Hyper-growth in SaaS

Following his well-received guest post about cohort analysis, here comes another guest post from my colleague Nicolas. Enjoy!

Status Quo

From an investor’s perspective, SaaS companies have a lot to love: High gross margins, predictable (recurring) revenues and capital efficient operations. On the flip side, most of them follow a common thread when it comes to growth. It might be too much to label it the ‘long, slow SaaS ramp of death’, but their revenues tend to develop slower than those for consumers plays. How come? In contrast to B2C companies like Uber, Delivery Hero or Homejoy for which it was critical to get the unit economics right, scaling distribution is usually the toughest challenge for a SaaS startup after it has found product / market fit. And this is understood by the markets.

If you are looking at the assumptions for frameworks like ‘T2D3’ and growth projections as outlined by Christoph recently, SaaS companies are typically expected to scale to $100m in revenues before approaching an IPO. You can also see this growth pattern in reality, here is a telling graph of the median SaaS revenue level pre-IPO that I borrowed from Tom Tunguz:

There is no question that growing to $100M in revenues in 7-9 years is an impressive achievement and doesn’t sound like a long, slow ramp of … anything. But if you compare that with Spotify’s estimated revenue of $1B in 2014, some 9 years after founding, you quickly see that there has been a significant difference in scale of successful consumer and enterprise businesses. This holds true for other consumer focused internet companies as well. As you can see, all but one member of this cohort have reached or are on track to reach $1B in year 6 (at the latest!):

At this point I want to stress that clearly all revenues are not equal and due to high margins, customer lock-in and predictability, $1 in SaaS revenue is really something else than say $1 in e-commerce revenue. But it’s fair to say that historically IPO prospects in the B2B field could not match the explosive revenue growth of successful B2C companies.

SaaS Growth in 2015

Something is changing though. Look at these growth curves:

(taken from this great presentation by Mamoon Hamid and slightly edited)

You guessed right, they are all SaaS businesses. And while you could argue that the revenue growth curves of Company B and C still roughly follow the slope of a long, slow SaaS ramp of death to an IPO and come in around the median we saw at the beginning of this post ($2.5M-4.5M ARR after two years and $8M-12M after three), Company A is on steroids! It’s Slack (and B and C are Yammer and Box respectively).

And while that is pretty wild, I couldn’t even fit Zenefits on there properly, because with $20M ARR in under two years and a goal of $100M after three, it’s literally off the charts. Admittedly, I can't say for sure that this reported 'ARR' is net revenues or what exactly their COGS structure looks like, but either way their pace is incredible.

Are we starting to see SaaS companies taking shortcuts and adopting consumer growth curves? Let’s quickly take a look at these two examples and see what they did differently.

Case 1: Slack

You are probably using Slack, but if not just have a look at the twitter love they get. Yes, looks like they are the hottest thing on the block since KoolAid. Although I am personally not 100% sold on all design choices, the way it handles integrations and plays nice on all platforms is quite impressive. I am sure that word of mouth and referrals are the key traffic drivers for them.

Second, it’s free. At least until you hit 10k messages. And by then it is likely that you are already locked-in. So are you going to become a paid customer? What if you commit to Slack now, but your team slowly drops off and you pay for these users anyway? Fear not as Slack will only charge you for monthly active users! Pretty clever, huh?

In summary:

• A very good, consumerised product with native connectivity 
• ‘Risk-free’ freemium business model

• Bottom up growth dynamics boosted by WOM

• A large bankroll ($180M in financing)


Case 2: Zenefits

How much are you paying for your HR software right now? How about $0, plus you can manage benefits through the platform with a few clicks? Hard to deny that value proposition (although we believe that this is not one-size-fits-all and best of breed solutions like our portfolio company Humanity will win large parts of the market).

So they ‘just’ had to push that value proposition into the market. And with push, I mean push real good, as according to LinkedIn, there are over 100+ people in sales roles at Zenefits. And that’s a company in its second year! Compare that to Atlassian or Zendesk, which didn’t have a proper salesforce until they reached thousands of customers.

In summary:

• Freemium again, yet this time with a different spin

• Aggressive outbound distribution
• A large bankroll ($84M in financing)


Conclusion

So what does this mean? It’s a bit too early to predict how these two specific stories play out, but this much seems to be true:

• It’s possible to scale SaaS companies faster than ever before
 in 2015
• Consumerization of the enterprise is happening on the product and business model level
• Freemium is a valid strategy in enterprise SaaS 

• Nobody, not even suits, like large upfront commitments

• Investors are willing to make large bets early in a company’s lifetime if it adopts consumer growth curves


It’s important to note that both cases here are horizontal SaaS solutions that are attacking broad markets. I haven’t seen a vertically focused cloud company scaling this fast, but who knows what the rest of 2015 holds. I’m curious to see how this new playbook for hyper-growth in SaaS develops.

Cohort Analysis: A (practical) Q&A [Guest Post]

My colleague Nicolas wrote a great guide with tips and tricks on how to do cohort analyses which I'd like to share with the readers of this blog. Thanks, Nicolas, for allowing me to guest publish it here. Without further ado, here it is!

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At Point Nine we believe that the only way to get a real sense of user retention and customer lifetime is doing a proper cohort analysis. Much has been said and written about them and Christoph has a published a great template and guide on the topic if the concept is new to you.

With this Q&A I want to focus on some of the more practical questions that might arise when you are actually implementing a cohort analysis for your startup. After close to two years of working with SaaS companies and doing numerous of these analysis I have learned that in most cases there is no perfect step-by-step procedure. But although you will always have to do some customisation for a cohort analysis to perfectly fit your business, there are a handful of questions and pitfalls that I have seen over again and again and want to share so that you can avoid them.

Now let's get into it!

Q: Which users should I include in the base number of the cohort?

There are two parts to the answer as it depends on what you want to measure. If you want to find out your overall user retention and have a free plan, then you should include all signups of a specific month.

However if you are trying to calculate your customer lifetime value, you should only look at the number of paid conversions. I only count an account as a paid one when the user has or will be charged for a period. So if you offer a 30-day free trial for example, wait to see if the user converts into a paying plan before you include him in the cohort. This way the numbers won't be biased with users that actually never paid for your service.

If possible without too much effort, you should also try to eliminate all 'buddy plans' that you have given to friends, your team or investors. If they are not paying, they are not representative for the real cohorts.

Q: How do I treat churn within the first / base month?

There are different approaches here, but in my view taking churn within the first month into account is the most accurate representation of reality. That means that in your first month the retention could be less than 100%, if people cancel their paid subscription within that month. It would look something like this:

I do this because I don't want the analysis to exaggerate churn in the second month and understate it in the first / base month. After all the reasons for churning in the first 1-4 weeks could be very different than after 5-8 weeks.

Q: Should I treat team and individual accounts differently?

If you are at a very early stage or sell mostly (90%+) individual plans it is probably sufficient to mix them all in the same analysis. But when team plans make up a significant part of your paid accounts, or your product has a very different user experience when a whole team uses it, you should probably look at both type of accounts separately.

Findings could include that team accounts are a lot more active, churn less and see a lower drop-off in the first month than individual plans. Or not. :)

Q: What about annual vs. monthly plans?

Again, if you are focusing on how active your users are over their lifetime it is OK to mix both plans. If you just want to see how many of the people that signed up still come back after X months, no need to split hairs.

If you are however focused on churn, you should only look at paid accounts that could have churned in that month. This is one of the 9 Worst Practices in SaaS Metrics and means that you should exclude all annual plans that are not expiring in the respective month. Including these in the denominator would otherwise skew churn numbers.

Q: Now that I have it, what can I take away from it?

The two most obvious take-aways are depicted in this (KISSmetrics) retention grid. Note that this is a most likely an analysis for a mobile app and the numbers for your SaaS solution should be significantly higher:

(click for larger version)

Moving horizontally you can see how the retention of a cohort decreases over the users lifetime. Interesting here is where the highest drop-offs occur and whether the numbers stabilise after a few months.

Vertically, you can (ideally) see how the retention of your cohorts change over the product lifetime. Assuming you are not twiddling your thumbs while catching up with House of Cards or sipping Mai Tai’s at the beach once your product launches, you should see an improvement in user retention with younger cohorts as the product improves. If this is not the case, you should consider whether the hypotheses or features you are working on are the right focus.

Most importantly though, this data will be the basis to give you a sense for your customer lifetime value (CLTV). If you take the weighed retention data for the 6th or ideally 12th month and extrapolate it, you will get an approximation for the average lifetime of your customers. Multiplying this with the average revenue per account (ARPA) or respective plan that you are looking at (e.g individual / team) it will give you your CLTV. This number is really the quint essence of the cohort analysis, as it gives you an idea about how profitable your business model is (=how much more money are you making with than what you are paying to acquire him). Subsequently it will also tell you the highest price you can spend on customer acquisition to grow profitably. It is important to note here that although super valuable, especially in the early stages of a startup this number will always be an estimation and most likely not 100% accurate. So keep in mind to continually track and fine-tune your CLTV calculations.

And one last thing: If you have accounted only for paid subscriptions as defined at the first question above, then the base rates of each month will also give you the most accurate number for paid customer growth and subsequently MRR growth. Two charts you will want to have at hand when talking to investors.

Q: Is that it?

For this post, yup! If you want to learn more about cohort analysis or SaaS Metrics, I would strongly suggest to check out Christoph’s and David Skok’s blog. And in case you have any questions on the above or something is unclear, feel free to ask away in the comments or send me a mail and I will do my best to answer you (or forward the hard questions to Christoph). ;)

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Like this post? Make sure you add Nicolas' blog to your reading list.