2014 in the numbers – fun stats from the #P9Family

It's that time of the year again, the blogosphere is full of reviews of the year that is coming to a close and predictions for the coming year. When it comes to predictions, I agree with Niels Bohr (or Mark Twain or various other people who the quote got attributed to): Prediction is difficult, especially about the future. Seriously, as Paul Graham just wrote in his latest essay, change is notoriously (and tautologically) hard to predict.

So let me take the safer path, take a look back at 2014 and show you some stats from the Point Nine family of startups. Some are true KPIs, others are from the fun/vanity metrics department – but I believe all of them are impressive and inspiring. Enormous gratitude goes to all the extremely hard-working and talented people in the #P9Family. You rocked this year (and not only this year)!

(If you're reading this post in an email client or RSS reader, the infographic below might not display correctly. In that case please go to the Web version.)

Introducing: The One-Slide Update Deck

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

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

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

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

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

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

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

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

A toast to all the great ones that we've missed

Picture taken by "nlmAdestiny"

One of the things that inevitably happens when you're in the angel or VC investing business for a couple of years is that besides a hopefully healthy portfolio, you're also building a growing anti-portfolio. As far as I know, the term "anti-portfolio" has been coined by Bessemer. Its meaning is described very well on Bessemer's website, and because it's so hilarious I want to quote it in its entirety:

"Bessemer Venture Partners is perhaps the nation's oldest venture capital firm, carrying on an unbroken practice of venture capital investing that stretches back to 1911. This long and storied history has afforded our firm an unparalleled number of opportunities to completely screw up.

Over the course of our history, we did invest in a wig company, a french-fry company, and the Lahaina, Ka'anapali & Pacific Railroad. However, we chose to decline these investments, each of which we had the opportunity to invest in, and each of which later blossomed into a tremendously successful company.

Our reasons for passing on these investments varied. In some cases, we were making a conscious act of generosity to another, younger venture firm, down on their luck, who we felt could really use a billion dollars in gains. In other cases, our partners had already run out of spaces on the year's Schedule D and feared that another entry would require them to attach a separate sheet.

Whatever the reason, we would like to honor these companies -- our "anti-portfolio" -- whose phenomenal success inspires us in our ongoing endeavors to build growing businesses. Or, to put it another way: if we had invested in any of these companies, we might not still be working."

What follows is a list of spectacularly successful companies which Bessemer saw and passed on, including Apple, eBay, FedEx, Google, Intel and others. (No need to send CARE packages to the guys at Bessemer though, they have more than 100 (!) IPOs under their belts).

I'm a big fan of dealing with failures openly, and I applaud Bessemer for being so open about their anti-portfolio. In the next version of our (meanwhile pretty outdated) website we should add a section about Point Nine's biggest misses, but let me already give you a sneak preview into my personal anti-portfolio:

The two "passes" which I regret the most are SoundCloud and TransferWise. The reason why these two ones stand out is that I had the opportunity to invest in them (at an early stage and at reasonable terms), spent some time looking at them and decided to pass. Since then, both SoundCloud and TransferWise have become "unicorns" or are on their way getting there. Congrats to the founders and early investors of these fantastic companies – Alexander, Eric, Christophe and Jan (SoundCloud) and Taveet, SeedCamp and Index (TransferWise)!

Another unicorn that we rejected is FanDuel. Congrats team FanDuel, Fabrice, Andrin!

As far as I know, these three are the only $1B-valuation companies that we've missed so far, but there are several other companies that we passed on and which are doing great. Most of these are probably worth well over $100M by now and they include:

• Wanelo
• Intercom
• Lovoo
• EyeEm
• Simply Measured
• OneLogin
• NewsCred (thanks for the pointer and congrats, Shafqat!)

The reasons for passing an all of these great companies varied and included concerns about market size, competition, defensibility, valuation ... all bullshit with the benefit of hindsight. :-) While I am of course trying to learn from all of these mistakes, I also know that it's inevitable that my anti-portfolio will continue to grow over time. And although that can hurt, I know that that is okay – at least as long as we're happy with our non-anti-portfolio.

Reflections on the early days at Zendesk (part 2)

This is part two of my post about the early days at Zendesk. The first part is here.

Small, fragmented and no potential for differentiation

As mentioned in the first part of this post, the seed round was only $500,000 and it was clear that we’d need much more money soon. That’s why Mikkel and I started to work on a pitch deck and a financial plan almost immediately after the closing of the seed round and started to pitch to VCs shortly thereafter.
In my personal experience as a founder, raising money has never been easy, and so I didn’t expect that it would be easy. I was quite optimistic though, since I thought we had a pretty good pitch: a well-rounded team of three complementary and experienced founders, a beautiful product, a proven business model, paying customers and nice (yet early) traction.

So why did all European VCs pass? I’m getting asked this question a lot and I don’t have a perfect answer, but here are a few important factors:

• There just weren’t (and still aren’t) that many VCs in Europe who can write a Series A check. If a couple of them pass for whatever reason, you’ve quickly exhausted your available options.
• Our timing was horrible – it was almost at the height of the global financial crisis which had started in 2007. While we were trying to raise the Series A, Lehman Brothers imploded and a collapse of the entire global financial markets seemed possible.
• We had failed to convince investors that we were going after a large market and that we could build a defensible position. One feedback that we got was that the market for help desk software is “small and fragmented” and that there are concerns about the “potential for differentiation” and several other VCs were concerned about the size of the opportunity and our ability to differentiate, too.

You’ll notice that I haven’t mentioned the “European VCs are risk-averse/dumb/whatever” theme to explain why we haven’t been able to raise money in Europe. While I do think that there are differences between how VCs work in Europe vs. the US, I think it wouldn’t be fair to blame European investors for missing Zendesk: With hindsight Zendesk looks like a clear winner, but back in 2008 it wasn’t that clear. It was still very early.

At a critical juncture

A few months later, after having talked to a number of US investors and and after an almost-deal with a West Coast VC which was pulled back at the last minute, we eventually got an offer from CRV in Boston. We were relieved, but the valuation was much lower than what we had hoped for.

Because of the dilution which the investment round would mean and because the whole fundraising process has been so hard, Morten and Alexander got more and more doubts if going the VC route was the right thing to do at all. They were wondering if we couldn’t go the 37signals way instead – stay a smaller team, grow organically and maybe raise money at a later point in time when we’d be in a stronger position and when the market conditions would be more favorable. That was definitely a viable alternative and worth considering, but Mikkel and I strongly believed that we had to raise money and that we shouldn’t wait. This led to a lot of long emails and Skype discussions between the four of us. It also led to some very heated discussions between Mikkel, Morten and Alexander, which is no surprise, given how much was at stake. We were at a critical juncture.

One relic from those days is this email snippet (Alex in red, me in green):

I still need to buy Alex a T-Shirt with “I’m not confident that Zendesk can grow into a $100 million company” on it.

In the end we decided to take the investment from CRV, but we took a smaller amount than what Devdutt had offered us to reduce the dilution. It was still a significant hit in terms of dilution, but given how many doors the CRV investment has opened for us and how much Devdutt has done for the company it proved to be the right decision.

The rest is history – get Mikkel’s book to read about it!

Reflections on the early days at Zendesk (part 1)

Yesterday I posted a brief review of Mikkel’s excellent book “Startupland”. For me, the book is also a good opportunity for some reflections and to share some thoughts in relation to Zendesk’s journey.

The first date

When I stumbled on Zendesk in 2008 I knew absolutely nothing about enterprise software, B2B or SaaS. I had always been a consumer Internet guy, having founded comparison shopping engine DealPilot.com back in 1997 and personalized homepage Pageflakes in 2005. If Zendesk’s website hadn’t been so beautiful and if the product hadn’t been so easy to try and use, Zendesk would never have caught my attention (and I wouldn’t be writing this post now). The nice little buddha, the logo/brand and the tone of voice of the site also helped, massively.

Interestingly, if I had been an enterprise software investor, Zendesk probably wouldn’t have caught my attention either, since the website didn’t look like a typical enterprise software website at all. Today the “consumerization of the enterprise” has become mainstream, but in 2008 it wasn’t. Apparently you had to be a consumer Internet entrepreneur looking for the next big thing on the Web in order to stumble on and be attracted by Zendesk. This characteristic – not being a consumer Internet startup but not being a classical enterprise software company either – has probably contributed to our difficulty raising a Series A later on, but more on that later.

So when Mikkel and I met for the first time, I knew nothing about SaaS and probably asked a lot of dumb questions. At that I also knew nothing about inbound marketing and customer success – topics which are now near and dear to my heart for some years – and I was somewhat puzzled when Mikkel explained to me how they’ve been getting customers. I was worried that the inbound marketing plus customer success (at that time, called “customer advocacy”) approach wouldn’t scale and thought that they’d have to do outbound sales soon to keep growing. That turned out to be epically wrong: Zendesk grew to 10,000 paying customers before starting to build a real sales team, and up until this day, the vast majority of customers come from organic sources.

Having been an entrepreneur since the age of 17 I did know a few things about starting and building companies though, and since both DealPilot.com and Pageflakes were VC-funded I also had some experience with venture capital. So Mikkel and I were very complementary, or, as Mikkel puts it in the book:

There was a good vibe between us, even though we were extremely different. […] Ultimately, I think we recognized that we were a good balance for one another.

I remember that a couple of years later, at the first PNC SaaS Founder Meetup in San Francisco in 2012, Mikkel ended his speech saying something along the lines of: “Kudos to Christoph for investing in us back in 2008 – I would never have invested in these three guys”, referring to his co-founders Morten, Alexander and himself. My response was: “Kudos to you for taking money from me – I never would have taken money from me”. I think there’s no better way to sum it up. :-)

After the financing is before the financing

Following our first meeting, we very quickly concluded that it would make sense to work together, agreed on the terms, and voilà, a six-figure dollar amount changed hands. I was excited, but it was also a little bit scary because it was my first angel investment (aside from a few small investments that I had made many years earlier). I didn’t have a diversified portfolio, and I didn’t know if I’d ever have one because I had no idea when I’d make my second investment. I didn’t have deal-flow, and I’m not even sure if I knew the term deal-flow.

I didn’t worry too much about it though, and the mood was good. Quoting Mikkel from the book:

We now had a new direction. The investment from this seed round inspired a new mindset and created a big change in pace. Christoph helped us with a business plan and helped us build out what would be the first attempt at describing the financial model of our business. [...] He helped us think about scale—and about the possibilities.

The seed round, including the friends & family investments and my own investment, was only $500,000 though. It was enough for the founders to take a modest paycheck and to hire a few people, but it was clear that we’d need a much larger round soon. That’s when things started to become worrisome for me, since it quickly became clear that raising a Series A round would be very difficult.

This was the first part. Part two coming soon.
[Update: Here is part two.]

Startupland – How three guys risked everything to turn an idea into a global business

As some of you may know, my friend Mikkel, founder and CEO of Zendesk, wrote a book. It’s called “Startupland: How Three Guys Risked Everything to Turn an Idea into a Global Business” and you can learn more about it here. The hardcover version will be released in about two weeks, but the Kindle version just became available on Amazon and I was lucky enough to get my hands on a draft a few weeks ago.

The book is a well-written and very personal look back at Zendesk’s amazing success story, which began in a loft in Copenhagen and culminated in the company’s Wall Street IPO earlier this year. It’s both autobiography and “tips & tricks" guide: First and foremost it’s a suspenseful chronicle of the journey of Mikkel and his co-founders Alex and Morten that lets you witness some of the many ups and downs which startupland has in store for entrepreneurs, but it also contains a lot of actionable advice for other founders.

It’s an entertaining read, too, and as someone who was fortunate enough to have played a small role in Zendesk’s beginnings, reading about those early days put a smile on my face many times. In some cases, it also made me laugh out loud, e.g. when Mikkel writes about my conversation with Michael Arrington.

One of the reasons why "Startupland" is such a great read is that it’s honest and humble. When other authors write things like “I didn’t know anything about XYZ” it often feels like fishing for compliments. When Mikkel writes it, you know that he really means it that way.

I highly recommend the book to any startup founder, and in particular to all founders from Europe who consider making the move to the US.

When deers morph into elephants, SaaS nirvana is nigh

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

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

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

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

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

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

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

Three more ways to build a $100 million business

It seems like my recent post about five ways to build a $100 million business resonated very well with a lot of people. I also got some really good comments and suggestions, and so I'd like to follow-up with another post on the topic.

Introducing: the Brontosaurus!

A reader by the name of "Vonsydow" commented that another way to get to $100 million is by having 100 customers, each paying you $1 million per year, and mentioned Veeva as an example. True! Veeva's ACV is around $780,000. That's almost an order of magnitude higher than the $100,000 ACV of the "elephants" category, so it's a different kind of animal. I'd suggest that we call Veeva's customers Brontosaurus (or Apatosaurus, which seems to be the correct name) but I'm open to other suggestions by people who know more about biology (or paleontology) than me.

Interestingly, it seems like there are only two Brontosaurus hunters in the SaaS world, Veeva and Workday*. What does that mean for SaaS entrepreneurs? Take a look at the backgrounds of the founders of Veeva and the founders of Workday. If your background looks similar – 20+ years of experience selling enterprise software, domain expertise and an extremely strong network in your target industry – get into the Brontosaurus hunting business. If you don't have a background like this, I think it's likely that you're better off starting with smaller animals (but I'd be happy to be proven wrong!).

Whale hunting?

Whale hunting is not the best topic for jokes, but if you know me (who has become a vegetarian a few years ago) you know that I can only mean this figuratively. And the category that I'm going to talk about now just has to be named after the blue whale, the largest animal ever known to have lived on Earth. I'm talking about companies with an ACV of $10 million. If you can sell a SaaS solution at an ARPA of $10 million per year, you need only ten customers and bada-bing, you've got a unicorn.

Does that make it easy? Of course not. I'm aware of only one SaaS company which might have an ACV in the neighborhood of $10 million: Palantir, as pointed out by Jindou Lee. In his excellent book "Zero to One", Peter Thiel writes that Palantir's "deal sizes range from $1 million to $100 million". I don't know if these amounts refer to the price of an annual subscription and I don't know which part of it is non-SaaS revenue, but it sounds like Palantir's ACV could be in the $10 million ballpark. Either way, the conclusion along the lines of the conclusion of the Brontosaurus category is: If you're Peter Thiel, hunt whales. If not, chances are that you should start at a lower end of the market.

Hunting microbes

I'd like to add another species at the other end of the spectrum, too. Jeff Judge pointed out that WhatsApp monetizes its users at about $0.06-$0.07 per active user per year. That means that even if Facebook increases monetization by a factor of ~15 (which I'm sure they can do if they want to) and reaches $1 per active user per year, that's still an order of magnitude below the $10 per active user per year that I've described in the "flies" category, so another category is justified: microbes. If you're making only $1 per active user per year, you need 100 million active users to build a $100 million business. That means you'll need hundreds of millions of downloads or signups, which requires an insanely high viral coefficient. If it happens, awesome, but hard to bet on it in advance.

With that, here's the updated chart, which now shows eight ways to build a $100M business:

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.

PS: One of my best childhood friends saw my post, and I don't want to withhold from you what he wrote me: "Mathematically, there are many more ways to build a $100 million business. The easiest one is to start with a $200 million business and lose $100 million".


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* Salesforce.com has a number of Brontosaurus as well as some whale customers. As far as I know, with few exceptions these customers were acquired at a time when Salesforce.com was a $100 million business already. Since this post is focused on ways to build a $100 million business in the first place, I haven't included Salesforce.com in the Brontosaurus and whale category.

Good VCs, bad VCs

Inspired by Ben Horowitz’ excellent “Good product managers, bad product managers” post and Stefan Smalla’s “Good leader, bad leader” masterpiece I’ve tried to put together my thoughts on what I think makes a great venture capital investor. Thanks go to my colleagues at Point Nine Capital for their invaluable feedback, in particular Michael, Mathias and Rodrigo, who reviewed an early draft of this post and provided lots of great comments.

This post represents our current thinking, which may evolve our time, and some parts are still work in progress. Feedback and discussion with other VCs and entrepreneurs is very welcome.

We’re fully aware that we don’t always live up to the ideal of the “good VC” described below, but as Stefan Smalla said in response to a comment on his leadership manifesto: “Nobody is exactly like that, but it's good to move towards that ambition. Inch by inch.”

A good VC does everything she possibly can to support her portfolio companies

A good VC is truly value-add

A good VC is available for her portfolio companies almost 24/7. If a portfolio founder needs her, she will do everything she can – roll up her sleeves, use her social capital, get on a plane – to help. A good VC is sometimes a recruiter, sometimes a beta tester, sometimes a personal mentor, and isn’t afraid of getting her hands dirty. Not scalable? Screw scalability. If a portfolio founder needs your help in putting out fires, the last thing he or she cares about is how this scales from a VC business model perspective.

A good VC doesn’t only react to requests from the founders. A good VC knows the current challenges of her portfolio companies and is proactively looking for solutions all the time.

Good VCs create firms where portfolio founders have equal access to all partners and not just to “their” partner.

Knowing that there are limits to the help she can provide to founders herself, a good VC tries to leverage the knowledge and expertise of other people. In particular, she facilitates knowledge exchange between the founders of her portfolio through various forums, online and offline.

A bad VC overpromises in the deal-making phase and under-delivers once the deal is done.

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“We view ourselves as a services firm. We try to earn our reputation and brand every day. We practice the art of adding value and we want to be the highest executing board member that founder has and we’re out there everyday trying to earn that reputation.”

Bill Gurley
General Partner, Benchmark Capital

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A good VC is humble and doesn’t try to run the show

A good VC is aware that there is a huge information gap between founders and VCs with respect to the founder’s business. He understands that the founder has thousands of hours of experience in his industry and with his customers and intimately knows the people on his team, whereas the VC’s knowledge of the startup is often much more superficial. He understands that many if not most of the ideas he will come up with are things that the founder has already considered and knows that while he can provide great input, advice and a different perspective, he should neither try to micro-manage nor try to make decisions for the founders.

A good VC knows that managing investors can be time-consuming for founders and tries to find the right balance between being close and providing value on the one hand and getting out of the way on the other hand.

A bad VC overestimates his insights, tries to micro-manage, tries to exercise control and becomes a maintenance burden for the founders.

A good VC goes all-in and avoids conflict within the portfolio

A good VC doesn’t invest in two or more companies that are directly competing against each other.

A bad VC, instead of going all-in into one company and giving his undivided attention and support to her portfolio company, tries to hedge her bets by investing in several companies in the same space.

A good VC tries to maximize the size of the cake vs. his slice of the cake

If a company wants to bring on board other investors, whether in the same round in which the VC invests in the company or at a later stage, a good VC helps the founders to attract great co-investors. A good VC also does this pro-actively – suggesting to invite value-add co-investors to a financing round whenever he sees a great potential fit for a company.

A bad VC worries that if co-investors join a company, he will get a smaller stake in the company. So he discourages founders from working with other investors, maximizing his stake in the company rather than trying to do what’s best for the company as a whole.

A good VC doesn’t take unfair advantage of the founders she invests in

A good VC uses simple term sheets. A good VC may negotiate hard, but she doesn’t try to screw founders by sneaking in hard to understand provisions that can hurt founders. A good VC tries to keep contracts simple, knowing that in an industry where the bulk of returns is produced by the best outcomes, there’s not much value in trying to protect herself against everything which can go wrong anyway.

A good VC also doesn’t overly use leverage, which she might gain over portfolio founders in different situations throughout the company’s life.

When a bad VC negotiates a term sheet, she spends way too much time (and legal fees) on micro-optimizations of all kinds of unlikely scenarios. She may even try to fleece the founders by imposing terms that are unfair, unusual and hard to understand.

Whenever she gets leverage over a portfolio company, e.g. when the company runs out of cash and asks its investors for a bridge financing, a bad VC exploits her leverage to improve her position.


A good VC treats every entrepreneur with utmost respect

A good VC respects the value of the founder’s time at all times

A good VC rarely re-schedules meetings with founders and is almost always on time. In meetings with founders, his phone stays in his pocket.

A bad VC re-schedules meetings with founders all the time, often at the last minute. Once the meeting finally happens, he often arrives late. In the meeting, he will start to check his email (or Facebook feed) on his phone the minute he gets bored.

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“If anybody is not on time I will fine them $10 a minute. That comes from my experience as an entrepreneur. When you are an entrepreneur you are living and dying with your company. You are working extremely hard and the last thing you need to do with your time is to sit in the lobby of a venture capital office.”

Ben Horowitz
General Partner, Andressen Horowitz

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A good VC handles “passes” professionally

Knowing that she has to pass at least 99% of the time, a good VC has built a team and established a deal assessment process that ensures that founders get timely responses. A good VC also tries to give an explanation on why a company is not a match for her, although unfortunately time constraints may make detailed feedback impossible in every case.

A bad VC takes forever to respond to inquiries, and often she doesn’t reply at all. When she passes on a potential investment, she doesn’t try to give the entrepreneur useful feedback. A bad VC also often delays the decision forever, trying to keep her options open.

Side note: This is the area where the distance between reality and ambition is the largest for us at Point Nine. We're trying to get better, but with ~ 200 potential investments to evaluate per month, it's tough.

A good VC only signs a term sheet when he’s going to make the deal

A good VC only signs a term sheet when he’s going to make the investment. After having signed a term sheet he only bails out if really bad things come up in the due diligence, which happens extremely rarely. A good VC also tries to be transparent in the deal evaluation phase before, trying to give the founders a realistic assessment of his interest level and timing requirements.

A bad VC sometimes signs a term sheet to secure the option to invest – at a point in time at which he is not yet sure about his intent to invest. A bad VC often also conveys a misleading impression as to how close he is to making a positive decision and how fast he can move.


A good VC aligns her interests with the interests of her LPs

A good VC is incentivized by carry, not by management fee

A good VC optimizes for higher carry and lower management fee. A good VC also invests most of the management fee in a way where it leverages her ability to make great investments and helps her portfolio companies (e.g. by building a team of associates and advisors and by providing resources to the portfolio) rather than drawing a large salary. A good VC invests heavily into her fund and doesn’t view the GP commitment ⁽¹⁾ as a burden.

A bad VC wants to make a lot of money even when she doesn’t make her LPs⁽²⁾ a lot of money. She tries to minimize her GP commitment while trying to maximize her salary.


A good VC is focused, courageous, humble and desires diversity

A good VC is focused

A good VC is focused on one or more investment theses built around expertise in a certain stage, geography and/or industry.

A bad VC invests broadly across all stages, geographies and industries. Rather than knowing a lot about a few things he knows nothing about everything, which prevents him from providing effective portfolio support and from seeing the best investments in the first place.

A good VC is courageous

A good VC makes bold moves. She has strong opinions, and although she values other investors’ opinions she often invests in companies which many other investors have passed on. A good VC also isn’t afraid of admitting mistakes and failures.

A bad VC’s main driver is FOMO (“fear of missing out”). She doesn’t have the expertise or courage to think independently, but as soon as other investors want to invest in a deal she gets excited. If an investment fails, she tries to produce a PR story to make it look like a success.

A good VC is humble

A good VC knows that luck and serendipity play a big role in investing. He knows that he has to constantly prove his value and that he’s only as good as his last investments. A good VC also doesn’t have a big ego, is a great listener and says “I don’t know” very frequently.

A bad VC, after having made one or two lucky shots, thinks he’s a genius. A bad VC has a big ego and is one of those people who make Board Meetings inefficient because they love to hear themselves talk.

A good VC desires and appreciates diversity

A good VC wants to work with people and invest in founders from a wide variety of languages, cultures, color, origin, gender, religion, age, personality and orientation. He knows that “these people can open up new markets and new geographies, and create potential outsize investment returns from opportunities that others may overlook or not want to risk going after”, to quote Dave McClure.

A bad VC prefers to invest in people who are like him.

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“Our Commitment to Diversity stems from an irresistible desire to explore, from a burning curiosity to learn more about the world, from a moral imperative & intellectual humility to help both others and ourselves become part of a larger, more enlightened global community and global family.” 

Dave McClure
Founding Partner, 500Startups

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A good VC invests for the long-term and gives back

A good VC invests in long-term relationships

A good VC optimizes for the long run in everything she does. She knows that you “always meet twice in life”, as the German saying goes, and tries to create win-win situations.

A bad VC tries to gain short-term advantages over other people, sacrificing relationships and long term gains.

A good VC shares knowledge (but keeps private information confidential)

A good VC openly shares knowledge with startups and investors, knowing that the tech community is not a zero-sum game.

A good VC never, ever shares confidential information like pitch decks with people outside of his firm, unless the founder explicitly gave him permission to do so.

A bad VC is secretive when it comes to sharing knowledge with the community – and leaky with respect to confidential information.

A good VC wants to make the world a better place

A good VC cares about others and knows that there’s more in life than financial returns alone. Whether it’s investing in clean technologies, giving to charity, doing community work or something else – she has a strong urge to make the world a better place.  When she’s made money she doesn’t forget that as much as her wealth is the result of decades of hard work, it’s also the result of being born and raised in the right place and having had opportunities that billions of people on the planet never have.

A bad VC has an exaggerated sense of entitlement, a lack of compassion for the poor and forgets that there are other things in life.


And last but definitely not least…

A good VC delivers sustainable superior performance.

A bad VC doesn’t.


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1) GP commitment = the investment made into the VC fund by the fund’s managers, often called “GPs” (General Partner)
2) LPs = Limited Partners, the people and funds which invest into VC funds

Impressions from the SaaS nirvana (a.k.a. as the 3rd annual PNC SaaS Founder Meetup)

Last week, we've held our third annual SaaS Founder Meetup in San Francisco. Following the first PNC SaaS Founder Meetup in San Francisco in 2012 and the second one in 2013 in Berlin, this has become a tradition for us: Once a year we're bringing together the founders of our SaaS portfolio companies, co-investors and leading experts for a full day of intensive knowledge sharing. To be precise, it was one day in 2012 and 2013. This year we've extended it to two full days.

It's hard to describe in a few words how awesome it was and how much we and our portfolio founders have been able to learn thanks to all the amazing speakers who were willing to share their insights at the event. I'll try to follow-up with some additional notes later, but for now here are some visual impressions from the meetup:

Impressions from the PNC SaaS Founder Meetup 2014 from Point Nine Capital

Huge thanks to all attendees and a special thanks to all of our incredible speakers and panelists:

Aaron Ross (Author of "Predictable Revenue"; former Director of Corporate Sales, Salesforce.com)
Albert Wenger (GP, Union Square Ventures)
Bill Macaitis (Former CMO, Zendesk; former SVP Online Marketing, Salesforce.com)
Boris Wertz (GP, Version One Ventures)
Colin Bramm (Founder & CEO, Showbie)
David Bizer (Founder, Talent Fountain; former Staffing Manager, Google)
David Hassell (Founder & CEO, 15Five)
Donna Wells (President & CEO, Mindflash; former CMO, Mint)
Doug Camplejohn (Founder & CEO, Fliptop)
Everett Oliven (National VP Sales, SAP)
Gil Penchina (serial entrepreneur & angel investor)
Heiko Schwarz (Founder & MD, riskmethods)
Hiten Shah (Founder & CEO, KISSmetrics)
Jason M. Lemkin (Managing Director, Storm Ventures; former Founder & CEO, EchoSign)
Jean-Christophe Taunay-Bucalo (Chief Revenue Officer, Vend)
Joel York (Founder & CEO, Markodojo; former CMO, Meltwater Group)
Julien Lemoine (Founder & CTO, Algolia)
Lars Dalgaard (GP, Andreessen Horowitz; former Founder & CEO of SuccessFactors)
Lincoln Murphy (Customer Success Evangelist, Gainsight)
Mark MacLeod (CFO, FreshBooks; former GP, Real Ventures)
Matthew Romaine (Founder & CTO, Gengo)
Nick Franklin (former MD Asia, Zendesk)
Nick Mehta (CEO, Gainsight)
Nicolas Dessaigne (Founder & CEO, Algolia)
Nikos Moraitakis (Founder & CEO, Workable)
Omer Gotlieb (Founder & Chief Customer Officer, Totango)
Paul Joyce (Founder & CEO, Geckoboard)
Rian Gauvreau (Founder & COO, Clio)
Ryan Engley (Director of Customer Success, Unbounce)
Ryan Fyfe (Founder & CEO, ShiftPlanning/Humanity)
Sean Ellis (Founder & CEO, Qualaroo)
Sean Jacobsohn (Principal, Norwest Venture Partners)
Sharad Mohan (Chief Customer Officer, Vend)
Steven Silberbach (VP Global Sales, Clio; former Area VP Sales, Salesforce.com)
Todd Varland (Solutions Architect)
Tomasz Tunguz (Partner, Redpoint Ventures)
Zvi Band (Founder & CEO, Contactually)

Benchmarking your SaaS startup

People often ask me questions like:

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

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

SaaS metrics benchmarking survey

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

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

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

Thanks in advance to all participants!

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!

[Update: I've posted a follow-up post, "Three more ways to build a $100 million business".]

[Another update: Here's an infographic version of this post.]

[Yet another update: We turned the post into a poster!]

[One more update: Here's a webinar that I did about the topic a few days ago.]
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* 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.

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.

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!

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!

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!

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.