There are over 100 SaaS unicorns. How long did it take them to get to $100 million in ARR?

A few days ago I wrote that there’s more than one path to $100 million. I argued that while it’s awesome to see that some companies are able to get from 0 to $100 million in ARR in 7-8 years or even less, trying to grow that fast may not be the best choice for most companies.

That raises the question: What are your chances of growing a little slower and still achieving massive success? Considering that most investors are pretty obsessed focused on finding companies that follow the legendary T2D3 growth path (directionally confirmed by the responses to our SaaS napkin survey earlier this year), you might expect that your chances are low.

To answer the question, I took a look at the historic revenue development of ~70 of the largest SaaS companies. A couple of notes (and some caveats) on the data sources and methodology that I’ve used:

• Most of the companies are publicly listed, in which case it was easy to get accurate revenue data from YCharts or from the companies’ SEC filings.
• For private companies, I used various online data sources, including Wikipedia and various blogs. For these companies, the numbers are by their nature less certain.
• All revenue figures are based on GAAP revenue as reported by public SaaS companies, i.e. the numbers do not show a company’s ARR. In most cases, this doesn’t make a huge difference (if all revenue is subscription based, GAAP revenue trails ARR) but note that for companies with a larger percentage of setup fees, revenue from professional services or other non-recurring revenue sources, the difference is bigger.
• Some companies use different fiscal years. As I didn’t want to look into monthly revenue numbers in order to get the exact revenue numbers for each calendar year, I used some simple rules in these cases: If a company’s fiscal year ends on March 31, I allocated the revenue of that fiscal year to the previous calendar year. If the fiscal year ends on October 31, I allocated it to the same calendar year.
• In most cases, the “founded” date corresponds with the year in which the company was founded, but there are a few exceptions, like Slack, which started in 2009 with a completely different product and didn’t launch Slack as we know it today until 2013. In that case, I used 2013 for the “founded” year.
• This is not a scientific project and the data hasn’t been double-checked by anyone so far, so it’s well possible that there are some bugs in there.

Here are my findings:

1.) I estimate that there are over 100 SaaS unicorns
The list contains almost all public SaaS companies and some of the largest privately held ones that I could find public data for. In total, the list contains 70 SaaS companies. All of them are at $100+ million in ARR, and with the exception of one company (Domo), all of them are worth more than $1 billion. I can think of at least 10-20 other SaaS companies that should be added to the list (Talkdesk, Pipedrive, Intercom, OneLogin, AirTable, InVision, Procore, Canva, Asana,...), and I’m pretty sure there are at least 20 further ones that I’m not aware of. That makes it a pretty safe assumption that there are now 100 SaaS unicorns.

2.) The average time-to-$100-million is 10 years
There you have it! :-) Even if you look at a selection of the best of the best SaaS companies, getting to $100 million in 7-8 years is not the norm.

3.) Growth has accelerated in the last decade
If you only look at companies that were started in the last 15 years, the average time-to-$100-million drops to an impressive 8 years. That’s not too far away from the T2D3 path and it shows that it is indeed possible to grow that fast; however, there are also several companies in this cohort that took 10 or more years.

4) Growth rates significantly drop as companies pass through $100 million
In the bottom right corner of the sheet you can see the average y/y growth rates for the year in which the companies hit $100 million and for the following year. As you can see, the average annual growth rate drops from around 75% going in to $100 million to around 50% coming out of $100 million. This is not surprising – as Rory O’Driscoll of Scale Venture Partners explained in this post, growth rates almost always decrease with increasing absolute numbers.

There’s more than one path to $100 million

A couple of years ago I wrote a post titled “How fast is fast enough?”. The subtext of the question was “How fast do you have to grow if your ambition is to get to $100M in ARR and build a very large company”. It’s an important question, as your target growth rate determines your hiring plan, budget, and fundraising strategy.

In that post, I looked at how long it took publicly traded SaaS companies to get to $100M in ARR and concluded that if your goal is to reach $100M in ARR, you should try to get there within 7-9 years after launch. The thinking was that if you grow significantly slower, your chances of ever getting to $100M will go down. Meanwhile, a few SaaS companies have shown even more spectacular growth. Slack reached $100M in ARR just two and a half years after launch and Dropbox got to one billion dollar in ARR within ca. eight years. UIpath, the wildly successful robotic process automation solution out of Romania, is on a similar trajectory. But if you’re thinking that in light of these bar-raising success stories, I will suggest to further push up your growth targets, I have a little surprise for you. :-) I’m going to say the opposite – that you might want to consider a slightly slower pace.

To be clear, if you can pull off a “T2D3”, that’s fantastic. A SaaS company that gets to $2M in ARR within 1-2 years, triples in each of the next two years and doubles in each of the three following years is headed straight to unicornland. If you can do that without burning hundreds of millions of dollars along the way (or even hitting a wall), go for it. The crux is that this is a pretty big „if“.

Setting yourself up for T2D3-style growth usually comes with a very high burn rate – hundreds of thousands of dollars per month, eventually likely millions, depending on where you’re at in the journey. The main reason is that your customer acquisition costs are highly front-loaded. While this is generally true for most companies, it’s particularly true for SaaS businesses, which invest heavily in product development, sales, and marketing upfront and get payments from customers over a delayed period of time, usually several years. Let’s say you have a CAC payback time of 12 months, i.e. your fully-loaded customer acquisition costs equal 12 months of gross profit. If your customer lifetime is, say, four years, this means that the gross profit from the first year pays back your customer acquisition costs, and the gross profit from the following three years can be used to cover your fixed costs and eventually create profits. Not bad.

What makes things tricky is, first, the uncertainty of how your CACs will develop at increasing scale and of how your churn rate will develop over time. As I wrote here, trying to forecast what happens to your CACs if you 10x your sales and marketing spend is very difficult. The second issue is the timing of some of the major expenses. If you close a mid-market or enterprise customer today, it usually means that a salesperson, let’s call her Maria, has been working on the deal for 6-12 months. Maria probably required at least three months of onboarding and training, and chances are that three months before Maria’s first day at your company you paid a recruiter (or incurred other types of recruiting expenses) to find her. Presumably, you also increased your marketing budget to generate more leads 6-12 months before Maria closed that deal.

In other words, if you want to meet your Q1/2020 targets, you will likely start incurring costs related to these targets very soon, a year before you start to generate cash, and two years before these investments start to become ROI positive. That enormous lag time (which the always excellent David Skok calls the SaaS Cash Flow Trough) makes it hard to course correct if things don’t go according to plan. Like a large tanker at cruising speed that cannot quickly take a turn, a startup with a fast-growing headcount and a high burn rate loses some of its ability to quickly react to new information, new insights, or changes in the market. 

If you’re setting yourself up for hypergrowth, the margin for error is very thin. If you’re highly confident in your PMF and the scalability of your sales and marketing machine and you’ve raised enough money to survive a few missed targets, go for it (but keep a very close eye on pipeline coverage, quota attainment, and other leading indicators). If, however, you’re less certain or you have a smaller war chest, consider going a little bit slower. 

One way to sanity check your budget is to simulate what would happen if your costs grew as planned while revenue increased only linearly, i.e. you assume that you’d keep adding the same amount of net new ARR in the next quarters that you’ve added in the last quarters. Let’s say you’ve grown from $6M to $18M in ARR in 2018, perfectly in line with the T2D3 mantra. Let’s assume you’re planning to double in 2019, from $18M to $36M in ARR, while burning around $20M (so you’d burn about $1.10 for each $1 of net new ARR, which is quite healthy). Now imagine that you’re spending money as planned, but instead of adding $18M in net new ARR in 2019 you’re adding only $12M, the same amount that you’ve added in 2018. As a result of missing your revenue target by 33% (or just 17%, if you want to fool yourself and calculate target achievement based on ARR as opposed to net new ARR), you’ll burn around $6M more than planned (the precise amount depends on your payment terms). I’ve created a very simple model that illustrates this.

As you can see, if you’re hiring for T2D3 growth but you end up growing revenue somewhat slower, the gap between your revenues and your costs will widen very quickly, which leads to a double whammy: Your runway shortened because you’ve burned more than planned, so you’ll have to raise again sooner, and at the same time your growth rate went down, which makes it harder to raise more money. In a situation like this, two or three missed quarters can be life-threatening if you don’t have enough cash in your war chest. Because of this, make sure that whatever path you choose, all key stakeholders (co-founders, board, investors, leadership team) are aligned on the plan and potential fallback scenarios.

The good news is that growing a little slower is not the end of the world. If you have a great product with high NPS, low churn, and an excellent position in your market segment, you have a decent chance of getting to $100M in ARR even if your growth rate starts dropping significantly below 100% y/y at around $10M in ARR. It just takes a few more years, but hey, $100M in ARR is cool even if it takes 10-12 years instead of 7-9, isn’t it? :) 

Giving yourself one or two more years to get to $100M has an enormous impact on the required growth rates. You can see this if you play around with the numbers in this little calculator that lets you calculate how fast you have to grow in order to reach $100M in ARR within different time spans. Besides a linear and an exponential growth model, it also shows what Rory O’Driscoll called the “Mendoza Line of SaaS growth”, a very interesting concept which assumes that your growth rate for any given year is likely around 80 percent of your growth rate in the prior year, which is a more realistic assumption than having a constant growth rate.

Now, what does the data tell us, are there any (or many?) SaaS companies that took a few extra years to get to $100M, or is it “T2D3 or bust”? I looked at more than 60 SaaS companies to answer that question, but I realize this post has already become much longer than planned, so with apologies for the cliffhanger, let me save the answer for a followup post that is coming very soon. :)

Founders: Please don’t allow anyone to screw your early backers

Understanding the mechanics of founder re-ups in financing rounds

This post will likely not make me more popular and might offend some people. But if your core beliefs on how business should be done are at stake, you can’t try to win the popularity contest.

If you know me a little you’ll probably agree that like everyone at Point Nine, I’m a pretty nice guy. We’re trying hard to make venture capital a little more human, and we really mean it when we say that we aspire to be good VCs. I’m pretty sure that almost all if not all of the more than 200 founders we’ve worked with over the last ten years would confirm this. 

I’m not saying this to brag or to say that we’re perfect (which we are not, of course). What I’m hoping is that the reputation of being a nice, founder-friendly VC, which I believe we’ve earned in the last ten years, as well as the fact that I’ve co-founded two VC-backed startups myself and therefore know both the founder perspective and the VC perspective, gives me the right and credibility to write this post. Calling out others for questionable behavior always comes with the risk of hypocrisy, but I’m happy to subject our business practices to public scrutiny. If you think I (or anyone from my team) ever did not meet our standards, please reach out.

In the last year, we have seen, on more than one occasion, a behavior among later-stage VCs that we’ve rarely observed in the years before. This might be due to the fact that our portfolio has become mature, which explains why there are now more portfolio companies that are at the stage at which the issue (which I will detail in a second) tends to occur. It’s also possible that the increasingly intense and sometimes downright crazy competition for the hottest deals among later-stage VCs has made this behavior more prevalent.

Here’s what I’m talking about. In the last 12 months or so it happened several times that later-stage VCs, as part of financing rounds, offered a “re-up” (i.e. new shares or options) to founders of portfolio companies. By doing this, they try to partially or completely offset the dilution (i.e. reduction of ownership percentage) experienced by the founders in the financing round. If you think “Great, if founders get more shares and are diluted less, that’s awesome!”, think about the effect which this maneuver has on the existing investors of the company (as well as on employees holding options or shares).

If founders get a re-up, every single share, option, or ownership percentage that they receive (obviously) needs to come from someone. And that someone are the existing shareholders of the company. Oftentimes, the re-up shares are proposed to come out of the pre-financing cap table, in which case it’s obvious who bears the dilution. Sometimes it is proposed that the re-up shares are created post-financing. The latter might make the maneuver seem fairer on the surface, as it appears as if the new investors joined the existing investors in paying the price for the additional founder shares. But if you do the math, you'll see that it doesn’t solve the crux of the issue. More on that in the example below.

An investor who suggests a founder re-up does that, of course, to make his/her offer more attractive to the founders in order to increase the chance of winning the deal. If a founder considers two offers, one with a founder re-up of a few percentage points and one without, the offer with the re-up will be significantly less dilutive to him/her even if the offer without the re-up comes with a significantly higher valuation. Consider this simple example:

(click for a larger version)

This (simplified) cap table model shows the effect of a $40M investment on the founders’ shares in two scenarios: The first one assumes a $140M pre-money valuation and no founder re-up; the second one assumes a $120M pre-money and a founder re-up of 10% pre-financing (which equals a transfer of 3% of the post-financing equity from the existing investors to the founders). As you can see, the founders are better off in the second scenario, in spite of a ca. 15% lower valuation.

Let’s take a closer look at the mechanics that are at play here:

(click for a larger version)

(Here is the Google Sheet if you'd like to see the calculations)

For all scenarios, I assumed that before the financing round, the founders and the existing investors own 60% and 40%, respectively, of the company. I further assumed that the company wants to raise $40M and that the existing investors will participate with an investment of $10M, so $30M come from the new investor.

Let’s say a VC (who I’ll call “VC 1”) offers the company a pre-money valuation of $120M (Scenario 1A). In this scenario, the founders and existing investors would hold 45% and 36.25%, respectively, after the round. Now let’s say another VC (“VC 2”) offers the company a higher valuation, $140M (Scenario 2). In this scenario, the founders would hold 46.67% after the financing, while the existing investors would be at 36.67%. Scenario 2 is significantly better than Scenario 1A, for the founders as well as the existing investors, so (assuming both VCs are of equal quality) the company should go for VC 2.

But VC 1 doesn’t want to lose the deal, of course. He/she could increase the valuation to make his/her offer more attractive, but hey, that would reduce his/her stake. So instead of offering a valuation that is equal to or higher than what VC 2 has offered, VC 1 now proposes a founder re-up of 10% of the pre-financing equity. As you can see in Scenario 1B, this would result in a 48% stake for the founders, which is significantly higher than the 46.67% they would hold if they went with VC 2. Meanwhile, nothing changed for VC 1, as he/she would own 18.75% in Scenario 1A as well as 1B, so everyone should be happy, right? Not quite: The existing investors’ stake in Scenario 1B is reduced from 36.25% to 33.25%, precisely by the three percentage points by which the founders’ stake is increased as a result of the re-up. This is the 3% transfer from the existing investors to the founders that I’ve mentioned a few paragraphs ago.

If VC 1 wanted to get the founders to 48% without meddling around with the cap table, he/she would have to increase the pre-money to $160M. You can see this in Scenario 1D. By offering a re-up instead, VC 1 managed to make his/her offer the top offer for the founders while offloading 100% of the costs of the re-up to the existing investors. Scenario 1C shows what happens if the investor is willing to do the re-up after the financing. In that scenario, he/she does end up with a lower stake compared to Scenario 1B (17.73% vs. 18.75%), but if you compare it with Scenario 1D (AKA the “don’t mess around with the cap table” offer), he/she is still much better off in 1C, at the expense of the existing investors.

I want to believe that the later-stage investors we’ve worked with so far all had good intentions, and maybe I should understand that if you’re trying to win a competitive deal and want to set up a company for success, concerns of other investors aren’t your number one priority. That said, there is an act which, according to Wikipedia, is defined as “giving something of value [in this case shares] in exchange for some kind of influence or action in return [in this case the deal] that the recipient would otherwise not alter.” ;-) The fact that here that “something of value” doesn’t even come from the later-stage investor, doesn’t make it any better.

Obviously, investors engaging in this tactic aren’t stupid, so the official version is usually not “rather than offering a higher valuation [which would benefit all shareholders equally], we’ll give you a lower valuation but will offset some of the dilution by giving you [the decision makers] some extra shares”. The official justification is almost always incentivization of the founders, i.e. some variation of “the founders only own x% of the company, we need to make sure they have enough shares to be fully motivated”. Well, if that was your concern, Mr. Late-Stage Investor, offer a higher valuation to make the round less dilutive. Oh, I forgot, that’s not possible because you have to own 20% of the company to make the investment worth your while. Sorry for getting cynical, but as you can see, this issue has caused me a great deal of annoyance.

The prospect of keeping a larger stake can understandably be tempting for founders, and once the pandora box has been opened by a new investor, it can be hard to shut it. What makes the situation particularly uncomfortable is that if as a seed investor you object the founder re-up, you suddenly look like the bad guy who doesn’t want to grant the founders some additional shares for all their hard work and who risks the entire deal by bringing up your concerns, while the later-stage investor looks like the good guy who wants to reward the founders. As we’ve seen in the example above, this interpretation is absurd because the later-stage investor proposes a reward that benefits him/her and is borne by someone else, but in the hectic and pressure of term sheet negotiations, this can be forgotten. Therefore it’s all the more important that founders fully understand the implications of a re-up and that they don’t let anyone divide their interests from the interests of other existing shareholders.

So is it always bad if an investor proposes changes to the cap table? No. There can be situations in which cap table restructurings may be necessary. If, for example, we wanted to invest in a seed-stage startup and found out that the company is majority-owned by an angel investor or incubator, we would most likely conclude that for the company to be VC-backable, and for the founders to be motivated and incentivized for the next ten years, something needs to change. But these are rare cases, and the fact that they exist doesn’t justify using founder re-ups as a tactic to win deals.

If any later-stage investors are reading this, please reconsider your tactics. Just treat upstream investors how you want to be treated by your downstream investors. Easy.

And to all founders out there: Please don’t let anyone screw your early backers.

10 Observations from Dropbox's S1

In last week's post I shared some thoughts about Dropbox and why, although Dropbox is unquestionably one of the most amazing SaaS companies ever built, I am a tad less confident in the company's long-term future than I am in other SaaS leaders such as Salesforce.com, Zendesk, or Shopify.

As mentioned in the first part of the post, I took a closer look at Dropbox’s recent IPO filing and would like to share some tidbits, along with a few observations.

#1 – Dropbox on consumerization

"Individual users are changing the way software is adopted and purchased
Software purchasing decisions have traditionally been made by an organization’s IT department, which often deploys products that employees don’t like and many refuse to adopt. As individuals increasingly choose their own tools at work, purchasing power has become more decentralized."

As mentioned in the first part, Dropbox was one of the early champions of the "consumerization of enterprise software" movement. This paragraph is a great description of that concept. If you ever have to pitch the idea of consumerization to anyone, copy these lines. :-)

#2 – The King of Freemium

Viral, bottom-up adoptionOur 500 million registered users are our best salespeople. They’ve spread Dropbox to their friends and brought us into their offices. Every year, millions of individual users sign up for Dropbox at work. Bottom-up adoption within organizations has been critical to our success as users increasingly choose their own tools at work. We generate over 90% of our revenue from self-serve channels — users who purchase a subscription through our app or website.

Before reading the S1, I didn’t know if Dropbox has become somewhat more focused on enterprise sales over the years. But here you have it – it really is the King of Freemium, generating more than 90% of revenue from self-service channels.

#3 – It’s a Mouse Hunter!

Dropbox’s ARPU is around $110 per year, confirming that the company is indeed the ultimate Mouse Hunter. It’s worth pointing out that $110 is the average revenue per user, not per account, and one account can consist of multiple users, so the company’s ARPA (which hasn’t been disclosed) is probably significantly higher. However, according to the S1, 70% of the company’s 11 million paying users are on an individual plan as opposed to a "Dropbox Business" team plan, so at least 70% of the company’s revenue does indeed come from mice.

#4 – More than half a million $ per head

As of December 31, 2017, Dropbox had 1,858 employees. Revenue for 2017 was $1.107B. That’s $595,800 per employee. Mind blown. For comparison, according to a Pacific Crest survey among private SaaS companies, the median SaaS revenue per employee of that group of companies was $136,000 in 2016.

Salesforce.com generates a similar (actually, even higher) amount of revenue per employee, but the company is almost twice as old and has much bigger scale, so you’d expect them to be more efficient. When Salesforce had around $1B in revenue, in 2008, it had around 3,300 employees, so at that time its revenue per employee was around $327,000. Not a bad ratio at all, but Dropbox’s revenue-per-employee ratio is truly spectacular – a testament to its extremely effective and efficient bottom-up adoption driven by product virality.

#5 – WTF?!

“Although it is important to our business that our users renew their subscriptions after their existing subscriptions expire and that we expand our commercial relationships with our users, given the volume of our users, we do not track the retention rates of our individual users. As a result, we may be unable to address any retention issues with specific users in a timely manner, which could harm our business.”

We “do not track the retention rate of our individual users”. Wait, what? Did I read this right?

#6 – A unicorn’s worth of office rent

“In October 2017, we entered into a new lease agreement to rent office space in San Francisco, California, to serve as our new corporate headquarters. The total minimum obligations under this lease agreement are expected to be approximately $827.0 million.”

When I read this number for the first time, I was wondering if there’s a typo. $827 million is going to be spent on office rent? A rough calculation shows that the number isn’t as crazy as it might appear on first sight. Assuming the company currently employs around 1,500 people in San Francisco and that that number will grow to 5,000 in the coming years, and assuming it’s a 12 year lease, rent per employee per year (at 5000 employees) would be around $13,800. That’s still expensive, but not “they must have accidentally added a zero” expensive.

#7 – I don’t understand this … is it just me?

“As of December 31, 2017, our blended Annualized Net Revenue Retention across the entire business, including individuals and Dropbox Business customers, was over 90%.”

“We continuously focus on adding new users and increasing the value we offer to them. As a result, each cohort of new users typically generates higher subscription amounts over time. For example, the monthly subscription amount generated by the January 2015 cohort doubled in less than three years after signup. We believe this cohort is representative of a typical cohort in recent periods.”

If you don’t understand how to reconcile these two statements, you’re not alone. Looking at the cohort chart on page 62 of the S1, you’d expect Dropbox to have a significantly negative net dollar churn rate, i.e. net revenue retention of significantly over 100%. The only scenario, in which the two statements above could be compatible, is if a user cohort’s revenue doubles during the first three years but then declines steeply, but I have no idea if that is the case. If you know or have an idea what I’m missing here, I’d love to hear it!

#8 – Weaning off AWS

Look at this. From 2015 to 2017, Dropbox increased revenue from around $600M to ca. $1.1B. During the same period, the company decreased cost of revenue from over $400M to less than $370M. In percentage terms, CoGS decreased from around 67% to around 33%. You don’t often see a company halving its CoGS percentage within two years. Either Dropbox was pretty wasteful in 2015 or they are extremely efficient now. ;-) I think it’s a bit of both.

According to the S1, the remarkable CoGS reduction was achieved primarily by closing accounts of inactive users and by moving more than 90% of all user data from AWS to Dropbox’s own server infrastructure. For what it’s worth, this also gives you a hint on the margins of AWS.

#9 – Eleven 9s?  

"Our users trust us with their most important content, and we focus on providing them with a secure and easy-to-use platform. More than 90% of our users’ data is stored on our own custom-built infrastructure, which has been designed from the ground up to be reliable and secure, and to provide annual data durability of at least 99.999999999%. We have datacenter co-location facilities in California, Texas, and Virginia."

I thought six 9s are considered best-in-class, so I was surprised when I counted eleven 9s in this paragraph. Eleven 9s correspond with 0.00032 seconds of downtime per year, which for all practical purposes means that Dropbox can never go down. I re-read the sentence and noticed that Dropbox isn’t referring to availability (i.e. uptime) but data durability, which, as I now know, is something else.

#10 - Multiple personalities?

This is how Dropbox wants to be viewed:

This is how I view it:

If you read the S1 and take a look at Dropbox’s website, it becomes clear that the company wants to become much more than just a service that takes care of file storage and synchronization behind the scenes. They don’t want to be just an icon in your file system, they want to unleash the world’s creative energy by designing a more enlightened way of working (Dropbox’s mission statement).

That makes perfect sense, as being a “background service” might ultimately prove not to be a defensible, high-margin business. I’m somewhat skeptical if their (relatively) new “Paper” product will become a success. But with 500 million registered users, 11 million paying users and 300,000 paying work teams, the company has time to figure it out.

Dropbox, the ultimate Mouse Hunter

I’m late to the party here, I know. Dropbox went public a bit more than a month ago and I’ve finally had a chance to take a close look at the company’s S1. I’ll be sharing a few specific observations from the S1 review, but let’s start with some more general thoughts about the company.

The mighty king of Freemium

Like Zendesk, Yammer, and a few other SaaS companies that were all founded around 2007-2008, Dropbox was one of the early champions of the "consumerization of the enterprise" movement. In contrast to Zendesk (and I think, Yammer), which eventually moved upmarket and now generates an ever-increasing percentage of revenues from larger customers, Dropbox is still getting most of its revenues from individual users and small teams. The company hasn't disclosed how much revenue it is generating from larger companies, but according to its S1 filing, a staggering 70% of its 11 million paying users are on an individual plan as opposed to a "Dropbox Business" team plans. More than 90% of its users are acquired via self-service channels, presumably driven in large part by the inherent virality of the product. These characteristics make Dropbox the "King of Freemium", as Tomasz put, or the ultimate “Mouse Hunter”.

And what an almighty King it is! Dropbox was the fastest SaaS company ever to hit $1B in ARR. As every aspiring SaaS entrepreneur knows, getting a hundred million dollars in ARR within around eight years is incredibly hard and extremely rare. Getting to more than one billion within the same timeframe is completely nuts. If the improbability of reaching a $1B valuation is epitomized by a unicorn, getting to $1B in SaaS revenues within eight years is as unlikely as seeing a unicorn with three heads.

Dropbox is one of the very, very few companies in the top left corner of the LTV/CAC chart.

A three-headed unicorn

So what is it that made Dropbox beat all odds? I believe that no single factor alone can explain a success of this magnitude. Instead, I think that the right team has to hit the right opportunity at the right time. Call it the positive equivalent of a perfect storm.
More specifically, here are some factors that I think contributed to Dropbox's success, in no particular order:

1. Timing
As consumers tend towards using more devices over time, they’ll experience a  bigger need for a solution that synchronizes files across all of their devices. Until 2005 or so, most people used only one or maybe two devices to work with their files: a desktop PC and/or a laptop. Dropbox was founded in 2007, the year the iPhone was launched and just when the move to a multi-device world started to become inevitable. Dropbox also benefited from an ever-increasing number of remote workers who need easy access to their company's files. According to a 2016 study by Deloitte that is mentioned in the S1, 30% of full-time employees primarily work remotely.

2. Product
Dropbox managed to beautifully solve a very difficult problem. It might look like a simple product on the surface, but from handling versioning conflicts to building deep integrations with different operating systems to ensuring secure and fast access to files, it required solving a number of hard technology problems. I remember that before switching to Dropbox, I used another piece of software to sync files across two computers. It was pretty messy. With Dropbox it just works.

3. Virality
While it's possible to use Dropbox just by yourself, my guess is that at some point, most users use Dropbox to share files with one or more other users. It's this built-in virality that allowed Dropbox to grow at a pace that no other B2B SaaS company has seen before. As if this wasn't enough, Dropbox also had a famous two-sided referral program that augmented the inherent virality with additional referral incentives.

4. Team
I don't know the founders of Dropbox, but looking at the quality of the early product and their referral program, it's clear that the founding team combined excellent product and tech skills with a strong growth mindset. In any case, the results speak for themselves – there's no question that a remarkable team must have been at work here.

Dark clouds on the horizon?

As much as I love Dropbox – the product and the company – I'm not entirely sure about the company's long-term prospects. Dropbox's one big weak spot, in my opinion, is that the product is almost UI-less. While you can access your files using Dropbox's (simple) Web app, there's very little need for it. We use Dropbox for all of our files at Point Nine and I have it running on four devices, but Dropbox does its magic almost entirely in the background. That makes me think that Dropbox is much less sticky than other SaaS products, e.g. workflow tools that require training. I could imagine that if a company's IT department decides to switch the file storage and sharing provider for its entire workforce overnight, most people wouldn't even notice it. In contrast, imagine the outcry that would ensue if you took away Zendesk from a support team or if you tried to get your development team off Slack.

Would I switch to another provider to save $20 a year? No, not worth the hassle. Would I consider moving all files to Google Drive if it's significantly cheaper and if a tighter integration with GMail, Google Calendar and Google Docs offers more and more benefits? Yes. (Interestingly Google Drive’s “Quick Access” feature is already using e.g. information from your calendar to predict which file you are likely to need at which point in time.)

I think the company has recognized this issue. Two and a half years ago they launched "Paper", a collaborative document-editing app, presumably to get more "face time" with its customers and to own a bigger part of the value creation chain. However, I know almost nobody who uses Paper and the company doesn't disclose any usage numbers, so my guess is that it's not a big success so far.

Don't get me wrong, more than $1B in ARR and 500 million registered users are an incredible asset. The King of Freemium won't be dethroned any time soon. But for what it’s worth I didn't buy the stock yet :-)

Update: Here is part 2 of this post.

Quick thoughts about Blogger and Medium. Plus: The 2018 SaaS Funding Napkin!

I usually use this blog when I write new posts. Occasionally I re-publish selected posts on our Medium channel. Lately, however, I've observed myself publishing on Medium first, for the simple reason that the authoring experience is much better on Medium than on Blogger, especially when you're including a lot of pictures. 

What can we learn from this?

1. You can lure users away from an old product by offering a much better UX. A bit better isn't enough to get over inertia and to offset switching costs. It has to be 10x better and cheaper, like Sarah Tavel said. (When I say "10x better" I don't mean it literally but figuratively because in most cases I don't know how the superiority of one user experience over another can be measured quantitatively.)
2. If the incumbent benefits from network effects, it's much more difficult. A complete migration from Blogger to Medium would be very painful for me because like you, most of my readers are here – and many of you are reading the blog using an RSS subscription or an email subscription, or you've bookmarked www.theangelvc.net, all of which would cause friction if I decided to migrate.
3. At some point I have to switch to a blogging platform that has not been built in the last millennium. :-) My current thinking is to switch to a hosted Wordpress provider, use a minimalistic Medium-like template, and find a solution that doesn't require readers to switch their RSS/email subscriptions. Let me know if you have any thoughts. :)

Anyway, the actual reason for this post is that I've just published a series of blog posts, along with the 2018 version of the SaaS Funding Napkin, on Medium, and I wanted to make sure that you don't miss it. 

Here you go:

Part 1: What does it take to raise capital, in SaaS, in 2018?

Part 2: The top 3 things investors are looking for in SaaS startups

Part 3: The SaaS Funding Napkin 2018

You can also check out the napkin on Product Hunt, and if you're interested in the physical, real version of the napkin, fill out this short Typeform!

We’re looking for an Associate

I’m very excited to announce that we’re looking for a new Associate. In all modesty, I think that for a young, smart person who’s passionate about startups and technology, an Associate role at Point Nine is one of the fastest ways to learn, build your network, and advance your career. Case in point: Rodrigo, who started as an Associate four years ago, is now a Partner at Point Nine; Fabian is running his own fund; Nicolas became a “30 under 30” and is now VP at Insight; and Mathias is now GM Germany at Uniplaces.

As I wrote last time when we were adding an Associate to our team, I'm pretty sure that it took me more than 10 years to get the expertise and network which you'll get during three years in this job.

If you’re interested, here are all the details. If you know somebody who could be a great fit, please pass on the link or let me know. Thank you very much in advance!

PS: As you may or may not know, the Associate role at Point Nine has historically been called “Truffle Pig” – because just like a truffle pig is digging up the best truffles from the ground, we as an early-stage VC try to find the best startups among a large number of potential investments. I still kind of the like that analogy, but all good things must come to an end. For now, we’ll just call the new position “Associate” but if you have a creative idea for something funnier I’m all ears!

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! :)

Getting feedback from your Board

After a Clio Board Meeting last week I received the following email from Jack Newton, the company's amazing co-founder & CEO.

Hi everyone,

I'd like to experiment with requesting some 1:1 feedback on our board meetings. Please take 5 minutes and provide feedback through this Typeform:

https://xxx.typeform.com/xxx...

Cheers,

Jack

I thought this was a really great idea and worth sharing here. I removed the URL from Jack's Typeform but rebuilt it quickly so that you can check it out:

powered by Typeform

If you're not getting feedback from your Board members you're missing out on something. Preparing and holding Board meetings is a big time investment, and making them really effective isn't easy. So you should try to get as much value out of them as possible.

Sending out a post-meeting Typeform is, of course, not the only way to get feedback: In some Boards that I'm a member of we sometimes do an executive session between the CEO and the directors. Sometimes I try to summarize my thoughts at the end of the meeting, sometimes I do it in a followup email after the meeting.

But doing it with a Typeform might help you ensure that you'll be getting feedback more consistently: after each Board meeting, from each director. I think this format might also help you get more candid feedback because not everyone is good at delivering honest feedback in a meeting. As a side benefit, you'll start building an archive of feedback that you can revisit later. No rocket science, but sometimes little things can make a difference, and I'm curious to see how this one will pan out.

Thanks to Jack for giving me permission to share this here (and thanks Fred Wilson, who, as I've learned from Jack, inspired Jack on this topic).

Unsure how much you should pay yourself? Check out this Founder Salary Calculator.

[July 21, 2023]
There’s a newer version of this post, including an updated calculator.

Founder salaries are not a topic I’ve had to spend a lot of time with so far. I usually just “OK” them, since the founders we are working with are all super reasonable people who carefully weigh how much they need against the interests of the company – their company. But sometimes founders ask me for a suggestion or some guidance because they are uncertain as to what is fair, and so I thought it might be useful to create a simple model.

Here it is.

The model calculates the founder salary based on three drivers: stage, family situation, and location.

Stage

Unless you’re in the fortunate position to generate revenues almost from day 1 or to raise a sizable seed round right at the start you’ll probably not be able to pay yourself any salary at all, at least in the first few months, for the simple fact that the company doesn’t have any money to spend. If you raise a small angel or friends & family round, you’ll probably want to spend it on other things than founder salaries. Once you’ve raised a bigger seed round and/or you start to generate revenues, that changes and you can pay yourself a modest salary.

In the calculator, I’ve assumed that the “entry salary” for a Berlin-based founder who doesn’t have kids is $50,000. I’ve then assumed that that amount increases to $75,000, $95,000 and $115,000 when you reach funding and revenue milestones that roughly correspond with a Series A, Series B and Series C round, respectively. I don’t think founders should get salaries that make them rich, but as soon as the company can afford it the founders should get enough so that they don’t have to be worried about how to make ends meet all the time. And if a little more allows them to outsource some errands and chores after a 100-hour-work-week I’m all for it!

Family

It might surprise you to hear this from a venture capitalist, but my approach to founder salaries is a little communistic: I think founder salaries should not be based on performance alone but should also take into account what the founder needs. If that means that one founder gets more cash than the others because in contrast to them he or she has a family to take care of, that’s fine with me. A founder’s cash compensation doesn’t reflect the value which she contributes to the company anyway, so who cares if one of them gets a little more than the others.

My model, therefore, assumes that for each kid you add $10,000 (multiplied by the location factor, more on that soon). Whether this is the right amount is of course debatable, and there can be other aspects besides having children that need to be taken into account.

The “need-based” approach can, of course, go both ways: if a founder had a sizable exit already, he may want to forgo his salary or reduce it to a symbolic amount, at least in the first few years. I did that at my last startup, Pageflakes, and thought that besides saving the company some money it can also have a positive impact on the company culture if people know that the founder’s interests are 100% tied to the company’s success.

Location

The third factor that I’ve included is location. I’ve defined Berlin as 1.0x and have assumed that in Paris, London and San Francisco, you’ll have to pay yourself 1.3x, 1.5x and 1.8x as much in order to have a similar standard of living. These ratios are roughly in line with the data published on this website. If you want to find out the ratios for other cities, take a look.

Notes

• The numbers in the model reflect what I think is market and fair based on the data points that we have and some industry benchmarks that we were able to get. However, our data set is quite limited and the numbers produced by the calculator should by no means be taken as the ultimate truth. If you disagree with my assumptions or have seen different numbers in the market I’d love to hear from you!
• I saw a study according to which founder salaries are much lower. According to this data source, 75% of Silicon Valley based founders pay themselves less than $75,000, with 66% paying themselves less than $50,000. Based on these numbers, even for companies that have raised more than $10M the average salary is only $81,700. This looked odd to me, and maybe the difference is due to the fact that the study is three years old. I ignored this data source for now, but again, suggestions and input are very much appreciated.
• The model assumes that the founder gets a fixed salary with no bonus. I’m not strongly against including a bonus component in a founder’s package, but I think it’s usually not necessary. If you own a big chunk of equity, I don’t think you’ll need a performance bonus to be motivated and rewarded.
• The model doesn’t differentiate between the founding CEO, tech founder and other roles. In the first couple of years it’s usually not necessary to differentiate based on the founder’s role because everyone in the founder team carries a similar load. At a later stage, when the company has a bigger leadership team, it makes sense that the CEO gets more than the other founders. The numbers in the model are calibrated for founder CEOs, so you may want to reduce the amounts for other founders at the Series B or C stage.
• The calculator shows the results for the various stages and locations simultaneously, so you can easily compare the numbers side-by-side. The number of kids, however, needs to be entered (column I). If you enter a different value here, the numbers in column K and column P will be updated accordingly. Showing the results for various numbers of kids simultaneously would have added a lot of additional permutations and would have made the sheet very large.
• The blue numbers are input variables and you can change them if you’d like to adjust the model. The brown numbers can be changed, too, but aren’t used as inputs for the calculation. To play around with the numbers please make a copy (File > Make a copy).

Knowing when to scale (and how to prove that you can do it)

When you’re talking to investors about a Series B, Series C or later round, one of the questions that will inevitably come up is “What are your CACs?”. It sounds like a simple question, but from the question of what costs to include and the right way to account for organic traffic to the pandora box of multi-touch attribution, there are lots of devils in the details.

What's more, the real question is not "What are your CACs?" but "What will your CACs be if you invest $10-20 million in sales & marketing?". It’s hard enough to calculate historic CACs for different acquisition channels with a high degree of accuracy. It’s much harder to predict future CACs at bigger scale.

And yet it shouldn’t come as a surprise that later-stage investors are so focused on this question. When you’re raising a Series B or later round, you’ve achieved Product/Market Fit (which is hard to define, see me attempt here) and you’ve got what Jason M. Lemkin calls “Initial Traction” and “Initial Scale”. At that point, the biggest thing standing between you and building a $100M+ business is finding scalable and profitable customer acquisition channels. Obviously you still have to overcome lots of other challenges along the way, but if you’re at $5-10M in ARR and you are confident that you’ve found scalable sales and marketing channels you are in an excellent (and rare) spot.

So how do you know if your customer acquisition channels will scale, that is, if a 10x increase of your sales and marketing spend will lead to a 10x increase in new customers? Consumer Internet startups are sometimes in the fortunate position to have found a profitable customer acquisition channel that offers huge potential for expansion. If ads on TV, YouTube or Facebook work for you, you might be able to increase your spending by 10x (and maybe much more) because these platforms have such a gigantic reach. In the B2B SaaS world this is very rare. Mass-market advertising won’t work because there’s way too much ad wastage, and targeted ads usually don’t give you the volume to easily 10x your spend.

Without a careful keyword volume analysis, being able to profitably spend $10k a month on AdWords doesn’t mean much in regards to your ability to spend $100k a month. If you spend small amounts on AdWords you will by definition (AKA by algorithm) capture the lowest-hanging fruits. As you’re trying to spend more, prices will go up. You might be able to offset the price increase by optimizing your campaigns, landing pages, onboarding, etc, but don’t take it as a given.

The underlying problem is that the existing “hot demand” for your product – people who are actively looking for a solution – is usually quite limited. The good news is that the amount of “lukewarm demand” – companies that would benefit from your product but aren’t aware of it yet – is usually much larger. That’s why content marketing is so critical in SaaS: it allows you to capture leads at a much earlier stage of the discovery process. But scaling up your content marketing by 10x is not as straightforward as simply 10x-ing your ad budget.

So how do you know, in B2B SaaS, if you’ve found scalable acquisition channels?

Nothing is completely certain here, but one great sign that should give you a lot of confidence is if you can hire new salespeople and the new hires (once they’re ramped up) are hitting their quota. If you add two AEs, add another two, and then another two, and most of them are hitting quota it shows that you’re able to increase the amount of high-quality leads. If that wasn’t the case, your growing sales team would quickly start fighting for the best leads and some of your salespeople wouldn’t be able to hit their quota any longer. Equally important, it also shows that you’ve managed to industrialize the sales process to a certain extent. Firstly, it doesn’t take the founders or superstar salespeople to sell your product, it can be sold by “normal” people. And second, you’ve managed to attract the right people, to set up the right processes and infrastructure and to create the right incentive structure and culture that is required to make a sales team successful.

Besides a growing, successful sales team, there are a few other factors that you can look at when you’re trying to decide if it’s time to put the pedal to the metal:

1. Are you able to make outbound sales work?
Doing outbound at reasonable CACs is usually very hard because you’re dealing with lots of unqualified leads. It requires lots of persistence from every AE and your sales leader as well as a strong commitment from the founders, since a serious attempt to make outbound work can cost a lot of money and time. The beauty of outbound sales is that if it works for you, you may have found a highly scalable customer acquisition channel: emailing or calling every single target customer in the world will keep your sales team busy for a while. :)

2. Have you managed to increase your SEM budget consistently and significantly without negative effect on CACs? What is your impression share, and how large is the search volume that you can still tap into?
As mentioned above, past performance in scaling an SEM budget from A to B alone is not a reliable indicator of future performance to scale from B to C. But in combination with a thorough analysis of the relevant search volume it can be a relevant data point.

3. Have you built a content marketing “machine” that consistently generates more leads month-over-month? 
If you can consistently increase inbound/content leads for some time, it means that you’ve found your narrative, or “North Star”; started to build content distribution channels; and managed to attract the right marketing people and make them effective. (Check out this great post from my colleague Clément for much more about this.)

If there are other aspects that you’re looking at to decide if you’re ready to scale, I’d love to hear about them in the comments below!

Thank you Rodrigo and Janis for reviewing a draft of this post and the valuable feedback.

A sneak peek into Point Nine's investment thesis

Over the last couple of weeks and months we spent some time putting our investment thesis on paper. The purpose of this exercise was to challenge and discuss our implicit assumptions and to get everyone on our team aligned on what kind of investments we seek.

One of the things that being very clear about our investment focus helps with is getting to “no” faster. If that sounds pessimistic, remember that we see thousands of potential investments every year but can only do 10-15 of them. Just like it’s crucial for sales teams to have clear qualification and disqualification criteria, it’s important for us to focus our time on “higher probability deals”. That means we’ll have to be able to quickly pass on a large number of deals that are likely not a good fit for us. Our “filter” is of course not perfect, so we’ll inevitably pass on lots of great companies, some of which will end up in our growing anti-portfolio – but there aren’t enough hours in the day to take a close look at each company that we see.

A fast decision process is also important for founders. As we’ve learned from this survey, being left in the dark is the single most important reason why fundraising often sucks for founders. We will obviously never be able to make decisions based on a simple algorithm, if only for the fact that the founding team remains the most important of all criteria. But anything that helps us streamline our decision making process is welcome.

Once the document is in a publishable form we will post it. Bear with us for a little while as we’re polishing the document a bit to make it more self-explanatory and to remove the worst typos. ;-) In the meantime, here’s a sneak preview.

We will continue to focus on two business models: SaaS and marketplaces

SaaS

• We use a broad definition of SaaS. Usually the first “S” stands for “software”, but sometimes it stands for “something”, e.g. a combination of software and hardware or software and data.
• We’re interested in horizontal and vertical SaaS. What counts is that the startup is aiming to solve a big enough problem for a large enough number of potential customers in order to build a big business. As a rule of thumb, we’re looking for markets that consist of at least 3,000 whales ($1M ACV), 30,000 elephants ($100k ACV), 300,000 deer ($10k ACV) or 3M rabbits ($1k ACV). ¹
• We’re equally interested in companies targeting SMBs (AKA rabbit and deer hunters) and companies targeting enterprises (AKA elephant and whale hunters). What’s important is the right founder/market fit. For companies targeting very small businesses (AKA mice and rabbit hunters) we want to see the potential for viral distribution.
• We’re looking for companies that we think can build a 10x better product and/or drive a paradigm shift in the industry. ²
• We want to invest in companies that can eventually build moat e.g. by becoming a system of record or a “system of intelligence”; by building a large data set that in combination with machine learning translates into a superior product; by building a platform; or by becoming a SaaS-enabled marketplace.
• With very few exceptions in areas like accounting, we’re looking for companies that have the potential to win the US market.
• We’re looking for SaaS companies that have the potential to get to $100M in ARR within 7-8 years and to $250-300M ARR within another 2-3 years.

Marketplaces

• Like in the case of SaaS, we use a broad definition for marketplaces. For us, a marketplace is a digital platform that brings two or more parties together and enables them to “transact”. The object of the transaction can be a physical product, a digital product, a service, or in some cases a piece of information or knowledge.
• We look for startups that leverage marketplace dynamics to create unique user experiences in fragmented markets, with the potential to develop a moat through network effects.
• We believe that marketplace platforms will continue to emerge in the most unexpected of places and in the most unexpected of forms. They will continue to transform entire industries.
• We are open to all of C2C, B2C, B2BC and other types of marketplaces. We are particularly excited about B2B marketplaces and “SaaS enabled marketplaces”.
• We are trying to identify platforms able to become international leaders. Thus, we will typically look for early proof of ability to operate in more than one country or globally.
• We are looking for early signs of liquidity. ³
• We look for founding teams with strong commercial sense.
• We think that blockchain technologies have the the potential to disrupt many marketplace models as we know them today; we will be exploring them in depth.
• We look for marketplaces that can become truly significant. In monetary terms, this means the potential to ultimately generate hundreds of millions of dollars in annual net revenues and billions in GMV.

Thanks for contributing this section, Pawel. Expect a follow-up post with more details from Pawel (who’s leading most of our marketplace investments) soon.

We will continue to invest in new areas and technologies that we like to dub “Frontier Tech”

• While we’re focused on two business models – SaaS and marketplaces – we’ll continue to keep our eyes wide open with respect to new technologies.
• We’re extremely interested in new opportunities in areas such as AI/ML, blockchain and cryptocurrencies, IoT and hardware-as-a-service, drones, or AR/VR. We have already made investments in most of these areas and will continue to do so.
• In many of these cases there are complex tech problems that must be solved. We’re happy take a certain level of technology risk, but at the same time we’re looking for founders who find ways to bring a product to the market quickly and cheaply.
• While a superior technology will usually be key to entering the market and have some early wins, most technologies will eventually be commoditized. Therefore we’re looking for additional sources of long-term defensibility such as high switching costs and large data sets (see the section on SaaS above) or network effects (see the section on marketplaces above).

Thanks to Mr. Frontier Tech Rodrigo for your help with this section, and looking forward to your follow-up post as well.

We will continue to focus on early-stage investments

• We’ll continue to focus on seed investments, investing anything from a few hundred thousand dollars up to around $2M in “seed” and “late seed” rounds, typically in companies that have strong indications of Product/Market Fit and promising early traction.
• We will continue to make what we call „founder bets“: Idea-stage investments into proven entrepreneurs from our close network. In these cases most of our „rules“ don’t apply. When people like Doreen Huber, Fabian Siegel, Iñigo Juantegui, Pan Katsukis, Sebastian Diemer or Stefan Smalla start something new, we want to be part of it. ⁴

We will continue to invest internationally

• Europe is our home market – we’ve made investments in most European countries and we’ll continue to invest all over Europe.
• Especially in SaaS we will continue to invest outside of Europe as well – e.g. in the US, Canada, Australia, New Zealand and other countries.
• In SaaS, our assumption is that you can start almost anywhere but you have to win globally (which requires winning the US). In marketplaces we want to find companies that can win several large markets.

We continue to aspire to be a “Good VC”

• We don’t pretend to be the right investor for every startup. But our aspiration is that if we do invest in a company, we’re the absolute best partner the founders can dream of and that we’ll play a significant role in helping the company get to the next stages.
• We’re optimizing for the long run in everything we do. You “always meet twice in life”, as the German saying goes.

_________________________

¹ Check out this post if you have no idea what I’m talking about. Then, get your poster.
² See Sarah Tavel’s post about “10x better and cheaper products” for a similar concept from the consumer Internet world.
³ Defining liquidity is tricky – a topic for another post!
⁴ True story – these are all guys who we backed or worked with closely before and who subsequently founded Lemoncat, Marley Spoon, OnTruck, Remerge, Finiata and Westwing, respectively.

WTF is PMF? (part 2 of 2)

In the first part of this post, I looked at what some of the most knowledgeable people in the industry said about Product/Market Fit (PMF) and how they try to define and measure it. While everybody seems to agree on the broad concept of PMF there is (unsurprisingly) no consensus on how exactly it can be defined and measured, and some people set the bar much higher than others. For example, according to Brad Feld you find PMF somewhere between $100k and $1M in MRR, while others argue that you can have PMF with much lower revenues.

In this part I’d like to talk a bit about my view on PMF and how we try to detect it when we look at SaaS startups at Point Nine. Here’s my favorite definition of PMF, inspired by many of the people mentioned in the first part of the post:

Product/Market Fit means having a product that solves a problem for a significant number of independent customers.

Note that this definition intentionally doesn’t say anything about market size. Lots of companies have PMF for a very small market, but addressing a small market is not a reason to deny a company its PMF.

If we talk about PMF for “VC cases”, i.e. the type of company venture capital investors are looking for, I would adjust the definition as follows:

Product/Market Fit means having a product that solves an important problem – without custom work and better than existing solutions – for a significant number of independent customers in a large market.

The next step in getting to a solid definition would be to define the pieces that this definition includes: How “important” is important enough, and how can it be measured? How much “better” is better enough, and how can it be measured? And so on.

There are no clear answers to these questions and – sorry – I don’t think there is a razor-sharp way of defining and measuring PMF. Some companies clearly have PMF, some clearly don’t. Others are somewhere in the middle – they have indications of PMF but it’s not clear if they will ever get to strong PMF. Most seed investments that we’re considering fall into the last bucket.

Here’s an overview of the most important factors that we’re looking at when we try to assess the degree of PMF of a SaaS company. In isolation, none of these factors can tell you if you have PMF or not. But taken together, it can hopefully give you at least a good indication:

This concludes my mini-series on Product/Market Fit (at least for now). Let me know if you have any feedback!

___________________________

1) For more background on the concept of rabbit/deer/elephant hunters, check out this post.
2) Take a look at this post to read more about "expected usage frequency".
3) This is from Sean Ellis’ test for PMF. More on this here.

WTF is PMF? (part 1 of 2)

I’ve been fascinated by the concept of Product/Market Fit for quite some time. The reason why it’s such an interesting and important concept is that getting to Product/Market Fit (PMF) marks a critical juncture in a company’s lifecycle. At least in theory, the life of a company can be divided into a “pre PMF” phase and a “post PMF” phase, with each of the two phases having very different objectives and requiring very different strategies. As Marc Andreessen famously said, “when you are before PMF, focus obsessively on getting to PMF”. Once you have PMF, you can start to focus on hiring, getting more customers, finding customer acquisition channels, optimizing pricing, and so on. In reality, there’s usually not a sharp line of demarcation that separates the “before” from the “after”. Rather, companies typically increase their level of PMF gradually.

The problem with PMF is that it’s hard to precisely define and even harder to measure. So difficult, in fact, that I’ve heard several people resort to the “I know it when I see it” phrase (famously used by a Supreme Court justice to define pornopgraphy). Think about it. We have the concept of a demarcation line which calls for different strategies “before” and “after”, but we don’t seem to have a precise definition of that concept, nor the tools to measure whether a company is “before” or “after”! To make things worse, according to data from a Startup Genome Report “premature scaling” (i.e. spending significant amounts of money on growth before you find PMF) is the #1 reason why startups fail!

Let’s look at what some of the smartest people in the industry have said and written about PMF.

1. What is Product/Market Fit?

• Paul Graham apparently said that PMF simply means “making stuff that people want” (I couldn’t find the original quote but saw it in this presentation).
• Marc Andreessen got more precise, saying that PMF means “being in a good market with a product that can satisfy that market”.
• Michael Skok added the important element of the “Minimum Viable Segment” in this article, pointing out that “your product isn’t going to fit the entire market from day one. Minimum Viable Segment (MVS) is about focusing on a market segment of potential customers who have the same needs to which you can align.”
• My dear colleague Clément Vouillon added another dimension – distribution – and defined PMF like this: “It happens when the product (a set of features that have a clear value proposition) resonates with customers (which are of a certain type and have defined needs) that you know how to reach and convert (through marketing and sales).”
• Andrew Chen has another interesting twist: PMF is “when people who know they want your product are happy with what you’re offering”.
• Last but not least, according to Eric Ries “The term product/market fit describes ‘the moment when a startup finally finds a widespread set of customers that resonate with its product”, and Andy Rachleff said: “You know you have fit if your product grows exponentially with no marketing.”

2. Is Product/Market Fit a discrete event, or is there a gradual development towards PMF?

• In his excellent talk at the great SaaStock conference in Dublin last fall, Peter Reinhardt, co-founder & CEO of Segment, explained how Segment, after struggling for a long time, suddenly got to PMF when they put up a landing page for what used to be a little side project. According to Peter, “product market fit is not vague, positive conversations with customers. It's not glimmers of false hope around some random positive interaction. What it actually feels like is a landmine going off”.
• According to Brad Feld and Ben Horowitz, Segment’s experience is the exception to the rule, though. According to Brad, PMF is something that you find somewhere between $100k and $1M MRR, and Ben has called PMF as a “discrete, big bang event” a “myth”.

3. How can Product/Market Fit be measured?

• As mentioned in the beginning, a lot of people would say you can’t measure PMF and that you “know it when you see it”. Sean Jacobsohn of Norwest Venture Partners took up the challenge and developed a 5-question quiz that you can use to rate your level of PMF. I like his approach a lot and turned the quiz into a little Typeform some time ago.

• Another quantitative approach comes from Sean Ellis:

“I ask existing users of a product how they would feel if they could no longer use the product. In my experience, achieving product/market fit requires at least 40% of users saying they would be “very disappointed” without your product. Admittedly this threshold is a bit arbitrary, but I defined it after comparing results across nearly 100 startups. Those that struggle for traction are always under 40%, while most that gain strong traction exceed 40%.” 

(taken from “The Startup Pyramid”)

I’m fascinated by Sean’s approach because it’s the most quantifiable way to detect PMF that I’ve seen so far. The question is whether the 40% threshold, which as Sean admits is a bit arbitrary, will continue to hold true with bigger sample sizes. I’m also wondering to what extent this “test” can be skewed by the type of users that you happened to attract. Nevertheless, it’s impressive that there seems to be a strong pattern among almost 100 startups.

• Andrew Chen offers a few examples of what PMF looks like. For a SaaS company, he mentions a few indicators, including these:
• 5% conversion rate from free to paid
• less than 2% monthly churn
• clear path to $100k MRR

• Brad Feld uses a pretty high bar for his definition of PFM. According to Brad, you don’t have PMF until you “blast through the $500k MRR mark and march to $1M MRR”.

In the second part of this post (which I’ll hopefully finish in a few days) I’ll talk a bit about my personal view on PMF and how we try to detect it when we look at SaaS startups at Point Nine. Don’t expect too much wisdom though, unsurprisingly we don’t have the ultimate answer to the PMF conundrum!

[Update: Here is part 2.]