How public SaaS companies report churn, and what you can learn from them

While doing some research for another post I just stumbled on this excellent overview from Pacific Crest on the churn rates of publicly listed SaaS companies. I’ve seen posts with churn benchmarks of public SaaS companies before, but this one is by far the most comprehensive collection I’ve seen and I think it’s very useful.

What’s maybe even more interesting than taking a look at the numbers themselves is to see how different companies define churn (or the inverse, retention). Since there is no official US-GAAP definition of churn or retention, different companies use different ways to measure and report these metrics. And because public companies are under the scrutiny by the SEC, any non-GAAP metric they report must be accompanied by a razor-sharp definition.

Most public SaaS companies report churn in the form of their dollar-based net retention rate, i.e. the inverse of net MRR/ARR churn (as opposed to account/logo churn), which compares the recurring revenue from a set of customers across comparable periods. Here’s a particularly nice description of this metric, coming from AppDynamics:

“To calculate our dollar-based net retention rate for a particular trailing 12-month period, we first establish the recurring contract value for the previous trailing 12-month period. This effectively represents recurring dollars that we should expect in the current trailing 12-month period from the cohort of customers from the previous trailing 12-month period without any expansion or contraction. We subsequently measure the recurring contract value in the current trailing 12-month period from the cohort of customers from the previous trailing 12-month period. Dollar-based net retention rate is then calculated by dividing the aggregate recurring contract value in the current trailing 12-month period by the previous trailing 12-month period.”

If you take a look at the data assembled by Pacific Crest you’ll see that many companies use the same logic with minor variations. For example, some companies look at the trailing 12 month period, while others look at calendar years, quarters, or months.

Some companies exclude customers that do not meet certain criteria, for example:

• Box includes only customers with $5k+ ACV and annual contracts
• Alteryx considers only customers which have been paying customers for at least one quarter.
• AppDynamics includes only customers who have been paying customers for at least one year.
• Zendesk excludes customers on the starter plan.

This makes perfect sense: It tells you what type of customer the company is focused on, and you can see the retention metrics in regards to this type of customer.

Other companies use variations that I think are questionable. Some companies report customer count-based retention, which I think is much less interesting than dollar-based retention. Some report renewal based on the number of seats; one company, Fleetmatics, reports churn based on the number of vehicles under subscription. But the majority of companies does report dollar-based net retention rate in a way that allows for an apples-to-apples comparison across companies.

What can you learn from this?

(1) There is not one perfect definition of churn that is right for every SaaS company. Depending on the specifics of your business you might want to:

• focus on monthly, quarterly or annual retention
• exclude customers that churned within the first, say, two months
• include only customers that represent the core of your business, e.g. customers above a certain ACV

(2) Having said that, dollar-based net retention is the way to go. You should stay close to the definition above and tweak it with care.

(3) There may not be one perfect way to define and measure churn, but there sure are lots of ways to get it wrong. :) One classic example is to calculate a monthly churn rate and to mix in annual plans with monthly plans. By including customers on annual plans who aren’t up for renewal in the period you’re measuring you’re underestimating your true churn rate.

(4) Whatever metric you choose, make sure that you use it consistently and that you have a razor-sharp definition.

Bonus tip: Whenever you report numbers, be it in monthly updates or in a Board deck, include footnotes or an appendix with definitions of every metric that you’re reporting. I can almost guarantee you that this will save you ten minutes of discussion with your VC Board member(s) who (understandably) want to make sure that they understand the numbers you’re showing them. :)

Update / September 17, 2019: Another bonus tip, we recently invested in a company called Brightback that helps you reduce churn by making it easy to implement sophisticated, personalized "churn deflection" pages and workflows. Have a look! :)

What does it take to raise capital, in SaaS, in 2016?

When we invest in a SaaS startup, which almost always happens at the seed stage, the next big milestone on the company’s roadmap is usually a Series A. If you carry this thought further and assume that the biggest goal after the Series A is to get to the Series B (and so on, you get the idea) it sounds like turtles all the way down. But financing rounds are obviously not a goal in itself. They are a means to a bigger goal. Some SaaS companies got big without raising a lot of capital – Atlassian, Basecamp and Veeva are probably the most famous examples. But they are exceptions, not the rule. According to this analysis of Tomasz Tunguz, the median SaaS company raises $88M before IPO.

So what does it take to raise money for a SaaS company in 2016? With constantly rising table stakes and a fundraising environment that looks quite a bit less favorable than last year’s, I believe the bar is higher than in the last 18-24 months (although raising money is still much easier than it was in “Silicon Valley’s nuclear winter” in 2008).

Below is my back of a (slightly bigger) napkin answer to this question.

A few important notes:

• The assumption of the information in the table is that the founding team is relatively “unproven”. Founding teams with previous large exits under their belts can raise large seed rounds at very high valuations on the back of their track records and a Powerpoint Keynote presentation.
• Some of the information is tailored to enterprise-y SaaS companies. If you have a viral product (like Typeform or infogram), some of the “rules” don’t apply.
• If you have virality and a proven founder team, you’re Slack and no rules whatsoever apply. :)

(click here for a larger version)

PS: Thanks to Jason M. Lemkin, Tomasz Tunguz, Nicolas Wittenborn and my colleagues at Point Nine for reviewing a draft of this post!

[Update 1: Here's a mobile-friendly version of the napkin.]

[Update 2: And here is a Google Sheet version for better readability. :) ]

Introducing: The One-Slide Update Deck

When we start to work with a new portfolio company, one of the things we always suggest is that in addition to (sometimes lots of) ad hoc communication via eMail, Skype, Basecamp, etc. we set up a standing meeting or call, at least during the first 9-12 months following our investment. Typically it's a one-hour monthly call, and the purpose of these calls is to get us updated and to talk through current issues. Our experience is that these calls are a very effective and efficient way to discuss things and to find out how we can help. The last thing we want to do is be a burden on the founders, and so we try to be very respectful of the their time (even if we're not as efficient as Oliver Samwer with his famous "supercalls" - 12 hours, 180 companies, or something like that).

Just like a regular Board Meeting, these monthly calls work best if the investors get an update before the call, so that the call can be spent discussing key challenges rather than spending too much time going through numbers and updates. And that brings me to the topic of this post: The One-Slide Update Deck.

Founders often ask me if I have a preferred format for updates and KPIs. And while I can point them to my SaaS metrics dashboard for KPIs, we've never had something like a template for other updates. So here's my attempt to create a super-simple deck which you can use to update your investors (or me!):

The idea is that in the beginning you create a rough roadmap for the next 12 months, broken down into key areas like Product & Tech, Sales & Marketing and Team/Hiring (see slide 1), plus a financial plan. Better yet, you already have a plan :-) and you discuss that with your investors to get everyone on the same page.

Then, every month you create one slide which shows progress and problems, as well as the original plan, in each of the three key areas, plus key metrics. I've borrowed the "Progress, plans, problems" technique from Seedcamp; the metrics are taken from my own SaaS dashboard template. So just one slide, once a month, with information you should already have anyway, and you should have a great basis for highly productive calls or meetings with your investors.

It obviously doesn't matter if you use Keynote, Google Docs or something else, and depending on the needs of your company you may want to emphasize different key areas or include other KPIs. So this isn't meant to be prescriptive but rather a suggestion or a starting point for founders who are thinking about reporting for the first time – if you are already providing more comprehensive monthly reports, don't change it!

If you want to take a closer look, here is a PDF and here is the original Keynote version.

Thanks to Nicolas, Rodrigo and Michael for providing valuable feedback on the draft of the slides!

When deers morph into elephants, SaaS nirvana is nigh

By now you’re probably sick of my infamous animal analogies. Sorry. But I just love them and want to resort to them one more time. :) Namely, what I want to talk about are deers that can morph into elephants, or more generally, smaller animals that can morph into bigger animals. ⁽¹⁾ In other words, I want to talk about account expansions, which are the result of a successful “land and expand” strategy.

The premise of this strategy is that it’s usually easier to get a minor commitment from a customer first and then work your way up towards a larger ACV, rather than trying to get a large deal from the get-go. There are different ways how SaaS companies have successfully employed land-and-expand strategies:
 

• Yammer is a classic example. Typically a small team in a company starts to use Yammer for internal communication. Then they add more and more people, usage might spills over to other teams or departments, and eventually Yammer’s sales team can come in and upsell the customer to an enterprise account. It’s hard to imagine a hotter, more qualified lead than a company where dozens or hundreds of people are using your product already!
• Dropbox is similar, but the difference is that you can start using Dropbox even as single user. Plus, they have another great growth vector, since people keep adding more and more files to their file storage.
• EchoSign: In this Quora post, EchoSign founder Jason M. Lemkin (one of the top SaaS experts and our co-investor in Algolia and Front) describes how EchoSign grew many departmental deployments into large, six-figure accounts over time (he also gives you the caveats).

Another way to get bigger and bigger accounts over time is of course to target startups and grow with your customers. Zendesk is extremely successful at employing land-and-expand strategies, but the company has also been fortunate enough to acquire customers such as Twitter, Uber and many others when they were still pretty small.

If your land-and-expand strategy works so well that your account expansions offset churn, then your MRR churn rate becomes negative – a state which I’ve previously described as the holy grail of SaaS. It’s hard to overstate how transformative this can be to a SaaS company. Think about it: Negative MRR churn means that even if you’re not growing, you’re still growing. More precisely, even if you stopped acquiring new customers tomorrow your recurring revenue would still continue to grow.

It’s no surprise that SaaS investors start to salivate when they see SaaS companies with negative MRR churn. Just a few days ago, Tomasz Tunguz of Redpoint highlighted that New Relic, which has filed to go public, has a negative MRR churn rate of about 14% per year. Especially for later-stage public SaaS companies, revenue churn is one of the most important metrics to look at. You cannot understand a company like Box, which is spending seemingly crazy amounts of money on customer acquisition, without understanding this metric. ⁽²⁾

---

⁽¹⁾ If you have no idea what I'm talking about, please read this post.
⁽²⁾ And yet, I have the impression that this metric hasn’t fully arrived in the world of financial analysts and accountants yet. There doesn’t yet seem to be a standard way of reporting it – every company defines the metric a little different, and some aren’t reporting it at all.

Benchmarking your SaaS startup

People often ask me questions like:

• "How many people can I expect to sign up on my SaaS website?"
• "My conversion rate is x% – is that good or bad?"
• "My churn rate is x% – is that OK?"
• "What kind of growth rates are VCs looking for?"

While we have quite a lot of data from our SaaS portfolio companies and from SaaS startups pitching to us (which I'll be happy to share, in aggregated form, in another post), I thought it would be good to increase our sample size by asking a larger number of SaaS startups to provide us with some key metrics:

SaaS metrics benchmarking survey

If you're a SaaS startup I'd love you to participate in the survey. I kept it as short and simple as possible, focusing on three of the most important metrics for early-stage SaaS startups:

1. Visitor-to-trial signup rate
2. Signup-to-paying conversion rate
3. Account churn rate

As soon as I have a meaningful number of submissions I'll share the results (in aggregated form) with the participants and will also publish them here.

Thanks in advance to all participants!

Learning More About That Other Half: The Case for Cohort Analysis and Multi-Touch Attribution Analysis (Part 1 of 2)

Note: This article first appeared as a guest post on the popular KISSmetrics blog. Thanks to Hiten Shah and Sean Work at KISSmetrics for publishing it. I'm republishing the post here as a series of two shorter posts, with a few small edits.

Anyone who has ever worked in marketing or advertising has heard the quote, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” It is from John Wanamaker and dates back to the 19th century.

Fortunately, the industry has come a long way since then, and especially in the last 10 to 20 years, new technologies have made advertising more measurable than ever. However, there’s still a considerable gap between what people could measure and what they actually are measuring, and that leads to significant under-optimization of advertising and marketing dollars.

In B2B SaaS, which we at Point Nine Capital focus a lot of our efforts on, there are two techniques that I feel are particularly important but not used widely enough – cohort analysis and multi-touch attribution analysis. In this series of posts, I’ll try to provide a brief introduction to both methodologies and explain why I think they are so important.

A Quick Primer about Cohort Analysis

If you're a reader of this blog or know me a bit, you know that I'm a huge fan of cohort analysis and have written about the topic before. If you’re new to the topic, a cohort analysis can be broadly defined as a dissection of the activities of a group of people (such as users or customers), who share a common characteristic, over time. In SaaS, the most frequently used common characteristic for grouping customers is “join date”; that is, people who signed up or became paying customers in the same period of time (such as a month).

Let’s look at an example, and it will become much clearer:

In this cohort analysis, each row represents all signups that converted to become paying customers in a given month. Each column represents a month in your customer’s life. The cells show the percentage of retained customers of the respective cohort in the respective “lifetime month.”

So What?

Why is it so important to do a cohort analysis when looking at usage metrics or retention and churn? The answer is that if you look at only the overall numbers, such as your overall churn in a calendar month, the number will be a blend of the churn rate of older and newer customers, which can lead to erroneous conclusions.

For example, let’s consider a SaaS business with very high churn in the first few lifetime months and much lower churn from older customers – not unusual in SaaS. If the company starts to grow faster, the blended churn rate will go up, simply because the percentage of newer customers out of all customers will grow. So, if they look at only the blended churn rate, they might start to panic. They would have to do a cohort analysis to see what’s really going on.

What else can you see in a cohort analysis? Whatever the key metrics are in your particular business, a cohort analysis lets you see how those metrics develop over the customer lifetime as well as over what might be called product lifetime:

If you read the chart above (which I've borrowed from my colleague Nicolas) horizontally, you can see how your retention develops over the customer lifetime, presumably something that you can link to the quality of your product, operations, and customer support. Reading it vertically shows you the retention at a given lifetime month for different customer cohorts. This might be called product lifetime, an, especially if you look at early lifetime months, it can be linked to the quality of your onboarding experience and the performance of your customer success team.

The Holy Grail of SaaS!

Maybe most importantly, a cohort analysis is the best way to estimate CLT (customer lifetime) and CLTV (customer lifetime value), which informs your decision on how much you can spend to acquire a new customer. As mentioned above, churn usually isn’t distributed linearly over the customer lifetime, so calculating it based on the blended churn rate of the last month doesn’t give you the best estimate. A better way is shown in the second tab of this spreadsheet, where I calculated/estimated the CLT of different cohorts.

A cohort analysis is even more essential when it comes to CLTV. Looking at how revenues of customer cohorts develop over time lets you see the impact of churn, downgrades/contractions, and upgrades/expansions:

This chart shows a cohort analysis of MRR (monthly recurring revenue) of a fictional SaaS business. As you can see in the green cells, it’s a happy fictional SaaS business as it has recently started to enjoy negative churn, which many regard as the holy grail in SaaS.

Still not convinced that you need cohort analyses to understand your SaaS business? :-) Let me know in the comments.

Three more ways to look at cohort data

I've just added three new charts to my Excel template for cohort analysis.

The first one shows the MRR development of several customer cohorts over the cohorts' lifetime:



Each of the green lines represents a customer cohort. The x-axis shows the "lifetime month", so the dot at the end of the line at the bottom right, for example, represents the MRR of the January 2013 customer cohort (all customers who converted in January 2013) in their 9th month after converting.

Here are some of the things that you can see in this chart:

The second chart is based on exactly the same data but shows MRR for calendar months as opposed to cohort lifetime months, and it uses a slightly different visualization:

One of the things you can see here is the contribution of older cohorts to your current MRR (something to keep in mind if you're considering a price increase and are thinking about the impact of grandfathering):

The third chart shows cumulated revenues minus CACs for different customer cohorts, i.e. it shows how much revenues a customer cohort has generated less the costs that it took to acquire the cohort:

The purpose of this one is to show if you're getting better or worse with respect to one of the most important SaaS metrics: The CAC payback time, i.e. the time it takes until a customer becomes profitable. Note that for simplicity reasons the chart is based on revenues. If you use it in real life, it should be based on gross profits, i.e. revenues minus CoGS.

What you can see here is that the first cohorts cross the x-axis (a.k.a. become profitable) around the 6th lifetime month, whereas newer cohorts are crossing or can be expected to cross the x-axis further to the left, i.e. become profitable faster.

If you want to take a closer look, here's the latest version of the Excel template, which includes the new charts. Or even better, download it and pay with a tweet! :)

The 8th DO for SaaS startups - Stay on top of your KPIs

“What gets measured gets done” – it seems like the source of this quote, often attributed to management expert Peter Drucker, isn’t certain, but its meaning is clear and very relevant for every SaaS founder. If you want to make sure that you make best use of your scarce resources, you need to have a clear understanding of your objectives and the KPIs that measure your progress towards those objectives.

Depending on the stage that you’re in you’ll want to focus on different metrics. I’ve tried to illustrate this in the following diagram:

(click image for larger version)

As you can see, I segmented the company lifecycle into three major phases: pre product/market fit, post product/market fit but pre-scale, and post-scale (being fully aware that there is no distinct definition of “product/market fit” and “scale” and that the transition from one phase to the next one is a gradual one). At the bottom I noted what these phases usually mean in terms of the stage of your product and company and which funding level it typically corresponds with. Note that the x-axis is not a true-to-scale representation of time elapsed. For a true-to-scale representation I would have to add much more space between the Series A and the Series B and between the Series B and the Series C.

The key message of the chart is that in the beginning you can focus on a small set of metrics, but as time goes by and you’re making progress you need to add additional KPIs to your cockpit.

Let’s have a closer look at each of the three phases.

Pre product/market fit

I’ve written about it before in my posts about sales and unscalable hacks: In the very beginning, when you’re in the process of finishing the first version of your product and trying to get the first customers, you shouldn’t worry too much about metrics. Firstly there just aren’t many metrics to keep an eye on yet. Secondly you should be obsessively focused on getting to product/market fit (Marc Andreessen’s words), and that means you should spend your time talking to customers and developing the product.

That said, the following metrics are relevant in the pre product/market fit phase:

• User feedback: Most of the user feedback that you collect in this phase is qualitative rather than quantitative, but if you talk to a larger number of potential users you might also be able to add some quantitative elements. For example, you could ask users to rate your prototype and see if that rating goes up over time.
• Development velocity: I don’t know if (or how strictly) you should use a software development methodology like Scrum, which allows you to nicely visualize your development velocity, in the very early days, when you’re maybe just two developers – I would be very interested in your thoughts on that question. At any rate, however, I think it’s a good idea to break down your project into a larger number of smaller pieces, features or “story points” early on. This will help you in getting an understanding of your development speed, which later on will become more and more important.
• Waiting list signups: When you put up a landing page to collect email addresses for your waiting list, track how many signups you’re getting. Driving signups probably isn’t a key priority for you at this stage but it’s an indication of interest in your product and hey, you’ll still have some space on your Geckoboard which you can fill with a nice chart! :)

Once you let potential customers try your product, the real fun begins. At that point, you should track signups and some indicators for activation and usage, which, for obvious reasons, are precursors to your ultimate goal, paying customers. What the right indicators for activation are depends on the type of your product. It could be a profile completion and the setup of a customized pipeline in case of a CRM application, the installation of a tracking snippet for a Web analytics product or… you get the idea. Similarly, usage metrics are highly specific to your application, so think about what the right events and parameters are in your particular case and make sure that you instrument your application accordingly. If your solution is a little more enterprisey and you’re working with a higher-touch sales model you may also want to track qualified leads along with trial signups.

In order to succeed you need happy customers who do free marketing for you, otherwise customer acquisition will always be an uphill battle. Therefore you should also consider regular Net Promoter Score (NPS) surveys. If you’re looking for the best survey tool, I have a tip for you.

Post product/market fit, pre scale

As you’re slowly but surely getting to product/market fit and starting to get the first paying customers (yay!), your trial-to-paid conversion rate becomes one of the most vital metrics. It’s hard to give you a benchmark, since your conversion rate not only depends on the quality of your product and the onboarding experience but also on many other things such as leads quality, pricing and many other factors. With that caveat in mind, the typical range that we’re seeing is between 5% and 25%.

Equally important is your retention, usually tracked by measuring churn (the inverse of retention), since your CLTV (customer lifetime value) is a direct function of how much you charge your customers and how long they stay on board. As a very rough rule of thumb you should try to get your churn rate to 1.5-3% per month.

Make sure to track churn not only on an account basis but also on an MRR basis. Your MRR-based churn rate will hopefully be significantly lower than your account-based churn rate, since smaller customers tend to have a higher churn rate and because your loyal customers will hopefully pay you more and more over time. Also, make sure that you avoid SaaS Metrics Worst Practice #8, mixing up monthly and yearly plans. Finally, if you want to get a good estimate of your customer lifetime, take a look at retention on a cohort basis.

If you don’t have a KPI dashboard yet that gives you an at-a-glance look at your key metrics, now is the time to build one. Here’s a template that I’ve created, along with some additional notes.

As you’re moving on, arguably the most important metric becomes MRR, and specifically net new MRR that you’re adding each month. Net new MRR is calculated using this simple formula:

Also keep an eye on your ARPA (average revenue per account). It’s an important metric at all times for obvious reasons, but as you’re nearing the next phase it’s becoming even more important.

Post scale

When you’ve reached a certain level of success, say you’re at around $500k MRR, the biggest challenge (besides growing a bigger organization and mastering all kinds of growing pains of course) is to find ways to profitably acquire customers at a much higher scale. By this time you’ve picked all the low-hanging fruits, and you may have maxed out what you can reasonably spend on AdWords to buy traffic and leads.

Therefore you’ll have to focus on the relationship between your CLTV and your CACs (customer acquisition costs), your CLTV/CAC ratio, which measures the ROI on your sales and marketing investments. Another way to look at it is your CACs payback time, which tells you how many months of subscription revenue it takes to recoup customer acquisition costs. If I had to choose I’d pick this one, since CLTV is always an estimate which can be more or less accurate.

A few last points:

• Many startups struggle to get all these numbers together because different numbers are collected in different systems (e.g. Web analytics software, billing systems, self-made databases,...), which often leads to inconsistencies. I don’t have a simple and general advice for this issue, I might address it in another post.
• If you’re not sure which metrics to track, e.g. which events in your application, err on the side of tracking too much data even if you have no immediate use for it. You never know if it becomes useful in the future, and the costs for tracking large amounts of data are no longer very high nowadays.
• If you want to read more about SaaS metrics, I highly recommend David Skok’s blog and Joel York’s blog, as well as Jason M. Lemkin and Tomasz Tunguz.

That was it for the 8th DO for SaaS startups – questions, comments and suggestions are as always very welcome!

[Update 01/17/2015: There's a new company called ChartMogul (which we invested in) which makes it easy to get a real-time dashboard of your SaaS metrics. Check it out!]