overhead, rather than directly linked to customer acquisition. This is a nuanced question, and some subjectivity exists. The important thing to remember is to keep consistent in the way you report on this, and to disclose your methodology fully, otherwise you won’t be able to compare your metrics reliably against benchmarks. Your investors (both current and prospective) will also want full clarity on what you include in this.
Aligning the S&M spend to the sales cycle
Many businesses will lag the S&M expense used to calculate CAC by approximately the length of the sales cycle. This might be one month for SMB software subscriptions, but can be one or two quarters for enterprise software. Aligning the spend to the sales cycle ensures the acquisition cost is lined up with the clients acquired through that expense, and therefore should tally with analysis conducted on pipeline conversion, or Account Executive productivity, etc.
Tip! It’s useful for the accounting team to spend time with the sales and marketing managers who are in charge of cost centres and explain this rationale, as the delay may not be wholly intuitive, and the setup may differ from company to company, which can cause confusion in the budget process.
Make sure to use the relevant costs when calculating CAC
Please note that sales and marketing costs aren’t the only relevant acquisition costs for all businesses. Their relevance depends on your GTM strategy (see section 3). For example, PLG businesses will have artificially low CAC when considering only S&M. Consider breaking out the costs of the product and engineering resources that work to improve the efficiency of your customer acquisition cycle (this might be labelled as a “growth squad”) to get a sense of their relative contribution to ARR acquisition.
SaaS Gross Margins (GM)
[$ NET SALES − $ COS] / $ NET SALES
The gross margin is a financial metric that represents the percentage of revenue a company retains after deducting the direct costs associated with producing or delivering its products or services. It is a key indicator of a company’s profitability and operational efficiency.
While the general accounting for gross margin has improved at the later stages of private and public markets, there’s a high level of variability in the earlier stages. To calculate the GM, you need to know the value for Cost of Goods Sold (COGS) and/or Cost of Sales (COS). These aren’t the most clearly defined terms in the software world. To differentiate between them, COGS specifically refers to the direct costs involved in producing or acquiring goods or services, while COS includes COGS along with other expenses directly related to the sales process. The term used can vary depending on the context and industry.
Our view is that a reasonable measurement of your COS should include:
- Hosting/infrastructure
- Data costs
- Professional services
- Part of customer support/success
Where to place Customer Support/Success
The Customer Support/Success (CS) team presents a challenge here. Costs associated with customer onboarding/servicing/gross retention should be included in COS, but any CS staff who are revenue generating (i.e. focused on generating upsell, rather than servicing customers) sit more naturally in S&M operating expenses (OPEX). Our rule of thumb is that any CS function that has revenue-linked quota targets moves further down the income statement, from GM into S&M.
FURTHER READING / FOUNDATIONAL UNIT ECONOMICS / DAVID SKOK
The most popular KPIs – and their limitations
There are five popular KPIs that are very commonly tracked: LTV: CAC, Magic Number, CAC Payback period, burn multiple and the Rule of 40/Rule of X. These KPIs are worth calculating because they are pervasive and well benchmarked, and companies are expected to have a good grasp on these numbers. However, it’s important to understand their limitations. These limitations are particularly apparent when it comes to GTM approaches such as product-led growth, or community-led growth.
LTV: CAC
This metric is the ROI-style ratio of lifetime value to the relevant acquisition cost. Generally, 3:1 is considered a good threshold to aim for, as a unit customer’s profitability should give sufficient margin to pay for other overheads and allow the business to be theoretically profitable at scale.
A key issue is that actually achieved lifetime figures often aren’t available until a SaaS startup is over three years old. Reliable lifetime predictions can be elusive and unreliable, particularly in new emerging categories. Another issue is businesses with negative net churn have implied infinite lifetimes. Using fixed-period ‘lifetimes’ can help, or using gross cohort churn rates to statistically estimate projected lifetimes.
It can also be confusing how to incorporate upsell. Our recommendation is to make sure your required CS cost is included either in GM or by adjusting CAC. Otherwise this results in highly spurious ratios, particularly for enterprise SaaS companies, or businesses with very high upsell rates.
This is such a common and challenging KPI ‘problem’ we have written a detailed ‘how-to’ article on this piece for SaaStock, available, available here.
Magic Number
NEW ARR/TOTAL S&M
This simple ‘rule of thumb’ metric provides a measure of the ratio between sales and marketing investment with new revenue. Different interpretations of ‘new ARR’ are possible here, so be careful when comparing benchmarks – some companies will use gross new ARR and others net. The periods of new ARR vs. S&M cost are also sometimes lagged to account for sales cycles (see CAC section for a description of this).
For businesses with a sales-driven GTM strategy, a magic number of above 0.7 is considered healthy and over 1.0 is considered a strong signal to invest in growth. Magic number calculations for PLG businesses are often extremely high, as new ARR growth isn’t driven exclusively by S&M spending, so the denominator doesn’t represent the ‘true’ cost of growth. As a result, this benchmark isn’t widely used for PLG driven businesses.
Please note that while the Magic Number is a helpful comparable, it takes a simplistic view of sales and marketing efficiency and has several issues. These have been outlined by Nnamdi Iregbulem in an article you can read here.
CAC Payback Period
GM% x ARPA / CAC
This KPI summarises how quickly a business can earn back the upfront investment required to acquire a new customer. Short payback periods drive higher capital efficiency because the customer revenue can be used more quickly to cover overheads on top of sales and marketing costs.
The Burn Multiple
CASH BURN RATE/NET NEW ARR
The term “burn multiple” refers to a financial metric that is used to assess the ratio of cash burn to ARR growth. The main benefit of the burn multiple is that, in contrast to other more S&M-focused efficiency metrics, such as CAC payback, LTV/CAC and magic number, it assesses a company’s overall growth efficiency across all operational aspects of its business – whether that’s marketing, sales, product, development, CS, PS or G&A.
From an investor perspective, the burn multiple shows how efficiently a company is deploying capital to grow. For fast growing, unprofitable companies, it is the closest analogue to a true profitability metric such as return on equity or return on capital employed that investors use to assess large profitable companies in the public markets.
As with other metrics, there are different definitions of the burn multiple. As investors, we prefer the “SaaSified version”, where the burn multiple is calculated by dividing the company’s cash burn rate for a particular period by the net new ARR added over the same period; typically a month, a quarter or a year.
The Rule of 40/X
REVENUE GROWTH RATE + PROFIT MARGIN ≥ 40%
The Rule of 40 provides a rough measure of a company’s financial health by comparing its profitability and revenue growth rate. The rule states that a healthy software company’s revenue growth rate and profitability margin added together should be equal to or greater than 40%. It is expressed as the sum of the company’s annual revenue growth rate and its profit margin.
The Revenue Growth Rate represents the percentage increase in a company’s revenue over a specific period, often on an annual basis. It indicates how fast the company is growing its top line.
The Profit Margin is usually measured at operating level, i.e. operating profitability, or in financial statement parlance: EBITDA (Earnings Before Interest Taxes Depreciation and Amortisation).
By summing up the revenue growth rate and profit margin, the Rule of 40 helps assess whether a company is balancing its growth ambitions with sustainable profitability. A score below 40% may indicate that the company’s growth is not compensating for its lack of profitability, potentially raising concerns about its long-term viability. Conversely, a score above 40% suggests a healthy balance between growth and profitability.
While the Rule of 40 can provide a quick snapshot of a company’s balance between growth and profitability it is a metric that is reductive, sometimes misleading and frequently used with insufficient discrimination. We don’t recommend using the Rule of 40 to assess companies that aren’t yet mature and are therefore investing in building the foundations of product and go-to-market. For fast-growing SaaS companies with bold ambitions, this means the Rule of 40 only becomes relevant at real scale, i.e. several 10s of millions of dollars of ARR if not $100m of ARR.
For more in-depth information on the limitations of the Rule of 40 see an article by Mikael Johnsson on the topic here.
Bessemer have brought out a tweak on this model called the Rule of X, which weights revenue growth by a multiplier. This is because in simple regression analysis, it has consistently been determined that revenue growth is a stronger indicator of company valuation than profitability (it’s typically 2-3x more important). Therefore this metric upweights revenue growth as part of the equation. The benchmarks here are different – typically a range of 70-130 is considered strong.
Nuances matter
It’s really important to be aware that these, mostly ‘traditional’, metrics that we have described can give biased results depending on your GTM strategy, and that averages themselves are blunt and can obfuscate from what’s really going on under the surface.
The metrics that will be of most value to you will depend on your GTM strategy. You will have realised that we in several instances have stated that certain metrics are of less importance, or need to be tweaked, for PLG strategies. You might also find these metrics less helpful if your GTM strategy is focused on enterprise sales with high levels of upsell, or community driven adoption, for example.
There is a story behind each number, so even though the data may look good at first glance, sharing the story behind them is equally important. Conversely, numbers that look less than ideal can also hide positives. In other words, adding context is of high importance. For instance, we’re increasingly finding that more thorough analysis and customer segmentation is needed for accurate and reliable performance insights.
FURTHER READING / ADVANCED SaaS METRICS / PHIL EDMONDSON-JONES
FURTHER READING / MAGIC NUMBER / NNAMDI IREGBULEM
FURTHER READING / RULE OF 40 / MIKAEL JOHNSSON
Why you should make cohort economics part of your standard reporting pack
The metrics above give a useful headline view, but no business is run on averages. There are always specific drivers behind the ups and downs. To both identify and manage them, you need to get granular. That’s where cohort analysis comes in. Creation of ‘cohorts’ facilitate analysis on the performance of the same group of clients over time. Cohorts are typically grouped according to some common attribute, for example a quarter that clients were acquired to allow comparison over time, or client characteristics (such as SMB/mid market/enterprise, or self-serve/assisted sale, etc) to allow cross-sectional comparison between distinctive groups.
Instead of averaging everyone into a single “blended truth”, you group customers by when (and who) you acquired, then track ARPA, retention, expansion, and payback for each group. That lets you answer the only questions that matter: are newer cohorts healthier than older ones; did the pricing or ICP change actually improve economics; and are fixes to onboarding/product compounding or just masking churn elsewhere. Report cohorts by quarter and segment/ICP, keep PLG and enterprise motions separate, and compare the last four cohorts to the prior four with a short narrative on what changed in GTM. Paired with your CARR→ARR→Revenue bridge, cohort views turn GTMF from a static snapshot into a running diagnostic you can manage.
Below we present three of our favourite analyses which can be particularly helpful when assessed on a cohort basis.
ARPA by Group ($)
Increasing ARPA can be a powerful driver of revenue growth and is particularly crucial for PLG businesses moving upmarket. It’s useful to see what’s driving ARPA growth. Looking at ASP vs churned ARPA (as well as ARPA of up and down-sold clients) is often illuminating.
Scenario A: If you start to see that your ASP is consistently higher than your ARPA and you’re consistently winning deals you’re likely naturally starting to move up-market. What does that mean? Well, to penetrate that segment further, consider shifting your next marketing hire to a B2B marketer with enterprise experience, your next sales hire to an Account Executive with experience selling to larger organisations, and being ready to incorporate typical enterprise demands (single sign-on, user permissions levels) into your product roadmap.
Scenario B: If your ARPA is quite variable among customers of similar characteristics, consider investing more into customer success to drive product adoption deeper in high-potential accounts that should be at a higher ARPA level. And don’t forget to continually re-examine your overall pricing structure to take into account the product improvements your team is continually shipping.
Example
In the example above, overall ARPA has more than doubled from $5k to $10k over 4 years. This has been driven by relentless increases in ASP for newly won clients, as this increases from $10k to $40k, whilst the business consistently churns clients with smaller ARPA.
MRR Drivers & Quick Ratio ($, #)
QUICK RATIO = (NEW MRR+EXPANSION MRR) / (CHURNED MRR + CONTRACTION MRR)
The Quick Ratio is measured as the total absolute Monthly Recurring Revenue (MRR) increase (new & upsell) as a proportion of MRR decreases (downsell & churn). The ratio is a measure of MRR growth and can be shown as a comparison to its components. You can use it for a fast read on whether growth gains outweigh revenue losses.
Quick ratios of 3 are respectable, and 5+ is better. The metric is a helpful indicator of not only MRR growth momentum, but also MRR growth quality.
Example
This business had a healthy quick ratio (yellow line, right axis) due to strong new MRR growth, which pared in Q5-6, as churn and downsell increased. Looking at this analysis for different groups of customers will give a clear view on the drivers of your MRR growth, and potential improvements.
The power of cohorts
In the example pictured, a software company could have high value enterprise customers with complex requirements who are supported with a more traditional outbound sales organisation; meanwhile high velocity, lower value, user driven adoption would come through a highly automated, PLG approach. The cohort behaviour and economics of these customer groups will clearly differ significantly, as will the team structures to serve these different customer groups. The average view doesn’t show this.
Splitting your customer base will allow you to see important nuances in behaviour and economics between the different groups. Calculating cumulative gross margin per cohort and comparing against that cohort’s acquisition costs allows you to be able to calculate actual realised CAC payback and lifetime values. This is the backwards-looking gold standard for how your business has performed historically. It can be hugely insightful for commercial planning decisions on how much to ramp up spend, as well as for financial planning and budgeting.
Bonus: Download Oxx’s SaaS metrics, unit economics and cohort analysis template
Through analysing the KPIs and metrics of (literally) thousands of SaaS businesses, we have developed a comprehensive template that we use ourselves, and which we have made available for download. This template only requires four data inputs to work: your MRR per client data, sales and marketing costs (or customer acquisition costs), gross margins and monthly burn. Inputting these four datasets allows you to immediately and automatically calculate a plethora of useful visualisations of your metrics, using the standardised off-the-shelf definitions.
The core analysis includes overall MRR/ARR growth, movements in client numbers and ARPA, underlying drivers of ARPA, quick ratios, core unit economics including CAC payback and LTV/CAC, magic number, as well as retention stats (gross and net), burn metrics, and a full suite of cohort analysis, including cohorted gross and net retention, and cohorted gross profit vs CAC.
Critically, the template also allows you to automatically split the entire customer base into two cohorts at a time (eg self-serve vs assisted, SMB vs enterprise, Europe vs North America etc.) and allows you to see almost all the analysis described in this section that’s not related to sales and marketing costs (as cohort attribution here is tricky to unpick).
Download Oxx’s SaaS metrics, unit economics
& cohort analysis template
FURTHER READING / GTM DASHBOARD / INSIGHT PARTNERS
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