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.]
___________________________________

* 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.

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

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

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

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

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

A Quick Primer about Cohort Analysis

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

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

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

So What?

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

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

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

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

The Holy Grail of SaaS!

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

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

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

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

It's a ZEN day!

Today is a very special day for me as as an entrepreneur and investor. About an hour ago, Zendesk went public on the New York Stock Exchange. The last time I watched an IPO so carefully was when Shopping.com, the company that had bought my price comparison startup, went public – almost ten years ago.

Here are a few visual impressions of my love affair with Zendesk, which began six years ago:



Huge congrats and thanks to the entire Zendesk team – I couldn't be more proud of you guys!

Three more ways to look at cohort data

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

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



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

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

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

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

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

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

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

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

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

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

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

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

Now let's get into it!

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

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

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

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

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

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

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

Q: Should I treat team and individual accounts differently?

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

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

Q: What about annual vs. monthly plans?

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

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

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

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

(click for larger version)

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

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

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

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

Q: Is that it?

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

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

Four (more) things we look for in SaaS startups

More than two years ago I wrote about what we look for in early-stage SaaS startups. Since then we've looked at hundreds of SaaS startups and have gained additional insights through the work that we've been doing with the SaaS startups that we have invested in. Therefore I thought it would be time for a follow-on post with some additional thoughts.

In the original post I focused primarily on early metrics as an indicator of product/market fit and of a favorable CAC/CLTV ratio in the future. Today I want to put more focus on factors that kick in a little later in the lifecycle of a SaaS company – aspects that have an impact on a company's ability to scale customer acquisition, increase ARPA and create lock-in. In other words, factors that can make the difference between a "good" and a "great" business.

Note that none of these factors is a must-have for building a successful SaaS company. For each one of them you'll probably find some great counterexamples. The point is that all other things being equal, these characteristics increase the odds of creating a big SaaS success:

1. High search volume combined with limited SEM/SEO competition

Search volume on Google is a good indicator of the awareness for the problem that you're solving. You may have a fantastic solution for a big problem, but if no one is looking for it, marketing it will be much harder. It means you'll have to spend more effort on educating the market and that you may not have a lot of low-hanging fruits on the customer acquisition front.

Related to search volume is of course competition for the relevant keywords, both with respect to SEM/PPC and SEO. If there's lots of competition for your keywords, PPC advertising might be prohibitively expensive and SEO will be much harder.

If, in contrast, there's high search volume and limited competition this not only indicates demand for your product, a gap in the market and potential to acquire customers via SEO/SEM. It also means that you have an opportunity to establish yourself as the thought leader in your space by doing great content marketing.

2. "Land and expand" and "bottom up" customer acquisition

Selling to big enterprises is tempting because one big enterprise deal can be worth tens or hundreds of thousands of dollars. But it's also tough: Sales cycles are long, you need to convince various different stakeholders, there are special requirements for the product and you have to do multiple meetings to get the deal. Anyone who's done or tried it knows what I mean. Conversely, selling to SMBs is much easier, but the value of each customer is obviously a lot lower as well.

A "land and expand" or "bottom up" customer acquisition strategy has the potential to give you the best of both worlds. There are different variations of this strategy, but the idea is always that a single user or a small team of people inside a company starts using your product, making the initial sale easy (if any "selling" is involved at all). Over time, more and more people inside the company use it, and eventually you can sell an enterprise account to the entire company.

Perhaps the most famous example of a successful bottom up adoption is enterprise social network Yammer. Within the first two years after launch, the company's freemium distribution model attracted users from 80% of the Fortune 500 companies and got Yammer into more than 90,000 customers. According to this Mashable article, 15% of these companies subsequently upgraded to a paid plan.

If you want to follow in Yammer's footsteps (or just copy some pages from their playbook) here are some of the things you should keep in mind:

• Since you want to sign up users with little to no sales efforts you need a great marketing website and frictionless onboarding.
• Your product needs to provide value for a small number of users inside a company but even larger value if more people use it.
• Your pricing needs to be highly differentiated – make your product cheap or even free for a small number of users to maximize distribution and make money out of bigger accounts.
• Once you want to sell bigger team accounts or enterprise accounts you need to provide the functionalities required by bigger companies (a sophisticated role/permission system, SLAs, audit logs, etc.) while still keeping the product easy to onboard and use.

3. Virality

It's very rare for B2B SaaS applications to get really viral, i.e. have a viral coefficient of over 1. However, even though your SaaS product will never get Hotmail/Skype/Instagram/Snapchat-like growth, any level of virality is valuable because it means you're augmenting your paid user acquisitions with free users.

There are two primary ways in which a SaaS application can be viral:

a) "Sharing"
A use case which involves communication, collaboration, file sharing or the like with external parties. Examples include project management software like Basecamp (where e.g. an agency invites a client to a Basecamp project), e-signing solutions like EchoSign (where the person who is asked to sign learns about EchoSign during the process) or file sharing providers like Dropbox (you got the idea). The more affinity there is between your target group and their "collaborators", the better it is for you, since it means a higher "invite to signup" conversion rate.

b) "Publishing"
A use case where your customers use your software to create something which gets published on the Web. Examples: Shopify, SquareSpace, MailChimp or our portfolio company Typeform. Another example is Zendesk's feedback tab. The signup conversion rate is much lower in this case, but it can be offset if your product gets exposed to large numbers of people.

You can't force it if there's no sensible "sharing" or "publishing" use case for your product, but you should think about it carefully. If sharing or publishing doesn't make sense for you, you can still get some virality in other ways:

• Employee fluctuation: If you have a product that is used by lots of employees inside a company, try to make everyone an ambassador of your software who will suggest using your product in future jobs.
• Referral programs: FreeAgent's user-to-user referral scheme is a good example.
• Incentives along the lines of "get XYZ for a tweet", where users can e.g. unlock features or remove limitations by inviting people to your product.

4. Economic moat

In the first couple of years you shouldn't worry too much about your long-term competitive advantages. Oftentimes execution is everything. Working harder than your competition, innovating faster and just doing everything a little bit better goes a long way.

Having said that, the best and most profitable companies in the world are those which manage to create wide moats around them – sustainable competitive advantages that allow them to keep market share and profit margins in spite of aggressive competitors. The best examples for wide economic moat are patents (think pharma) and natural monopolies (think eBay).

These two examples aren't very relevant for SaaS companies and there is no simple silver bullet for creating sustainable competitive advantage, but there are a couple of factors which can create moat around a SaaS business:

• A platform. The best example is the Force.com platform. The large number of applications that integrate with Salesforce.com make Salesforce.com the most comprehensive CRM solution on the market and give the company a huge competitive edge. This is a classic example of a virtuous circle: More customers attract more developers which in turn attract more customers. When a platform has reached a certain size, it's very hard for competitors to attack you. 
• Distribution channels: If you have thousands of partners who have been trained to sell your software and make a lot of money doing it, this can be another very valuable asset. Admittedly the role of VARs and other distribution partners is typically lower in SaaS than it is in traditional enterprise software, and the best example of an extremely valuable VAR channel is probably SAP.
• Lock-in: A great product with a fantastic user experience alone can create significant lock-in. But different types of SaaS products have different levels of lock-in. The more people inside a company use your product, the more business partners interact with the software and the deeper the product is integrated into a company's core businesses processes, the higher are the switching costs.
• Network effects: Great examples include Freshbook's billing network and MailChimp's eMail Genome Project. What these two examples have in common is that (at least in theory) every users makes the product more valuable for all other users.
• Big data: If you have tens of thousands of customers, the massive amounts of data created by your customer base might allow you to draw insights which you can then give back to your customers. Zendesk's benchmarking reports come to mind as an example.

Measuring your SaaS success

I recently participated in Marco Montemagno's SuperSummit and gave a webinar about the topic "Measuring your SaaS success". Thanks, Marco, for inviting me!

Below are the slides of my talk. Since some of the slides aren't self-explanatory I've added some notes, see the yellow bubbles. If you want to dive in deeper, check out this post, which the talk was based on.

Measuring your SaaS success from Point Nine Capital

Contactually + MailChimp = yummy

Some time ago I wrote that we at Point Nine love to eat our own dog food. That is, we run Point Nine  almost exclusively on Cloud apps. We're also heavy users of Zendesk, Mention, Geckoboard and other products from our own portfolio companies.

Another great example is Contactually. At its core, Contactually is a relationship management platform for salespeople and service providers in relationship-based businesses. The combination of two killer features – an address book that updates itself and a very smart system for so-called "follow-up reminders" – allows Contactually users to stay top of mind with all of their important contacts, which can have a huge positive impact on their business.

One really really nice thing which Contactually does for us is that it continuously adds subscribers to our (in)famous newsletter – almost automatically. Here's how it works.

1) By scanning my email accounts, the software automatically adds all new people who I'm exchanging a message with as contacts. You only have to connect your email accounts once, Contactually does the rest.

2) Every two weeks or so I put the new contacts into one of my "buckets":



This takes just one click per contact (and you can also do it from your mobile).

3) Then the magic starts. If I've added a contact to a bucket which is set to be synchronized with MailChimp, the contact will be pushed to our newsletter subscription list in MailChimp.

Here's how the bucket settings look like for these buckets:



If I don't want want to add the contact to our newsletter I just use a different bucket, one which is not set up for synchronization with MailChimp.

4) As soon as the new contact is pushed to MailChimp, the contact receives this email:



This is done using MailChimp's auto-responder feature:



That's it!

When we started this experiment we were of course wondering if it's too aggressive to automatically subscribe people to our newsletter. We came to the conclusion that it's OK if we're selective (i.e. only add people who we think are interested in news from us), have a fun confirmation email (see above) and have a one-click unsubscribe link. So far, we didn't receive a single complaint and very few people have unsubscribed, so it looks like it's working.

We ♥ vanity metrics ;-)

Who ever said only startups love vanity metrics? Here's our revenge for all those misleading stats that we have to muddle through almost on a daily basis when startups pitch us!

Yesterday I saw this post on the blog of Karlin Ventures. In response to a tweet by Paul Graham which was highlighted in Danielle Morrill's excellent Mattermark Daily newsletter,  the guys at Karlin Ventures revealed the "days since last contact" numbers for their portfolio.

Here are the numbers for the 26 active companies in our current fund, Point Nine Capital II:

As written in Karlin Ventures' blog post, frequent communication is by no means a guarantee for helpfulness. Sometimes companies are in a phase in which the best thing an investor can do is to shut up and let the founders do their jobs. More often than not, though, I feel that a very close relationship and between founders and investors is a good sign. So – take a look at the stats above but don't read too much into them. :)

6 things SaaS founders should keep in mind in 2014

First of all, a Happy New Year to all readers of this blog. I hope you've had a great start into the new year, and I wish you a happy, healthy and prosperous (and of course SaaSy) 2014.

I've done a bit of reflection on what I've learned in the last couple of months. Here are six things that I think SaaS founders should keep in mind in 2014. This is obviously not meant as a definite or comprehensive list by any means. Rather, it's a synopsis of some of the things that keep me up at night these days.

1) Have the right mix of paranoia and patience

In the spirit of Andy Grove you need to be paranoid about becoming and staying the #1 player in your market. For a variety of reasons, most SaaS markets have "winner takes most" characteristics, so you have to do everything you can to dominate your market. But since we're still in the early days of Cloud adoption and since it usually takes 5-10 years to build a large SaaS company, you also need lots of patience. Gail Goodman of Constant Contact reminded me of that in this excellent talk.

2) Work on your weaknesses until they become your strengths

At the outset, almost every SaaS founder team that we talk to is either very strong on the product/tech side or on the sales/marketing side, but rarely on both sides. It's like a team DNA, and it's hard for a product-driven team to become excellent at sales and vice versa. At the same time, you have to be great at both product/tech and sales/marketing in order to succeed, so you should do everything you can to work on your weak side. This usually means a combination of a) learning really fast and going out of your comfort zone and b) hiring senior people with complementary skills and experiences. I'm not saying that you shouldn't leverage your strengths, but I know you're going to do that anyway. :) Doing what you love to do and what you're good at is comparably easy. Fixing your weaknesses is the tougher part.

3) Have a plan for 2014

Become clear on what you want to achieve in 2014 and what this means for your product roadmap, your marketing plan and your financial plan. Define company-wide OKRs as well as quarterly OKRs for each employee. It sounds like a no-brainer, but my guess is that most startups will benefit from going through a more structured OKR exercise. More about this in my recent post about OKRs.

4) Prioritize "mobile"

Mobile is eating the world. 'Nuff said.

If you don't offer your customers a fantastic experience on smartphones and tablets (which usually means native apps that leverage the unique capabilities of the device or the mobile usage scenario) you're at risk of getting disrupted by a mobile-first startup, faster than you can disrupt the incumbents of your industry.

5) Don't optimize for the edge cases

One thing I've noticed is that many startup founders are trying too hard to make everyone happy, which leads them to optimize pricing, sales tactics and maybe even product design for a small vocal minority of users. When I discuss e.g. lifecycle email marketing and pricing with SaaS founders I like to say:

"If no one is complaining about your prices, you're most likely too cheap"
"If no one is calling your emails 'spam', maybe you're not sending enough emails"

Similarly, if one user requests a new feature or a change in the product that's no reason to do it, unless you think it makes sense for a large part of your target group.

The temptation to please every user is understandable but it doesn't mean it's the right thing to do. The pricing expectation of your users, for example, will probably follow some kind of bell curve. If you optimize for users on the far edges you're leaving a lot of money on the table in the much bigger middle area of the curve.

6) Raise money when you can, not when you need it

It's a pretty good time for SaaS startups to raise money. If you have the possibility to raise a meaningful amount of money at a good valuation, you should seriously consider it even if you don't necessarily need the money right away. First of all, it's usually unclear what "need" really means. Enough to get to break even? Enough to get to the next round of funding? Enough to win the market? More cash almost always means de-risking and an opportunity to accelerate. I venture to say that if you don't know what to do with an additional couple of million dollars that shows a lack of imagination. Secondly, I don't want to send a "R.I.P. Good Times" message, but currently the times are pretty good and no one knows what will happen in the next one or two years. Thirdly, just because you raise money doesn't mean you have to spend it imprudently, and most SaaS founders who I know are not at risk of failing due to premature scaling because frugality is part of their DNA.

What do you think about these six themes? Which additional ones do you think SaaS founders should pay attention to in 2014?