Data and Direction

I’m a data fiend.

My guess is, you are too.

Many of the SaaS CEOs I speak with can’t get enough data, because more plus better data leads to better decisions, right?

Actually, not so much.

You probably don’t need more data. It’s even possible that, without knowing it, you’re drowning in data. Because many of the decision makers I meet…

Confuse data with measurement

Metrics are lagging indicators we use to inform future decisions.

So absolutely, we need data with which to measure, but the SaaS companies I’ve worked with and observed in the past, the ones that go off track, it’s rarely because they don’t have enough data.

It’s because they’re not tracking and measuring the right metrics and KPIs.

The whole purpose of working with metrics is to track the underlying fundamentals that drive performance or outcomes.

You track, and then act, according to the right metrics, you get desirable outcomes (growth).

If you get those fundamentals wrong, you get forecasting wrong, which means your reporting and dashboards aren’t meaningful.

And when this happens, I notice a common pattern.

A SaaS collects a lot of data, they don’t understand which metrics they should be tracking, they get overwhelmed, and when they’re overwhelmed, they choose the data that’s easy to measure or easily accessible rather than the right data.

But what’s wrong with that?

When you’re not directed by the fundamentals, you ask the wrong questions. Logical questions, but wrong all the same.

Here’s an example…

Let’s say you’re getting a lot of traffic to your website. Naturally, you want to understand how people are navigating your site, so you open up analytics hoping to answer the question: How are people using our website?

It’s the wrong question

To demonstrate, let’s go on the ‘Why’ journey:

Why does it matter what people do on your website?

Because then we can optimise different web pages or different content to increase the probability of getting a result. So with this question, your thinking is, if we understand how people are going through the website, we’ll know what to optimise.

But then reality hits you.

You start noticing people go to a page, then they go back to another page, and then they go forward, and then they go back, and then they go to a page you didn't expect.

Originally, you had this original hypothesis in your mind that visitors go to the homepage, click on the case studies, go to the product pages, and then go to book a demo.

The reality is, they go to the homepage, go to one of the product pages, then the blog, read two or three posts that seem completely unrelated to the product page they were on, and then disappear.

So now you’re thinking, ‘ok I wanted to understand how people use the website, and I have thousands of variable instances, but my visitor’s journeys are fragmented, chaotic, and complex. Do I have any more insight on how people are using our website?’

So, we ask the next why question…

Why do you want to optimise the pages?

Because you want to increase the likelihood of conversion.

What does that mean?

You want to get more people requesting a demo or joining for a free trial. More than that, you want the right people taking those actions, the people most likely to convert from free to paid.

So, why do you need to understand the customer journey?

You don’t, it’s largely irrelevant. A better question is:

What activities do we need to undertake to get the right visitors on our site and then convert to a lead?

And that’s when we realise that the data doesn’t give us direction.

The metrics do.

But only when we track meaningful metrics.

Because, even metrics that seem useful aren’t necessarily meaningful.

I’ve worked with a lot of LinkedIn ad agencies in the past and often they’ll report on the comparative ad click through rate between creatives.

And though that information seems useful, when think about it, you ask yourself, ‘So what?’

Sure, it’s good to know which creatives are performing better than others, but which creative is driving pipeline? Are they sending the right kinds of people to our website? Have they shown any intent to purchase?

If the answer is no to any of those questions, then the click through rate is irrelevant.

That type of metric is essentially shallow and superficial, but because it’s easily accessible and simple to measure, it gets tracked.

That’s what makes data and direction complex

It’s hard to understand what’s really impacting the customer journey. There’s so much overlap, so many touch points, many of which are hidden.

So we measure the easiest stuff, most of which isn’t particularly valuable.

That’s not to say it’s not possible to track the metrics that matter. They are out there, but there isn’t one metric that captures everything. What’s available is a combination of critical metrics that, when measured in cohesively, provide clarity on the activities that drive desirable and undesirable outcomes.

And the truth is, once you start tracking the right metrics, suddenly begin asking all the right questions, and when you ask the right questions, you take actions that directly impact the goal you’re trying to achieve.

As a CEO, there are only certain metrics and KPIs you need to be worrying about.

That’s what I’ll be sharing in this article. I’ll help direct your attention to the marketing related metrics that determine the success of your SaaS

Let’s get started!

Financial Modelling

These are the top-level, strategic metrics that feed into your Profit and Loss Statement. An aggregate of how marketing is impacting the overall business results. The kind of numbers your CFO is interested in…

Customer Acquisition Costs (CAC)

When to review: Monthly or Quarterly

A classic SaaS marketing Metric.

CAC is useful in the sense that when we know the number, we can ask ourselves questions like Is our CAC lower than Google Ads or LinkedIn ads?

Let’s say it’s lower with Google Ads, great! Let’s shift more spend from LinkedIn to Google.

That’s what makes CAC a good measure.

It’s also why it’s a bad measure.

Let’s say your CAC with Google Ads is lower. Are these better-quality customers? How many churn? What are they worth to the business?

CAC doesn’t answer those questions, it simply tells you the cost of acquiring any customer, which is why CAC by itself is quite useless, even when you’re running channel comparisons.

Therefore, we also track…

Lifetime Value (LTV)

When to review: Quarterly or Annually

Calculation: Use a predictive modelling tool

Another classic metric. LTV is an estimate of the overall value of a customer.

Between 2014 and 2020, the average CAC across SaaS was up by around 60%.

The rise in CAC means that, year on year, we need to increase bids just to maintain acquisition numbers, never mind increase them. So, if we want to generate growth, we need to increase LTV in order to reinvest sufficient spend in customer acquisition.

The challenge here comes in measuring and identifying an accurate LTV. Often, you need a significant amount of time to collect enough data to make accurate calculations.

If you’re an early stage B2B SaaS, that’s tricky because it’s likely your customers are on long contract periods, hopefully stay with you for years, and the LTV isn’t apparent until they churn.

A couple of ways around that are:

• Using Predictive Modelling: Instead of using an LTV calculation, use a machine learning model that you can train to predict customer behaviour based on early usage patterns.
• Measure in short intervals: Estimate LTV over shorter intervals (monthly or quarterly) to get approximate indications until long-term data is available.

So, we’re measuring CAC and LTV, But neither of these metrics are particularly useful in isolation, that’s why we track…

LTV:CAC Ratio

When to review: Quarterly or Annually

Benchmark: 3:1 = Good. 5:1 = Great

This is a way of measuring the return on investment of customer acquisition strategies.

LTV:CAC shows which segments and/or channels are most profitable and helps you assess efficiency.

Like, are you in the healthy 3:1 or better?

If you have excellent customer retention, you’ll have good LTV. Your CAC could be fixed, but if you improve retention and then enhance LTV by upselling, cross-selling, or just retaining customers for longer, then your CAC ratio will improve.

So you’ve got two levers. You can either acquire customers at a lower cost, reduce CAC, or improve the LTV, which improves the ratio.

Now, whilst these metrics will indicate whether you’re on course to grow, they won’t show you whether that growth is sustainable. This will…

Payback

When to review: Quarterly or Annually

Benchmark: 18 months = Good. <12 months = Great

The faster we get our investment back, the more we can allocate for growth.

That’s the importance of the Payback metric.

If a customer costs $100 in acquisition, and they spend that with you on day one, you can take that $100 immediately and reinvest it back into marketing to get another customer, which directly relates to your growth rate.

But that’s not the only reason this is such a critical metric.

Payback period is also important for speed of learning.

It’s hard to determine if a particular marketing activity of acquiring new customers represents a smart investment if the time horizon for payback is too far into the future.

It’s high risk, for example, if it takes you three years to payback the acquisition of a customer, because that’s a very long time period for which you need to retain them.

Secondly, we need to wait three years to learn whether that channel actually represents a good or bad investment.

Thirdly, it gives us deeper insight into our acquisition channels. If the LTV:CAC ratio is 3:1 for both Google Ads and Facebook Ads, but the payback window for Facebook Ads is six months shorter, where are we shifting our budget?

It’s also arguably more valuable than LTV:CAC because you need a lot of data to get an accurate LTV, technically, years because you need to wait until the customer has churned to know what her lifetime value is, and that’s why LTV:CAC in isolation is problematic.

Further reading: If you want more, check out this article from Chargebee.

And finally, for Financial Modelling, we want to know whether any growth determined from the previous metrics will likely continue. We get that from…

Net Promoter Score (NPS)

When to review: Quarterly or Annually

NPS is a loyalty metric, and that loyalty directly impacts financial modelling. It indicates potential future revenue because if a customer is happy, they renew (and maybe refer).

There are a few different ways to calculate NPS, but I like to keep it simple:

Of course, like every single metric I share in this list, there are shortcomings with NPS:

• It’s not tied to revenue.
• It doesn’t give us any concrete definition of what people like or why
• It’s backward-looking. It’s not telling us whether people will continue to be happy or whether the changes we make will improve their satisfaction.

But for me, the positives outweigh the negatives.

Research shows that there’s a strong correlation between a company’s NPS score and future business growth. It’s a good indicator of our trajectory regards customer retention, upselling, and cross-selling.

I won’t go into further detail here. It’s a subject in and of its own right, but if you want further reading, I recommend The Ultimate Question 2.0.

One other point to make: I’ve not given benchmarks for NPS as they’re very much industry dependant, and the range is high, but check this article for more on SaaS NPS benchmarks.

Forecasting

Forecasting is about trying to get predictability in your pipeline. We know the revenue you require to achieve the desired growth. Forecasting metrics help us determine whether we’ll hit those targets, and if not, where we’re going wrong.

Monthly Recurring Revenue (MRR)

When to review: Monthly or Quarterly

MRR gives you a clear picture of business growth trajectory by tracking the month-over-month growth in recurring revenue. It's a number that gives you a top-level picture.

But, in isolation, a lot of data is hidden.

Imagine a SaaS company with the following scenario:

• Month 1: The company has 100 customers, each paying $50 monthly. The total MRR is $5,000.
• Month 2: The company gains 20 new customers at $50 per month, but ten existing customers churn (cancel their subscription). The total MRR is now $5,500 (110 customers * $50).

Looking at the MRR alone, the company has grown by $500, or 10%, which seems positive. But this only tells part of the story.

Amongst other things, it doesn't tell us that the company lost 10% of existing customers (which may mean that we have an issue with customer satisfaction).

It doesn't tell us the value of the customers who left - were they previously loyal, long-term customers? It doesn't tell us the value of the customers who joined - did they join low-value or high-value plans?

So, we also need to track…

Churn Rate

When to review: Monthly or Quarterly

Arguably, one of the most important metrics for a SaaS is the percentage of customers or subscribers who cancel or choose not to renew their subscriptions during a given period.

Predicting churn is essential because if you need to hit revenue and know what churn is, you know how many net new customers you need to bring in to meet your financial goals.

Traditionally, there are two ways to calculate churn. Dale W. Harrison refers to them as Logo Churn vs Net Revenue Retention (NRR).

Logo Churn → Looks at the customers lost over a given period.

For example, if a company had 1,000 customers at the start of a month and lost 50 customers, the logo churn rate would be 50/1,000 = 5%.

NRR Churn → Looks at the changing revenue (expansion or contraction).

I'm not a big fan of NRR churn because it doesn't really highlight true churn.

You could run the calculation and find that you have low churn, but what actually happened was that you lost a bunch of customers, picked up a couple of large ones (plus upsells), and that papered over the cracks. So you think churn is reasonably low because our NRR calculation tells us so, but when you look at it, you lost a lot of user. It's just that the revenue growth rate was higher than the loss of customers.

Sure, we want to attract and retain the highest-value customers, but that's not what we use the churn calculation for.

When we measure churn, we really want to know the true expansion or retraction of customer numbers, and that's measured by logo churn.

And, rather than using the standard Logo Churn calculation I referenced above, there's a more sophisticated and helpful approach.

Instead of measuring the customer base as a whole, we put them into buckets that you plot over time.

You take every customer you ever acquired and split them out into segmented cohorts (acquisition channel, customer type, etc.). Then, you bundle them into buckets according to the total number of weeks they were active customers.

Again, referencing Dale W. Harrison, this is called cohort-based survival analysis.

These buckets are customer' survival periods' - how long they stuck around as a customer.

Then, we work out the percentage of lost customers during each' survival period.'

Here's an example of a fictional SaaS business aimed at the construction design market where we break out cohorts by two different customer segments:

Architects and Interior Designers.

Architects

Interior Designers

In this example, we can see exactly how many accounts were lost in each period, and because we’ve clustered by customer segments, we get a sense of underlying issues.

We can see that both have a similar onboarding churn.

For architects, that churn rate calms after the initial onboarding. That’s what we’d expect to see for a healthy SaaS product.

But, with interior designers, that loss number doesn’t level out. We continue to lose large chunks of people within the first 90 days. That indicates a problem. Maybe the product is missing features for the interior designers? Maybe we should stop targeting interior designers and go all in on architects.

Either way, that’s insight we wouldn’t have if we had lumped everyone into the same churn rate calculation and then not broken that calculation into survival periods.

Further reading - If you want more, check out this article from Dale W. Harrison.

Another point: I’ve not given benchmarks for Churn Rates because they’re very industry-dependent. Good is often in the range of 3% to 8%, but for more on benchmarking, here’s more from Paddle.

Now, as our chosen churn calculation isn’t going to take into account the revenue resulting from an increase or decrease in customer base, we need to find that measurement elsewhere…

Net Dollar Retention (NDR)

When to review: Quarterly or Annually

Benchmark: 105% = Good. 120%+ = Great

Churn rate doesn’t always give the full picture because it typically only shows the rate of customers cancelling (or not renewing).

This is where NDR is helpful because it considers revenue retention and growth. It measures the percentage of recurring revenue retained from existing customers over a specific period. That’s critical because it gives a clear picture of growth, including upgrades, downgrades, and churn.

So, let’s say our SaaS has a 10% revenue churn from customers leaving, but the 90% remaining customers, on average, increase their spend by 20% through upsells and upgrades.

That’d make our company’s NDR 110%, which is good.

Anything over 100% means upgrades outweigh downgrades and churn (in relation to revenue.)

Average NDRs are 104%, but obviously, the higher we get that number, the more we have to reinvest into marketing, the faster our growth.

But, whilst retention is important, we still need to go out and get new customers…

Lead Conversion Rate

When to review: Monthly or Quarterly

You’re likely familiar with this one. Lead to MQL to SQL to Opportunity to Deal Won/lost. A lot of marketing is focused is on this, and it’s important because if any of the conversion rates drop here, the impact is major.

The categories of lead and progress may differ slightly from SaaS to SaaS, but the model remains the same. The lead conversion rate gives us a top-level insight into any weak points we need to work on in our conversion process.

We take this information to figure out whether we’ll hit sales targets…

Sales Pipeline Coverage

When to review: Weekly or Monthly

Benchmark: 3:1 = Good 5:1 = Great

Sales pipeline coverage is a metric that compares the total potential value of all open sales opportunities in your pipeline against your revenue targets or sales quotas for a given period. It essentially indicates whether you have enough prospective deals in your pipeline to hit the desired sales goals.

It’s a critical metric because you won't hit those sales targets if you don’t have enough opportunities in your pipeline. It all feeds in from the different measures in the lead conversion rate (leads, MQLs, etc.). If you get enough of those, you should theoretically hit your targets based on conversion rates.

In the example above, based on the funnel maths, you end up with three closed deals per 1,000 uniques. But just looking at the tail-end, the number of opportunities and opportunity-to-close rate only tell part of the story. If you need six new customers this quarter and you have eight opportunities, you might think you're covered. But if you need a minimum of 12 opportunities to yield six customers, you're actually short.

That's the concept of pipeline coverage.

It's about having enough flow and coverage higher up in the funnel to predictably generate the required number of closed deals based on your conversion metrics. That's why the coverage ratio is vital - it shows if you have enough juice in the pipeline to hit the number by comparing your actual open pipeline value to the revenue target.

But, whilst we need to get the appropriate number of sales, speed is everything here, so we need to track…

Sales Velocity

When to review: Weekly or Monthly

Benchmark: 4 to 6 months = Good. 3 months or less = Great (although this is dependent on other factors)

This is really only relevant for businesses with a sales touch point (i.e. not PLG).

Sales Velocity is a simple metric that tells us the speed at which a SaaS is generating sales from its pipeline. It's important because it provides insight into the momentum and efficiency of the sales engine.

When you think about it, each of the previous metrics in forecasting all boil down to this principle:

The more opportunities you have with progressively higher value, progressively better win rates, and progressively shorter sales cycles, the more revenue you generate at a faster rate.

The pace of revenue generation is very material for your business's growth rate. But why is speed so important?

Let's think about Uber.

When Uber came out, how many Uber's were there? As far as the market was concerned, none.

Fast forward four years, here’s what the market looks like:

Now you have this saturation of competitors, differentiation becomes watered down, and all your metrics begin to drop.

Reducing CAC is harder, your payback window is longer, and your profitability takes a hit. Your business is at risk…

That’s why speed is so critical

This is particularly relevant in Venture Capital-backed businesses. But, even bootstrapped businesses want to grow faster, although they’re more focused on profitability than market share and growth rate.

VC-backed businesses are concerned with economies of scale and market share size because they need to exit at a minimum of £100m to recoup their investment, and you can’t get that kind of exit without substantial market share. Which is why…

The valuation multiple of a SaaS is fundamentally determined by growth velocity.

Here’s a handful of examples to demonstrate that point:

Scenario One: A High-Growth SaaS Company

• Annual Recurring Revenue (ARR): $20 million
• Year-over-year growth rate: 50%
• Revenue multiple range for high-growth SaaS: 10x to 15x
• Calculated Valuation: $200 million to $300 million

The company here commands a high multiple due to its rapid growth rate, which signals the potential for significant expansion and market capture.

Scenario Two: A Mature, Steady Growth SaaS Company

• Annual Recurring Revenue (ARR): $20 million
• Year-over-year growth rate: 50%
• Revenue multiple range for high-growth SaaS: 6x to 8x
• Calculated Valuation: $120 million to $160 million

This company has the same ARR as scenario one, but because the growth rate isn’t as explosive, the Valuation is potentially half that of a high-growth SaaS.

Scenario Three: An Early-Stage, Pre-Profit SaaS Company

• Monthly Recurring Revenue (MRR): $500,000 (which translates to $6 million ARR)
• Year-over-year growth rate: 80% (very high due to early stage)
• Revenue multiple range for high-growth SaaS: 8x to 12x
• Calculated Valuation: $48 million to $72 million

Even though it’s pre-profit, the high growth rate alone justifies a relatively high multiple due to the future revenue potential assumed by investors.

That’s why more or less every metric in here links back in some way to measuring and impacting fast growth.

Three more metrics and KPIs you should be tracking

The following metrics don’t fit into either of the top-level categories (financial modelling or forecasting), but I believe every CEO should have an eye on them.

I’ve already mentioned lead conversion rates in the forecasting section, but for a SaaS business, it’s worth digging deeper into arguably the most important section of that flow…

Free-to-paid conversion rate

When to review: Weekly or Monthly

“Your free-to-paid conversion is the percentage of new accounts who end up paying for the product in the first 6 months.” - Kyle Poyar

The value of this metric is that it directly impacts growth without the need to invest in additional headcount or significant marketing spend. Instead, you can increase it by improving onboarding, sales process, etc.

What ‘good’ looks like depends on your free-to-paid model:

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The best practice here is to approach it as we did with churn, on a cohort basis, month by month. That way, we can determine whether we’re improving conversion. That’d look something like this:

All of this then feeds into our marketing decisions.

Which channel, for example, delivered free sign-ups with a better conversion rate? Facebook or Google Ads?

Also, if you make changes to the funnel, do conversion rates go up or down? You want them to go up, but you also want to experiment to find additional improvements.

Further reading - If you want more, check out this from Lenny Ratchitsky.

User Retention Rate

When to review: Monthly or Quarterly

Benchmark

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If monetisation is determined by the Lifetime Value of a customer multiplied by the number of customers, the retention rate is a critical measurement in our business operations. The longer we retain a customer, the higher their lifetime value.

“Retention is not only the primary measure of product value and product/market fit for most businesses; it is also the biggest driver of monetisation and acquisition as well.” Casey Winters, former head of growth at Pinterest and Grubhub, and now CPO at Eventbrite

Through that lens, this metric clearly reflects customer satisfaction and product value.

Calculation:

User Retention: % of new users who are still active 3-6 months later

Logo Retention: % % of new companies who are still active 3-6 months later

Average revenue per account (ARPA)

When to review: Monthly or Quarterly

If we continue with this line of thinking around monetisation as determined by lifetime value, a logical approach to increasing monetisation is upselling and cross-selling.

A simple way of tracking all of the above is through Average Revenue per Account.

What's the average account worth to you, and is that number going up month by month?

This simple metric, my friend, is the final in my list of the metrics and KPIs that you, as a SaaS CEO, should track to drive growth and success in your business.

Like I said at the start, I'm a data fiend.

Without restraints and boundaries, I get just as lost in the weeds as any other data-hungry professional.

This list helps me keep track of the numbers that actually matter.

It helps me develop a clear picture of a client's financial health and make informed marketing decisions about where to allocate resources for maximum impact.

By tracking and acting on these metrics, you'll be able to ask the right questions, make data-driven decisions, and ultimately achieve the growth and success you're after.

So, my challenge to you is this: take a hard look at the metrics you're currently tracking. Are they the ones that truly matter for your business? If not, it's time to make a change. Start focusing on the metrics we've discussed today, and watch how they transform the clarity of the decisions you make (and consequently drive growth).