Monday, October 13, 2014

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:


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!


Sunday, October 05, 2014

Five ways to build a $100 million business

Some time ago my friend (and co-investor in Clio, Jobber and Unbounce) Boris Wertz wrote a great blog post about "the only 2 ways to build a $100 million business". I'd like to expand on the topic and suggest that there are five ways to build a $100 million Internet company. This doesn't mean that I disagree with Boris' article. I think our views are pretty similar, and for the most part "my" five ways are just a slightly different and more granular look at Boris' two ways.

The way I look at it can be nicely illustrated in this way:



The y-axis shows the average revenue per account (ARPA) per year. In the x-axis you can see how many customers you need, for a given ARPA, to get to $100 million in annual revenues. Both axes use a logarithmic scale.


To build a Web company with $100 million in annual revenues*, you essentially need:

  • 1,000 enterprise customers paying you $100k+ per year each; or
  • 10,000 medium-sized companies paying you $10k+ per year each; or
  • 100,000 small businesses paying you $1k+ per year each; or
  • 1 million consumers or "prosumers" paying you $100+ per year each (or, in the case of eCommerce businesses, 1M customers generating $100+ in contribution margin** per year each); or
  • 10 million active consumers who you monetize at $10+ per year each by selling ads

Salespeople sometimes refer to "elephants", "deers" and "rabbits" when they talk about the first three categories of customers. To extend the metaphor to the 4th and 5th type of customer, let's call them "mice" and "flies". So how can you hunt 1,000 elephants, 10,000 deers, 100,000 rabbits, 1,000,000 mice or 10,000,000 flies? Let's take a look at it in reverse order.

Hunting flies

In order to get to 10 million active users you need roughly 100 million people who download your app or use your website. This is of course a gross simplification, and the precise number depends on various factors like your conversion rate, how active your users are, churn, etc. But it doesn't change the take-away: To get to $100 million in ad revenues, you need dozens of millions of users. I know of only two ways to achieve that (plus one mega-outlier which breaks all rules, Google). The first one is to have a product that is inherently social and has a high viral coefficient (Instagram, Snapchat, WhatsApp). The second one is a ton of UGC (user-generated content), which leads to large amounts of SEO traffic and some level of virality. Good examples of this second option include Yelp or our portfolio company Brainly.

Hunting mice

To acquire one million consumers or prosumers who pay you roughly $100 per year, you need to get at least 10-20 million people to try your application. This is – again – a gross simplification, but I believe it's order-of-magnitude correct. To get to 10-20 million users you almost certainly need some level of virality, too – maybe not Snapchat-like virality, but some social sharing or "powered by"-virality. Great examples of this category include Evernote and MailChimp. If you're an eCommerce business you might be able to acquire one million customers using paid marketing, but it requires huge amounts of funding.

Hunting rabbits

Most SaaS companies that target small businesses charge something around $50-100 per month, so their ARPA per year is around $1k. To acquire 100,000 of these businesses you need something in the order of 0.5-2 million trial signups, depending on your conversion rate. Let's assume that your CLTV (customer lifetime value) is $2,700 (assuming an average customer lifetime of three years and a gross margin of 90%) and that you want your CLTV to be 4x your CACs (customer acquisition costs). In that case you can spend $675 to acquire a customer. If your signup-to-paying conversion rate is 10% that means you can spend $67.50 per signup (assuming a no-touch sales model where your CACs can go entirely into lead generation).

So how can you get one million signups for less than $70 each? Most SaaS products aren't inherently viral, there usually isn't enough inventory to make paid advertising work at scale, and cold calling usually doesn't work at this ARPA level. There's no silver bullet, but the closest thing to a silver bullet is inbound marketing – besides having a fantastic product with a very high NPS (net promoter score) and being obsessively focused on funnel optimization. I've written about this in more detail in my "DOs for SaaS startups" series: Create an awesome product, Make your website your best marketing person, Fill the funnel, Build a repeatable sales process. Another option is a an OEM strategy (i.e. getting your product distributed by big partners), which can work but comes with its own challenges.

Interestingly, hunting rabbits looks much less straightforward than hunting flies or hunting elephants. Why we have a strong focus on rabbit hunting SaaS companies nonetheless is something for another post.

Hunting deers

If you're a deer hunter and want to acquire 10,000 customers paying you $10k per year each, most of the rabbit hunting tactics still apply. An ARPA of $10k per year usually isn't enough to make traditional enterprise field sales work, and you likely still have to get 100,000 or more leads. The main difference is that when you're hunting deers you can use an inside sales force to close leads, potentially also to generate leads. It also means that you can pay VARs and channel partners an attractive commission, although I've rarely seen this work in SaaS.

SaaS companies sometimes start as rabbit hunters and expand into deer hunting over time. This can work very well and we're very excited about these types of businesses, but to successfully execute this strategy, SaaS founders with a product/tech/marketing DNA usually have to bring in an experienced VP of Sales who has built an inside sales organization before.

Hunting elephants

Like it or not, most of the biggest SaaS companies derive most of their revenues from selling expensive subscriptions to large enterprises. Workday, Veeva, SuccessFactors, Salesforce.com, you name it. Jason M. Lemkin, another friend and co-investor, once said (I'm quoting from memory) that if you have a good solution for a significant problem experienced by large enterprises, building a $100 million business is relatively straightforward. After all, you only need 1,000 customers, and the $100k you need from each of them is less than they spend on the salary of one executive. I think there's a lot of truth in that.

The other part of the truth, though, is that it may take you several years and millions of dollars to find out if you really are solving a problem (a.k.a. product/market fit), and once you're at that point, you still need tens of millions of dollars or more to finance the enterprise sales cycle. This does not at all mean that elephant hunting isn't attractive. It just requires very different skills, which usually means a founder team with enterprise sales DNA.

That leaves me with the million dollar – sorry, one hundred million dollar – question: Which other ways to build a $100 million business are there that I've overlooked? Let me know!


___________________________________

* If you have $100 million in annual high-margin revenue, you will likely be able to exit for $500 million to $1 billion or more. That's the kind of exit most venture capitalists are looking for, although we as a small fund can achieve a great fund performance with somewhat lower outcomes. 

** For eCommerce companies, which naturally have a much lower contribution margin than purely digital businesses like SaaS and are therefore valued at much lower revenue multiples, it makes more sense to target $100M in contribution margin.



Tuesday, September 16, 2014

3 Reasons We're in a Bubble. And 3 Reasons We're Not.

In a Wall Street Journal interview that was published yesterday, Bill Gurley, General Partner at Benchmark and one of the smartest and most successful VCs of all time, said that the current environment reminds him of the tech bubble of the late 1990s:
“Every incremental day that goes past I have this feeling a little bit more. I think that Silicon Valley as a whole or that the venture-capital community or startup community is taking on an excessive amount of risk right now. Unprecedented since ‘'99. In some ways less silly than '99 and in other ways more silly than in '99.”
The full interview is behind WSJ’s paywall, but here’s a summary.

So – are we in a tech bubble? Trying to answer that question could easily turn into a book because there are so many aspects to consider, but let me try give you three reasons why I think we’re in a tech bubble – and three reasons why I think we’re not.

Three reasons we’re in a tech bubble

1) So-called unicorns and companies believed to become unicorns can raise as much money as they want at extremely high valuations. While it’s perfectly rational for large growth funds to do everything they can to invest in one of the few companies that get big enough to return their funds, my impression is that there’s too much money chasing too few “certain” winners. 

2) In Silicon Valley, competition for seed and early-stage investments is so fierce that deals are done at mind-blowing speed and ever-increasing valuations. Some years ago, YC startups used to raise seed rounds with a $4M cap. About two years ago, $8M became the new $4M, and it seems like the new standard is now $12M. While this might be a reasonable valuation for some startups, if most startups are raising seed rounds at double-digit valuations I believe it shows that investors are getting increasingly oblivious to risk. 

3) The competition for talent in Silicon Valley is getting tougher and tougher, and what startups do (and maybe have to do) to attract people and get mindshare is sometimes starting to feel crazy. I’ve heard of startups renting prime office space in the best locations of San Francisco ... because of the foot traffic. This may or may not be a smart move by the entrepreneurs (and it’s in the nature of bubbles that rational decisions of individuals lead to an irrational outcome), but it sure makes my bubble alarm antennas vibrate. :)

Three reasons why we’re not in a bubble:

1) The amount of venture capital flowing into Internet startups is significantly below 1999/2000 levels. According to data from the NVCA and pwc MoneyTree, VCs invested about $23.8B and $41.8B in Internet companies in 1999 and 2000, respectively. In 2013, that number was $7B and in the first half of 2014 it reached $4.9B. 

2) There’s no IPO bubble. Back in the 1990s, everyone and his dog was buying Internet stocks. Companies with negligible revenues went public and reached market caps of billions of dollars. Nothing even remotely close is happening today. Today, mature companies with tens of millions of dollars in revenues and strong market positions go public. Whether the stock market will go up or down by 20-50% in the next 1-2 years I have no idea, but we certainly won’t see dozens of public Internet companies go bankrupt. 

3) Outside of the SF Bay area I don’t see many signs of a bubble. As far as Europe goes, there aren’t that many angel investors or VCs in Europe and most of the US-based VCs don’t invest in European startups. As a result, raising money is still quite tough for most European entrepreneurs, across pretty much all stages.

So are we in a bubble or not? With respect to VC investments in the Bay area I would say “yes”, but that doesn’t mean that it has to burst any time soon, especially if you keep in mind how far we’re away from a 1999/2000-like situation. As far as Europe is concerned I say: No, non, nej, ei, ohi, nē, nee and nein.


Friday, August 08, 2014

Does your SaaS startup have product/market fit?

Product/market fit is a topic that I've touched on a few times on this blog. It's that extremely crucial but somewhat hard to define (and even harder to measure) step which every startup needs to cross as it goes from an idea to a product to a real, scalable business. It's also a very important concept for us at Point Nine Capital since we tend to look for some level of proof of product/market fit when we evaluate potential investments.

Sean Jacobsohn of Emergence Capital has just published an excellent post titled "Here’s how to find out if your cloud startup has product-market fit". It's easy to fool yourself into thinking that you've found product/market fit, and Sean's post mentions some of the most important of these pitfalls. "All my customers are fellow startups in my incubator class" might be an obvious one, but there are also less obvious ones. :-)

I like Sean's article so much that I've turned it into a Typeform

So, if you're curious how your SaaS startup is doing in terms of product/market fit on a scale of 5-25, answer these five questions!


Sunday, July 27, 2014

A/B testing is like sex at high school

A few days ago I went on record saying that A/B testing is like sex at high school. Everyone talks about it, not very many do it in earnest. I want to follow up on the topic with some additional thoughts (don't worry, I won't stretch the high school analogy any further).

When talking to people about A/B testing I've noticed that there are four (stereo) types of mindsets which prevent companies from successfully using split tests as a tool to improve their conversion funnel.

1) Procrastinative

The favorite answer to suggestions for website or product improvements from people from this camp is "we'll have to A/B test that" – as in "we should A/B test that, some time, when we've added A/B testing capability". It is often used as an excuse for brushing off ideas for improvement, and the fallacy here is that just because the best way to test assumptions is an A/B test doesn't mean that all assumptions are equally good or likely to be true.

Yes, A/B tests are the best way to test product improvements. But if you're not ready for A/B testing yet, that shouldn't stop you from improving your product based on your opinions and instincts.

2) Naive 

People from this group draw conclusions based on data which isn't conclusive. I've seen this several times: Results are not statistically significant, A and B didn't get the same type of traffic, A and B were tested sequentially as opposed to simultaneously, only a small part of the conversion funnel was taken into account – these and all kinds of other methodological errors can lead to erroneous conclusions.

Making decisions based on gut feelings as opposed to data isn't great, but in this case at least you know what you don't know. Making decisions based on wrong data – thinking that you understand something which you actually don't – is much worse.

3) Opinionated

There's a school of thought among designers which says that A/B testing lets you find local maxima only. While I completely agree with my friend Nikos Moraitakis that iterative improvement is no substitute for creativity, I don't see a reason why A/B testing can't be used to test radically different designs, too. 

Designers have to be opinionated. Chances are that out of the 1000s of ideas that you'd like to test, you can only test a handful because the number of statistically significant tests that you can run is limited by your visitor and signup volume. You need talented and convinced designers to tell you which five ideas out of the 1000s are worth a shot. But then do A/B test these five ideas.

4) Disillusioned

The more you learn about topics like A/B testing and marketing attribution analysis, the more you realize how complicated things are and how hard it is to get conclusive, actionable data. 

If you want to test different signup pages for a SaaS product, for example, it's not enough to look at the visitor-to-signup conversion rate. What matters is the entire funnel conversion rate, starting from visitors all through the way to paying customers. It's well possible that the signup page which performs best in terms of visitor-to-signup rate (maybe one which asks the user for minimal data input only) leads to a lower signup-to-paying conversion rate (because signups are less pre-qualified) and that another version of your signup page has a better overall visitor-to-paying conversion. To take that even further, it doesn't stop at the signup-to-paying conversion step as you'll want to track the churn rate of the "A" cohort vs. "B" cohort over time.

If you think about complexities like this, it's easy to give up and conclude that it's not worth the effort. I can relate to that because as mentioned above, nothing is worse than making decisions which you think are data-driven but which actually are not. Nonetheless I recommend that you do use split testing to test potential improvements of your conversion funnel – just know the limitations and be very diligent when you draw conclusions.

What do you think? Did you already fall prey to (or see other people fall prey to) one of the fallacies above? Let me know!



Friday, June 13, 2014

Uber's Wonderlamp

Uber's uber large funding round has been the talk of the day in the tech community in the last week. And it should be, since it doesn't happen very often that a four year old company raises $1.2B at a $17B valuation. In fact, according to this Bloomberg story, Uber's new valuation sets a record for investments into privately-held tech startups.

When I first heard about Uber a few years ago, I didn't quite get it in the beginning. The traditional taxi system works quite well in Germany, and I thought that the advantage of using an app to order a cab as opposed to making a quick call wasn't such a big deal. Also, the expensive "private limo" service, which Uber started with in the beginning, didn't appeal to me.

After using mytaxi in Germany, I started to like the idea, but it was the launch of UberX and my recent two-months stay in San Francisco which turned me into a huge Uber fan. What is it that makes Uber so compelling? It's a number of smaller and bigger factors, which, combined with a slick mobile app, make Uber a highly habit-forming service:

  • Speed: In San Francisco, Uber has such a large number of drivers that no matter where you are in the city, it rarely takes more than 5-10 minutes until your car arrives. It happened to me several times that "my" Uber arrived in less than a minute because a driver was just around the corner, which gives you an Aladdin's wonderlamp feeling: You hit the order button on your phone, and almost instantly a car shows up to pick you up. 
  • Transparency: You get an ETA and you can watch your car on the map as it's getting closer to you, so you know pretty exactly when your car will arrive.
  • Price: The company's budget option, UberX, is cheaper than normal taxis.
  • Convenience: The fact that you only have to enter your credit card once makes the payment process extremely convenient and saves you a lot of time every time you arrive at your destination. Related to that, Uber has constructed its business model in such a way that the drivers aren't allowed to take tips, so you don't have to think about how much tip to give. That leads to another almost magical experience – you arrive at your destination and off you go. No waiting for your credit card to be processed or for the driver to look for change. You don't have to worry about getting a receipt neither, since a receipt is emailed to you after the ride. The driver stops and 5 seconds later you're out of the car. Brilliant.

Last but not least, virtually all of the drivers I drove with were very friendly and courteous. Maybe that was just professional friendliness in some cases, but my feeling was that almost all of them were very happy working for Uber and were genuinely trying to provide a great service (besides making sure that they maintain a great rating).

So Uber is great for riders, and based on what I know, it's good for the drivers, too. But is it also a great business? I think so. If a company delivers so much value to both sides of a marketplace, it can take a significant cut and acquire buyers and sellers profitably. I also think that although driver and rider loyalty might not be huge in principal (as this WSJ piece suggests), Uber will be able to create significant moat around its business through network effects and the building of its brand.

If Uber manages to sign up more and more drivers in an area (something which I don't doubt they'll be able to do), those magical moments which I described above – where your car arrives almost instantly – will occur more and more frequently. Competitors with less driver density won't be able to deliver the same level of uber user experience. In theory, an extremely well-funded competitor might be able to attack one of Uber's markets by offering both drivers and riders a much better deal. In practice that will be very, very difficult given Uber's lead and the quality of its execution. And the fact that Uber has now more than a billion dollars in its war chest won't make it easier.

Is Uber worth $17B? I don't know enough about the company to judge that, but what's clear is that Uber has a very realistic chance to revolutionize the worldwide taxi industry. What's more, Uber's long-term vision is much bigger. As Travis Kalanick puts it, they want to make "car ownership a thing of the  past", and my guess is they'll try to disrupt a few other industries (such as last-mile delivery) along the way. Huge congrats to Bill Gurley and his partners at Benchmark for betting on Uber early!



Thursday, June 05, 2014

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

Note: This is the second part of a post which first appeared on KISSmetrics' blog. The first part is here, and here is the original guest post on the KISSmetrics blog. Thanks go to Bill Macaitis, CMO at Zendesk, for providing extremely valuable input on multi-attribution analysis.

Multi-touch Attribution Analysis – Giving Some Credit to the “Assist”

Multi-touch attribution, as defined in this good and detailed post, is “the process of understanding and assigning credit to marketing channels that eventually lead to conversions. An attribution model is a set of rules that determine how credit for conversions should be attributed to various touch points in conversion paths.”

It’s easier than it sounds, and, since this is the year of the World Cup, let me explain it using a soccer analogy. Multi-touch attribution gives the credit for a goal to not only the scorer but also gives some credit to the players who prepared the goal. Soccer player statistics often calculate scores based on the goals and the assists of the players. That means the statistics are based on what could be called a double-touch analysis that takes into account the last touch and the touch before the last one.

Since the default model in marketing still seems to be “last touch” only, it looks like soccer has overtaken marketing in terms of analytical sophistication. :-)

Time for Marketing to Strike Back!

If you are evaluating the performance of a marketing campaign solely based on the number of conversions, you are missing a large piece of the picture. Like a great midfielder who doesn’t score many goals himself but prepares goals for the strikers, a marketing channel might not be delivering many conversions but could be playing an important role in initiating the conversion process or assisting in the eventual conversion.

This is especially true for B2B SaaS where sales cycles are much longer than in, say, consumer e-commerce. When you’re selling a SaaS solution to a business customer, it’s not unusual for there to be several touch points before a company becomes a qualified lead, and then many more before the lead becomes a paying customer. The process could easily look like this:

  • A piece of content that you produced comes up as an organic search result and the searcher clicks on it
  • A few days later, the person who looked at the content piece sees a retargeting ad
  • A few days later, she sees another retargeting ad, visits your website, and signs up for your newsletter
  • A week after that, she clicks on a link in your newsletter
  • A few days later, she receives an invitation to a webinar, signs up for it, and attends the webinar
  • After the webinar, she signs up for a trial
  • The next day, one of your customer advocates gives her a call
  • Close to the end of her trial, your lead does some more research, happens to click on one of your AdWords ads, and signs up for a paid subscription

If you look at this conversion path, it becomes clear that if you attribute the customer only to the first touch point (SEO) or to the last one (PPC), you’ll draw incorrect conclusions. And keep in mind that the example above is still quite simple. In reality, the number of marketing channels and touch points that contribute to a conversion can be much higher.

Data Integration in a Multi-device World

Maybe you use Google Analytics or KISSmetrics for Web analytics, Salesforce.com for CRM, and Zendesk for customer service. If you want to get a (more or less) complete picture of your user’s journey, you need to get and integrate the data from all of the major tools you’re using and track user interactions.

A big complicating factor here is that we now live in a “multi-device world”. It’s very possible that the person in the example conversion path above used a tablet device, a smartphone, and two different computers to access your content and visit your website. Since tracking cookies are tied to one device, there’s no simple way to know that all of these touch points belong to the same person, at least not until the person registers.

Going deeper into the data integration and multi-device attribution problem would go beyond the scope of this post, but there’s a lot of valuable information available on the Web. And, please feel free to ask questions or share experiences in the comments section.

Toward a Better Attribution Model

The next question to tackle is how credit should be distributed to touch points in a conversion path. A simple approach is to use one of these rules:

  • Linear attribution – Each interaction gets equal credit
  • Time decay – More recent interactions get more credit than older ones
  • Position based – For example, 40% credit goes to the first interaction, 40% to the last one, and 20% to the ones in the middle

While using one of these rules is a big improvement over a “first touch only” or “last touch only” model, the problem is that all of the rules are based on assumptions as opposed to real data. If you’re using “linear attribution,” you’re saying “I don’t know how much credit each touch point should get, so let’s give each one equal credit.” If you’re using “time decay” or “position based,” you’re making an assumption that some touch points are more valuable than others, but whether that assumption is true is not certain.

A more sophisticated approach is to use a tool like Convertro, which takes a look at all touch points of all users (including those who didn’t convert!) and then uses a statistical algorithm to distribute attribution credit. The advantage of this approach is that the model gets continuously adjusted based on new incoming data. Explaining exactly how it works, again, would go beyond the scope of this post, but there’s more information available on Convertro’s website, and I assume there are additional tools like this on the market.

Is It Worth It?

Implementing a sophisticated multi-touch attribution model is obviously a large project, and so the next question is whether it’s worth it. The answer depends mainly on these variables:

  • Product complexity and sales cycle – The more complex your product and the longer the sales cycle, the more likely you are to have several touch points before a conversion happens
  • Number of simultaneous campaigns and size of marketing budget – The more campaigns you’re running in parallel and the more you’re spending on marketing, the more important it is to account for multi-touch attribution

While cohort analysis is something you should do as soon as you launch your product, I think multi-touch attribution analysis can usually wait until you’re spending larger amounts of money on advertising. Until then, spending too much money or time getting your attribution model right probably is not the best use of your resources. So, as an early-stage SaaS startup, don’t worry too much about it just yet. Just remember to take your single-touch attribution CACs with a grain of salt.


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