Written by Tim Ferriss Topics: Entrepreneurship
Noah Kagan built two multi-million dollar online businesses before turning 28. He also looks great in orange. (Photo: Laughing Squid)
I first met Noah Kagan over rain and strong espressos at Red Rock Coffee in Mountain View, CA. It was 2007. We were both in hoodies, had a shared penchant for the F-bomb and burritos, all of which led to a caffeine-infused mindmeld.
It would be the first of many.
The matchmaker then introducing us was the prophetic and profane Dave McClure, General Partner of 500 Start-ups, which is now headquartered just down the street from Red Rock.
Mr. Noah has quite the start-up resume.
He was employee #30 at Facebook, #4 at Mint, had previously worked for Intel (where he frequently took naps under his desk), and had turned down a six-figure offer from Yahoo. Since we first met, Noah’s helped create Gambit, an online gaming payment platform and a multi-million dollar business; and AppSumo, loved by entrepreneurs and moms everywhere. He also helped pour fire on both the 4-Hour Workweek and 4-Hour Body launches.
The purpose of this post is simple: to teach you how to get a $1,000,000 business idea off the ground in one weekend, full of specific tools and tricks that Noah has used himself.
He will be your guide…
For some reason, people love to make excuses about why they haven’t created their dream business or even gotten started. This is the “wantrepreneur” epidemic, where people prevent themselves from ever actually doing the side-project they always talk about over beers. The truth of the matter is that you don’t have to spend a lot of time building the foundation for a successful business. In most cases, it shouldn’t take you more than a couple days.
We made the original product for Gambit in a weekend. “WTF?!” Yes, a weekend. In just 48 hours, some friends and I created a simple product that grew to a $1,000,000+ business within a year.
Same deal for AppSumo. We were able to build the core product in one weekend, using an outsourced team in Pakistan, for a grand total of $60.
Don’t get me wrong–I’m not opposed to you trying to build a world-changing product that requires months of fine-tuning. All I’m going to suggest is that you start with a much simpler essence of your product over the course of a weekend, rather than wasting time building something for weeks… only to discover no one wants it.
I know what you’re thinking: “Yes, Noah, you are SO amazing (and handsome), but what can I do this weekend to start my own success story?”
Here are the steps you can take right now to get started on your million dollar company:
Step 1: Find your (profitable) idea.
At this stage, you are simply looking for something that people are willing to spend money on. So grab a seat and write down a list of ideas that you think might be profitable. If you’re having trouble coming up with ideas, try using the methods below to speed the research process along:
Review top sellers on Amazon. Find products that already have guaranteed customers, then build something complementary. A good example of this isDodo making a gorgeous $60 case to buy for your iPad (which costs over $500, and over 5 million sold).
Think of all the things you do on a daily basis. Anything done more than once has potential for a product or service to improve the process. For me, one of those products was a mirror I could hang in the shower. It saves me tons of time while shaving, and now I don’t know how I ever lived without it.
Be cognizant of products you use and frequently complain about. Before Gambit, we were constantly asking our payment tool partners for certain features, yet our requests were always rejected. That was the impetus for us to create Gambit for our own games.
Check completed listings on eBay. This allows you to see how well certain products are selling. It’s also an easy way to measure sale prices of items and gauge the overall percentage of the market that’s receiving bids (i.e. in demand).
Look for frequent requests on Craigslist gigs. These listings are from people actively searching for someone to give their money to in exchange for particular services. Try searching for certain keywords (e.g. marketing, computers, health) and keep track of the total number of results displayed. Evaluate the most popular keywords and see if you can create a product or service around those requests.
Step 2: Find $1,000,000 worth of customers.
Now that you’ve found an idea, it’s time to assess whether there’s a big enough pool of prospective buyers. In this step, you’ll also want to ensure your market isn’t shrinking, and that it fares well compared to similar markets.
For example, let’s say you decide to build information products for owners of Chihuahuas (remember “Yo quiero Taco Bell”?). Here’s how I would check to see if there are enough customers:
1. Search Google Trends for the term “chihuahua” and other similar words (e.g. poodle, dogs) for comparison:
We can see that the word “chihuahua” has a decent search volume (relative to “dogs”), and that “poodle” isn’t as popular. It also looks like the number of searches for “chihuahua” has been relatively stable for the last few years.
2. Double-check on Google insights:
Google Insights is great, because it breaks down the search data by location (i.e. what regions the searches are coming from), by date, and what they’re searching for (news, images, products). Click here to see the full report for the above chart.
3. Look at the total number of people available on Facebook for dogs:
3.1 million. Not bad, not bad.
And for Chihuahuas:
84,260 people. Score.
You can also see if there is a large property that you can piggyback on.
Paypal did this with eBay, AirBnb is doing it with Craigslist home listings, andAppSumo looks to the 100 million LinkedIn users. If you can find a comparable site with a large number of potential customers, you’ll be in good shape.
What helped me with finding $1,000,000 worth of customers for AppSumo was studying my successful competitors; specifically, Macheist. Their site did a Mac-only deal that generated more than $800,000. Macheist shares their sales revenue publicly, but you can use your own business acumen on the CrunchBase list to see which business you want to replicate. For instance, you might research Airbnb.com, discover that they have a profitable and growing marketplace, then decide to create a similar service for alternative verticals.
I like to create a Google Spreadsheet of the key numbers for my competitors’ businesses. Below is an example of what that might look like for Macheist in their Mac bundles. [Warning to the haters: This may not be accurate, but I used these numbers just to get a rough idea of the business’ potential.]
Step 3: Assess your customer’s value.
Once you’ve found your idea and a big pool of potential customers, you’ll need to calculate the value of those customers. For our example above, we’ll need to estimate how much a Chihuahua owner (i.e. our customer) is worth to us. This will help us determine the likelihood of them actually buying our product, and will also help with pricing. Here’s how we do that:
1. Find out how much it costs, on average, to buy a Chihuahua (about $650). This is the base cost.
2. See how much it costs to maintain a Chihuahua each year (i.e. recurring costs). Looks like it’s between $500-3,000. For this example, we’ll call it $1,000.
3. Look up their life expectancy, which is roughly 15 years. This is the number of times they’ll have to pay those recurring costs.
Therefore, a Chihuahua’s average total cost of ownership is:
[$650 + ($1,000*15)] = $15,650
Damn… you could buy a lot of burritos with that kind of cash. Silly dog owners.
In any case, these owners are already committing to spend a LOT of money on their dogs (i.e. they are valuable). After putting down $650 on the dog itself and an average of $80/month on maintenance (a.k.a. food), spending $50 on an information product that could help them train their Chihuahua–or save money, or create a better relationship between them, etc.–does not seem unreasonable. Of course, the product doesn’t have to cost $50, but we now have some perspective for later deciding on a price.
Now we need to utilize the TAM formula (a.k.a. Total Available Market formula), which will help us see our product’s potential to generate a million dollars.
Here’s the TAM formula for estimating your idea’s potential:
(Number of available customers) x (Value of each customer) = TAM
If TAM > $1,000,000, then you can start your business.
Let’s plug in some basic numbers to see the TAM for our Chihuahua information product:
(84,260 available customers) x ($50 information product) = $4,213,000
We have a winner!
Okay, obviously you are not going to reach 100% market penetration, but consider the following…
1. This is only through Facebook traffic.
2. This does not include the 5,000,000 monthly searches for “Chihuahua” on Google:
3. This is only for one breed of dog. If you find success with Chihuahuas, you can easily repeat the process many times with other dog breeds.
4. This is only for one product. It’s far easier to sell to an existing customer than it is to acquire new ones, so once we’ve built up a decent customer base, we can make even more products to sell to them.
By all measures, it appears that we have a million dollar idea on our hands. Now we can move on to the final step!
Step 4: Validate your idea.
By now, you have successfully verified that your idea has that special million-dollar-potential. Feels good, right? Well, brace yourself — it’s time to test whether people will actually spend money on your product. In other words, is it truly commercially viable?
This step is critical. A lot of your ideas will seem great in theory, but you’ll never know if they’re going to work until you actually test your target market’s willingness to pay.
For instance, I believed AppSumo’s model would work just on gut-feeling alone, but I wasn’t 100% convinced people wanted to buy digital goods on a time-limited basis. I mean, how often do people find themselves needing a productivity tool (compared with, for instance, how often they need to eat)?
I decided to validate AppSumo’s model by finding a guaranteed product I could sell, one with its own traffic source (i.e. customers).
Because I’m a frequent Redditor and I knew they had an affordable advertising system(in addition to 3 million+ monthly users), I wanted to find a digital good that I could advertise on their site. I noticed Imgur.com was the most popular tool on Reddit for sharing images, and they offered a paid pro account option ($25/year). It was the perfect fit for my test run.
I cold-emailed the founder of Imgur, Alan Schaaf, and said that I wanted to bring him paying customers and would pay Imgur for each one. Alan is a great guy, and the idea of getting paid to receive more customers was not a tough sell The stage was set!
Before we started the ad campaign, I set a personal validation goal for 100 sales, which would encourage me to keep going or figure out what was wrong with our model. I decided on “100″ after looking at my time value of money. If I could arrange a deal in two hours (find, secure, and launch), I wanted to have a return of at least $300 for those two hours of work. 100 sales ($3 commission per sale) was that amount.
By the end of the campaign, we had sold more than 200 Imgur pro accounts. AppSumo.com was born.
I share this story because it illustrates an important point: You need to make small calculated bets on your ideas in order to validate them. Validation is absolutely essential for saving time and money, which will ultimately allow you to test as many of your ideas as possible.
Here are a couple methods for rapidly validating whether people will buy your product or not:
Drive traffic to a basic sales page. This is the method Tim advocates in The 4-Hour Workweek. All you need to do is set up a sales page using Unbounce orWordPress, create a few ads to run on Google and/or Facebook, then evaluate your conversion rate for ad-clicks and collecting email addresses. This is how we launched Mint.com (see one of our original sales pages here). You are not looking for people to buy; you are simply gauging interest and gathering data.
[Note: With Facebook advertising, $100 can get you roughly 100,000 people viewing your ad, and about 80 people visiting your site and potentially giving you their email addresses.]
Email 10 people you know who would want your pseudo-product, then ask them to send payment via Paypal. This might sound a bit crazy, but you’re doing it to see what the overall response is like. If a few of them send payment, great! You now have validation and can build the product (or you can refund your friends and buy them all tacos for playing along). If they don’t bite, figure out why they don’t want your product. Again, the goal is to get validation for your product, not to rip off your friends.
Of course, there are other techniques for validating your product (like Stephen Keyleaving his guitar pick designs in a convenience store to see if people would try to buy them). However, I’ve found these two methods to be super efficient and effective for validating ideas online.
No need to get fancy if it does the trick.
The Final Frontier: Killing Your Inner Wantrepreneur
We made it! You officially have a $1,000,000 idea on your hands and you know for a fact that people are willing to pay for it. Now you can get started on actually building the product, creating your business, and freeing yourself from the rat race!
I can just see it… You’re all nodding and thinking, “Hey, this Noah guy is pretty snazzy!” (Sorry ladies, I’m taken.)
So, what now?
– You are inspired. Check.
– You want to do something. Check.
– You get a link to a funny YouTube video, then you open up Reddit. Check.
– Suddenly, everything you thought you were going to do goes down the drain. Check.
– You and I softly weep. Check.
I want to challenge you! Whoever generates the most profit (not just revenue) within 14 days of this article will win some fantastic goodies. First, here are the basic rules and the process:
– Contest void where prohibited.
– The business/product must be new. This means either a landing page created from scratch using Unbounce or WordPress above.
– Results and proof of some type must be submitted as a comment below no later than 1am PST Saturday on October 8, 2011. Don’t cut it too close; if a timezone misjudgment knocks you out, we can’t make exceptions.
– Put your 14-day profit number (or increase) in the FIRST line of your comment.
– Ultimately, verifiable proof with lower number beats unverifiable proof with higher number.
[NOTE: THIS CONTEST HAS ENDED. Still need help starting a business? Check out AppSumo’s “How to Make your First Dollar” course.]
– $1,000 credit from AppSumo.com
– Roundtrip flights to Austin, Texas to have the most delicious tacos in the world with Noah Kagan, CEO of AppSumo. Sorry, but we can only cover flights within the USA. If you want to hoof it to the US, we can then pick up from there.
– Above all: your $1,000,000 business, of course!
Don’t let this post become another feather in your Wantrepreneurship cap. Just follow the steps and start working towards your $1,000,000 business! Remember, you can start laying the foundation for your product without building anything.
All you need is one weekend.
Here is an essay version of my class notes from Class 4 of CS183: Startup. Errors and omissions are my own. Credit for good stuff is Peter’s entirely.
CS183: Startup—Notes Essay—April 11—The Last Mover Advantage
I. Escaping Competition
The usual narrative is that capitalism and perfect competition are synonyms. No one is a monopoly. Firms compete and profits are competed away. But that’s a curious narrative. A better one frames capitalism and perfect competition as opposites; capitalism is about the accumulation of capital, whereas the world of perfect competition is one in which you can’t make any money. Why people tend to view capitalism and perfect competition as interchangeable is thus an interesting question that’s worth exploring from several different angles.
The first thing to recognize is that our bias favoring competition is deep-rooted. Competition is seen as almost quintessentially American. It builds character. We learn a lot from it. We see the competitive ideology at work in education. There is a sense in which extreme forms of competition are seen as setting one up for future, non-competitive success. Getting into medical school, for example, is extremely competitive. But then you get to be a well-paid doctor.
There are, of course, cases where perfect competition is just fine. Not all businesses are created to make money; some people might be just fine with not turning a profit, or making just enough to keep the lights on. But to the extent one wants to make money, he should probably be quite skeptical about perfect competition. Some fields, like sports and politics, are incredibly and perhaps inherently competitive. It’s easier to build a good business than it is to become the fastest person alive or to get elected President.
It may upset people to hear that competition may not be unqualifiedly good. We should be clear what we mean here. Some sense of competition seems appropriate. Competition can make for better learning and education. Sometimes credentials do reflect significant degrees of accomplishment. But the worry is that people make a habit of chasing them. Too often, we seem to forget that it’s genuine accomplishment we’re after, and we just train people to compete forever. But that does everyone a great disservice if what’s theoretically optimal is to manage to stop competing, i.e. to become a monopoly and enjoy success.
A law school anecdote will help illustrate the point. By graduation, students at Stanford Law and other elite law schools have been racking up credentials and awards for well over a dozen years. The pinnacle of post law school credentialism is landing a Supreme Court clerkship. After graduating from SLS in ’92 and clerking for a year on the 11th Circuit, Peter Thiel was one of the small handful of clerks who made it to the interview stage with two of the Justices. That capstone credential was within reach. Peter was so close to winning that last competition. There was a sense that, if only he’d get the nod, he’d be set for life. But he didn’t.
Years later, after Peter built and sold PayPal, he reconnected with an old friend from SLS. The first thing the friend said was, “So, aren’t you glad you didn’t get that Supreme Court clerkship?” It was a funny question. At the time, it seemed much better to be chosen than not chosen. But there are many reasons to doubt whether winning that last competition would have been so good after all. Probably it would have meant a future of more insane competition. And no PayPal. The pithy, wry version of this is the line about Rhodes Scholars: they all had a great future in their past.
This is not to say that clerkships, scholarships, and awards don’t often reflect incredible accomplishment. Where that’s the case, we shouldn’t diminish it. But too often in the race to compete, we learn to confuse what is hard with what is valuable. Intense competition makes things hard because you just beat heads with other people. The intensity of competition becomes a proxy for value. But value is a different question entirely. And to the extent it’s not there, you’re competing just for the sake of competition. Henry Kissinger’s anti-academic line aptly describes the conflation of difficulty and value: in academia at least, the battles are so fierce because the stakes are so small.
That seems true, but it also seems odd. If the stakes are so small, why don’t people stop fighting so hard and do something else instead? We can only speculate. Maybe those people just don’t know how to tell what’s valuable. Maybe all they can understand is the difficulty proxy. Maybe they’ve bought into the romanticization of competition. But it’s important to ask at what point it makes sense to get away from competition and shift your life trajectory towards monopoly.
Just look at high school, which, for Stanford students and the like, was not a model of perfect competition. It probably looked more like extreme asymmetric warfare; it was machine guns versus bows and arrows. No doubt that’s fun for the top students. But then you get to college and the competition amps up. Even more so during grad school. Things in the professional world are often worst of all; at every level, people are just competing with each other to get ahead. This is tricky to talk about. We have a pervasive ideology that intense, perfect competition makes the best world. But in many ways that’s deeply problematic.
One problem with fierce competition is that it’s demoralizing. Top high school students who arrive at elite universities quickly find out that the competitive bar has been raised. But instead of questioning the existence of the bar, they tend to try to compete their way higher. That is costly. Universities deal with this problem in different ways. Princeton deals with it through enormous amounts of alcohol, which presumably helps blunt the edges a bit. Yale blunts the pain through eccentricity by encouraging people to pursue extremely esoteric humanities studies. Harvard—most bizarrely of all—sends its students into the eye of the hurricane. Everyone just tries to compete even more. The rationalization is that it’s actually inspiring to be repeatedly beaten by all these high-caliber people. We should question whether that’s right.
Of all the top universities, Stanford is the farthest from perfect competition. Maybe that’s by chance or maybe it’s by design. The geography probably helps, since the east coast doesn’t have to pay much attention to us, and vice versa. But there’s a sense of structured heterogeneity too; there’s a strong engineering piece, the strong humanities piece, and even the best athletics piece in the country. To the extent there’s competition, it’s often a joke. Consider the Stanford-Berkeley rivalry. That’s pretty asymmetric too. In football, Stanford usually wins. But take something that really matters, like starting tech companies. If you ask the question, “Graduates from which of the two universities started the most valuable company?” for each of the last 40 years, Stanford probably wins by something like 40 to zero. It’s monopoly capitalism, far away from a world of perfect competition.
The perfect illustration of competition writ large is war. Everyone just kills everyone. There are always rationalizations for war. Often it’s been romanticized, though perhaps not so much anymore. But it makes sense: if life really is war, you should spend all your time either getting ready for it or doing it. That’s the Harvard mindset.
But what if life isn’t just war? Perhaps there’s more to it than that. Maybe you should sometimes run away. Maybe you should sheath the sword and figure out something else to do. Maybe “life is war” is just a strange lie we’re told, and competition isn’t actually as good as we assume it is.
II. Lies People Tell
The pushback to all this is that, generally speaking, life really is war. Determining how much of life is actually perfect competition versus how much is monopoly isn’t easy. We should start by evaluating the various versions of the claim that life is war. To do that, we have to be on guard against falsehood and distortion. Let’s consider the reasons why people might bend the truth about monopoly versus competition in the world of technology.
A. Avoid the DOJ
One problem is that if you have a monopoly, you probably don’t want to talk about it. Antitrust and other laws on this can be nuanced and confusing. But generally speaking, a CEO bragging about the great monopoly he’s running is an invitation to be audited, scrutinized, and criticized. There’s just no reason to do it. And if the politics problem is quite severe, there is actually strong positive incentive is to distort the truth. You don’t just not say that you are a monopoly; you shout from the rooftops that you’re not, even if you are.
The world of perfect competition is no freer from perverse incentives to lie. One truth about that world is that, as always, companies want investors. But another truth about the world of perfect competition is that investors should not invest in any companies, because no company can or will make a profit. When two truths so clash, the incentive is to distort one of them.
So monopolies pretend they’re not monopolies while non-monopolies pretend they are. On the scale of perfect competition to monopoly, the range of where most companies fall is shrunk by their rhetoric. We perceive that there are only small differences between them. Since people have extreme pressure to lie towards convergence, the reality is probably more binary—monopoly or competitive commodity business—than we think.
B. Market Lies
People also tell lies about markets. Really big markets tend to be very competitive. You don’t want to be a minnow in a giant pool. You want to be best in your class. So if you’re in a business that finds itself in a competitive situation, you may well fool yourself into thinking that your relevant market is much smaller than it actually is.
Suppose you want to start a restaurant in Palo Alto that will serve only British food. It will be the only such restaurant in Palo Alto. “No one else is doing it,” you might say. “We’re in a class of our own.” But is that true? What is the relevant market? Is it the market for British food? Or the restaurant market in general? Should you consider only the Palo Alto market? Or do people sometimes travel to or from Menlo Park or Mountain View to eat? These questions are hard, but the bigger problem is that your incentive is not to ask them at all. Rather, your incentive is to rhetorically shrink the market. If a bearish investor reminds you that 90% of restaurants fail within 2 years, you’ll come up with a story about how you’re different. You’ll spend time trying to convince people you’re the only game in town instead of seriously considering whether that’s true. You should wonder whether there are people who eat only British food in Palo Alto. In this example, those are the only people you have pricing power over. And it’s very possible that those people don’t exist.
In 2001, some PayPal people used to go eat on Castro Street in Mountain View. Then, like now, there were all sorts of different lunch places. Whether you wanted Indian, Thai, Vietnamese, American, or something else, you had several restaurants to choose from. And there were more choices once you picked a type. Indian restaurants, for instance, divided into South Indian vs. not, cheaper vs. fancier. Castro Street was pretty competitive. PayPal, by contrast, was at that time the only e-mail based payments company in world. It employed fewer people than the Mountain View restaurants did. Yet from a capital formation perspective, PayPal was much more valuable than all the equity of all those restaurants combined. Starting a new South Indian food restaurant on Castro Street was, and is, a hard way to make money. It’s a big, competitive market. But when you focus on your one or two differentiating factors, it’s easy to convince yourself that it’s not.
Movie pitches unfold in much the same way. Most of them are the same in that they all claim that this movie will be truly unique. This new film, investors are told, will combine various elements in entirely new ways. And that may even be true. Suppose we want to have Andrew Luck star in a cross between “Hackers” and “Jaws.” The plot summary is: college football star joins elite group of hackers to catch the shark that killed his friend. That’s definitely never been done before. We’ve had sports stars and “Hackers” and “Jaws,” but never anything at the intersection of that Venn diagram. But query whether that intersection would be any good or not.
The takeaway is that it’s important to identify how these rhetorical narratives work. Non-monopolies always narrow their market. Monopolies insist they’re in a huge market. In logical operator terms, non-monopolies tell intersection stories: British food ∩ restaurant ∩ Palo Alto. Hometown hero ∩ hackers ∩ sharks. Monopolies, by contrast, tell union stories about tiny fishes in big markets. Any narrative that carries the subtext of “we’re not the monopoly the government is looking for” will do.
C. Market Share Lies
There are all kinds of ways to frame markets differently. Some ways are much better than others. Asking what is the truth about a given market—and reaching as close to an objective answer as possible—is crucially important. If you’re making a mobile app, you need to determine whether your market is apps on the iPhone, of which there are several hundred thousand, or whether there’s a good way to define or create a very different, smaller market. But one must stay on guard against the sources of bias in this process.
Let’s drill down on search engine market share. The big question of whether Google is a monopoly or not depends on what market it’s in. If you say that Google is a search engine, you would conclude that it has 66.4% of the search market. Microsoft and Yahoo have 15.3% and 13.8%, respectively. Using theHerfindahl-Hirschman index, you would conclude that Google is a monopoly since 66% squared is well over 0.25.
But suppose you say that Google is an advertising company, not a search company. That changes things. U.S. search advertising is a $16b market. U.S. online advertising is a $31b market. U.S. advertising generally is a $144b market. And global advertising is a $412b market. So you would conclude that, even if Google dominated the $16b U.S. search advertising market, it would have less than 4% of the global advertising market. Now, Google looks less like a monopoly and more like a small player in a very competitive world.
Or you could say that Google is tech company. Yes, Google does search and advertising. But they also do robotic cars. They’re doing TV. Google Plus is trying to compete with Facebook. And Google is trying to take on the entire phone industry with its Android phone. Consumer tech is a $964b market. So if we decide that Google as a tech company, we must view it in a different context entirely.
It’s not surprising that this is Google’s narrative. Monopolies and companies worried about being perceived as such tell a union story. Defining their market as a union of a whole bunch of markets makes them a rhetorical small fish in a big pond. In practice, the narrative sounds like this quotation from Eric Schmidt:
“The Internet is incredibly competitive, and new forms of accessing information are being utilized every day.”
The subtext is: we have to run hard to stay in the same place. We aren’t that big. We may get defeated or destroyed at any time. In this sense we’re no different than the pizzeria in downtown Palo Alto.
D. Cash and Competition
One important data point is how much cash a company has on its balance sheets. Apple has about $98b (and is growing by about $30b each year). Microsoft has $52b. Google has $45b. Amazon has $10b. In a perfectly competitive world, you would have to take all that cash and reinvest it in order to stay where you are. If you’re able to grow at $30b/year, you have to question whether things are really that competitive. Consider gross margins for a moment. Gross margins are the amount of profit you get for every incremental unit in marginal revenues. Apple’s gross margins are around 40%. Google’s are about 65%. Microsoft’s are around 75%. Amazon’s are 14%. But even $0.14 profit on a marginal dollar of revenue is huge, particularly for a retailer; grocery stores are probably at something like 2% gross margins.
But in perfect competition, marginal revenues equal marginal costs. So high margins for big companies suggest that two or more businesses might be combined: a core monopoly business (search, for Google), and then a bunch of other various efforts (robotic cars, TV, etc.). Cash builds up because it turns out that it doesn’t cost all that much to run the monopoly piece, and it doesn’t make sense to pump it into all the side projects. In a competitive world, you would have to be funding a lot more side projects to stay even. In a monopoly world, you should pour less into side projects, unless politics demand that the cash be spread around. Amazon currently needs to reinvest just 3% of its profits. It has to keep running to stay ahead, but it’s more easy jog than intense sprint.
III. How To Own a Market
For a company to own its market, it must have some combination of brand, scale cost advantages, network effects, or proprietary technology. Of these elements, brand is probably the hardest to pin down. One way to think about brand is as a classic code word for monopoly. But getting more specific than that is hard. Whatever a brand is, it means that people do not see products as interchangeable and are thus willing to pay more. Take Pepsi and Coke, for example. Most people have a fairly strong preference for one or the other. Both companies generate huge cash flows because consumers, it turns out, aren’t very indifferent at all. They buy into one of the two brands. Brand is a tricky concept for investors to understand and identify in advance. But what’s understood is that if you manage to build a brand, you build a monopoly.
Scale cost advantages, network effects, and proprietary technology are more easily understood. Scale advantages come into play where there are high fixed costs and low marginal costs. Amazon has serious scale advantages in the online world. Wal-Mart enjoys them in the retail world. They get more efficient as they get bigger. There are all kinds of different network effects, but the gist of them is that the nature of a product locks people into a particular business. Similarly, there are many different versions of proprietary technology, but the key theme is that it exists where, for some reason or other, no one else can use the technology you develop.
Apple—probably the greatest tech monopoly today—has all these things. It has complex combination of proprietary technology. By building both the hardware and the software, it basically owns the entire value chain. With legions of people working at Foxconn, it has serious scale cost advantages. Countless developers building on Apple platform and millions of repeat customers interacting with the Apple ecosystem provide the network effects that lock people in. And Apple’s brand is not only some combination of all of these, but also something extra that’s hard to define. If another company made an otherwise identical product, it would have to be priced less than the Apple version. Even beyond Apple’s other advantages, the brand allows for greater monetization.
IV. Creating Your Market
There are three steps to creating a truly valuable tech company. First, you want to find, create, or discover a new market. Second, you monopolize that market. Then you figure out how to expand that monopoly over time.
A. Choosing the Right Market
The Goldilocks principle is key in choosing the initial market; that market should not be too small or too large. It should be just right. Too small a market means no customers, which is a problem. This was the problem with PayPal’s original idea of beaming money on palm pilots. No one else was doing it, which was good. But no one really needed it done, which was bad.
Markets that are too big are bad for all the reasons discussed above; it’s hard to get a handle on them and they are usually too competitive to make money.
Finding the right market is not a rhetorical exercise. We are no longer talking about tweaking words to trick ourselves or persuade investors. Creating your market has nothing to do with framing stories about intersections or unions. What is essential is to figure out the objective truth of the market.
B. Monopoly and Scaling
If there is no compelling narrative of what the market is and how it can scale, you haven’t yet found or created the right market. A plan to scale is crucial. A classic example is the Edison Gower-Bell Telephone Company. Alexander Graham Bell developed the telephone, and with it, a new market. Initially, that market was very small; only a few people were involved in it. It was very easy to be the only one doing things in such a small, early market. They expanded. They kept expanding. The market became durable. Network effects began to operate. It quickly became very hard for others to break in.
The best kind of business is thus one where you can tell a compelling story about the future. The stories will all be different, but they take the same form: find a small target market, become the best in the world at serving it, take over immediately adjacent markets, widen the aperture of what you’re doing, and capture more and more. Once the operation is quite large, some combination of network effects, technology, scale advantages, or even brand should make it very hard for others to follow. That is the recipe for building valuable businesses.
Probably every single tech company ever has fit some version of this pattern. Of course, putting together a completely accurate narrative of your company’s future requires nothing less than figuring out the entire future of the world, which isn’t likely to happen. But not being able to get the future exactly right doesn’t mean you don’t have to think about it. And the more you think about it, the better your narrative and better your chances of building a valuable company.
C. Some Examples
Amazon started very small. Initially, it was just going to be an online bookstore. Granted, becoming the best bookstore in the world, i.e. having all books in catalogue, is not a trivial thing to do. But the scale was very manageable. What is amazing about Amazon was that and how they were able to gradually scale from bookstore to the world’s general store. This was part of the founding vision from the outset. The Amazon name was brilliant; the incredible diversity of life in the Amazon reflected the initial goal of cataloging every book in the world. But the elasticity in the name let it scale seamlessly. At a different scale, the Amazon’s diversity also stood for every thing in the world.
eBay also started small. The idea was to build a platform and prepare for the unexpected. The first unexpected thing was the popularity of Pez dispensers. eBay became the single place where people who were into collecting all the various kinds of Pez dispensers could get them. Then came beanie babies. eBay soon became the only place in world where you could quickly get any particular beanie baby you wanted. Creating a marketplace for auctions lent itself to natural monopoly. Marketplaces are full of buyers and sellers. If you’re buying, you go where the most sellers are. And if you’re selling, you go to where the buyers are. This is why most companies list on just one stock exchange; to create liquidity, all buyers and sellers should be concentrated in the same place. And eBay was able to expand its marketplace to cover a surprisingly large number of verticals.
But eBay ran into problems in 2004, when it became apparent that that auction model didn’t extend well to everything. That core monopoly business turned out to be an auction marketplace for somewhat unique products, like coins and stamps, for which there was intense demand but limited supply. The auction model was much less successful for commodity-like products, which companies like Amazon, Overstock, and Buy.com dealt in. eBay still turned out to be a great monopoly business. It’s just a smaller one than people thought it would be in 2004.
LinkedIn has 61 million users in the U.S. and 150 million worldwide. The idea was that it would be a network for everyone. The reality is that it’s largely just used for headhunting. Some have proposed a unique long/short strategy utilizing that insight: short the companies where lots of people are joining LinkedIn to post résumés and look for jobs, and go long the companies who are suspiciously quiet on LinkedIn. The big question about LinkedIn is whether the business network is the same as the social network. LinkedIn’s narrative is that the business network is fundamentally discrete. If that’s true, it will probably own that market for a long time.
Twitter is a classic example of starting with a small, niche product. The idea was simply that anyone can become a microbroadcaster. It works even if you just do it with a small number of people. But as it scales you basically build a new media distribution center. The big question about Twitter is whether it will ever make any money. That’s not an easy question to answer. But if you ask the future tech questions—Do you have a technological advantage? Do you have a moat? Can people replicate this?—Twitter seems safe. If Twitter’s market is the market for sending messages of 140 characters or less, it would be incredibly hard to replicate it. Sure, you can copy it. But you can’t replicate it. Indeed, it’s almost impossible to imagine a technological future where you can compete with Twitter. Move to 141 characters and you break SMS compatibility. Go down to 139 and you’re just missing a character. So while monetization is an open question, Twitter’s robustness and durability are hard to beat.
Zynga is another interesting case. Mark Pincus has wisely said that, “Not having clear goal at outset leads to death by a thousand compromises.” Zynga executed very well from the beginning. They started doing social games like Farmville. They aggressively copied what worked, scaled, figured out how to monetize these games—how to get enough users to pay for in-game perks—better than anyone else did. Their success with monetization drove the viral loop and allowed them to get more customers quickly.
The question about Zynga is how durable it is. Is it a creative or non-creative business? Zynga wants the narrative to be that it’s not a creative or a design company. If it is, the problem is that coming up with new great games is hard. Zynga would basically just be game version of a Hollywood studio whose fortunes can rise or fall with the seasons. Instead, Zynga wants the narrative to be about hardcore psychometric sauce. It’s a better company if it’s figured out how psychological and mathematical laws give it permanent monopoly advantages. Zynga wants, perhaps needs, to be able to truthfully say, “we know how to make people buy more sheep, and therefore we are a permanent monopoly.”
Groupon also started small and scaled up aggressively. The questions for Groupon is what is the relevant market and how can they own it. Groupon insists it’s a brand; it’s penetrated to all these cities, and people look to it, not others, for deals. The anti-Groupon angle is that it has no proprietary technology and no network effects. If the branding isn’t as strong as Groupon says it is, it will face lots of challenges in the long term.
All these companies are different, but the pattern is the same: start with a small, specific market, scale up, and always have an account of how robust you are going forward. The best way to fail is to invert this recipe by starting big and shrinking. Pets.com, Webvan, and Kozmo.com made this mistake. There are many modes of failure. But not being honest about objective market conditions is a sort of failure paradigm. You can’t succeed by believing your own rhetoric over reality except by luck.
V. Tech Frontiers
There is always some room to operate in existing markets. Instead of creating a new market, you could “disrupt” existing industries. But the disruptive tech story is possibly overdone. Disruptive companies tend not to succeed. Disruptive kids get sent to principal’s office. Look at Napster. Napster was certainly disruptive…probably too disruptive. It broke too many rules and people weren’t ready for it. Take the name itself: Napster. It sounds disruptive. But what kinds of things can one “nap”? Music and kids. Yikes. Much better than to disrupt is to find a frontier and go for it.
But where is the frontier in technology? How should we begin to think about it? Here is one possible framework. Picture the world as being covered by ponds, lakes, and oceans. You’re in a boat, in a body of water. But it’s extremely foggy, so you don’t know how far it is to the other side. You don’t know whether you’re in a pond, a lake, or an ocean.
If you’re in a pond, you might expect the crossing to take about an hour. So if you’ve been out a whole day, you’re either in a lake or an ocean. If you’ve been out for a year, you’re crossing an ocean. The longer journey, the longer your expected remaining journey. It’s true that you’re getting closer to reaching the other side as time goes on. But here, time passing is also indicative that you still have quite a ways to go.
So where are the places where technology is happening? Where is there room for the journey to continue? The frontier is a promising place, but also a very uncertain one. You can imagine a tech market where nothing is happening for a long time, things suddenly start to happen, and then it all stops. The tech frontier is temporal, not geographical. It’s when things are happening.
Consider the automotive industry. Trying to build a car company in the 19thcentury was a bad idea. It was too early. But it’s far too late to build a traditional car company today. Car companies—some 300 of them, a few of which are still around—were built in 20th century. The time to build a car company was the time when car technology was being created—not before, and not after.
We should ask ourselves whether the right time to enter a tech industry is early on, as conventional wisdom suggests. The best time to enter may be much later than that. It can’t be too late, since you still need room to do something. But you want to enter the field when you can make the last great development, after which the drawbridge goes up and you have permanent capture. You want to pick the right time, go long on tech, succeed, and then short tech.
Microsoft is probably the last operating system company. It was also an early one, but there’s a sense in which it will be the last as well. Google, the narrative goes, is the last search engine company; it wrought a quantum improvement in search with its shift to an algorithmic approach, and that can’t be much improved on. What about bioinformatics? A lot seems to be happening there. But whether it’s too early to jump in is hard to know. The field seems very promising. But it’s difficult to get a sense of where it will likely be in 15 or 20 years. Since the goal is to build companies that will still be around in 2020, you want to avoid a field where things are moving too quickly. You want to avoid being an innovative but non-profitable disk drive company from the ‘80s.
Some markets are like the automotive market. Should you start a new lithium battery company? Probably not. The time for that may have passed. Innovation may be too slow. The technology may be too set by now.
But sometimes seemingly terminal markets aren’t. Look at aerospace. SpaceX thinks it can cut space launch costs by 70-90%. That would be incredibly valuable. If nothing has happened in an industry for a long time, and you come along and dramatically improve something important, chances are that no one else will come and do that again, to you.
Artificial Intelligence is probably an underrated field. People are burned out on it, largely because it has been overrated and overstated for many decades. Few people think AI is or will soon be real at this point. But progress is increasingly relentless. AI performance in chess is increasing. Computers will probably beat humans in Go in 4 or 5 years. AI is probably a good place to look on the tech frontier. The challenge is that no one knows how far it will go.
Mobile Internet deserves some mention. The question is whether there’s a gold rush in mobile. An important subquestion is whether, given a gold rush, you’d rather be a gold digger or the guy selling shovels to gold diggers. But Google and Apple are selling the shovels. And there may not be that much gold left to find. The worry is that the market is just too big. Too many companies are competing. As discussed above, there are various rhetorical tricks one can use to whittle down the market size and make any given company seem way more unique. Maybe you can create a mobile company that owns a valuable niche. Maybe you can find some gold. But that’s worth being skeptical about.
VI. Frontiers and People
One way to tell whether you’ve found a good frontier is to answer the question “Why should the 20th employee join your company?” If you have a great answer, you’re on the right track. If not, you’re not. The problem is the question is deceptively easy sounding.
So what makes for a good answer? First, let’s put the question in context. You must recognize that your indirect competition for good employees is companies like Google. So the more pointed version of the question is: “Why would the 20thengineer join your company when they could go to Google instead and get more money and prestige?”
The right answer has to be that you’re creating some sort of monopoly business. Early businesses are driven by the quality of the people involved with them. To attract the best people, you need a compelling monopoly story. To the extent you’re competing with Google for talent, you must understand that Google is a great monopoly business. You probably should not compete with them at their core monopoly business of search. But in terms of hiring, you simply can’t compete with a great monopoly business unless you have a powerful narrative that has you becoming a great monopoly business too.
This raises the question that we’ll discuss next week: kinds of people do you want to take with you as you head off into the frontier?
David Skok • February 17, 2010
This blog post looks at the high level goals of a SaaS business and drills down layer by layer to expose the key metrics that will help drive success. Metrics for metric’s sake are not very useful. Instead the goal is to provide a detailed look at what management must focus on to drive a successful SaaS business. For each metric, we will also look at what is actionable.
Before going any further, I would like to thank the management team at HubSpot, and Gail Goodman of Constant Contact, who sits on the HubSpot board. A huge part of the material that I write about below comes my experiences working with them. In particular HubSpot’s management team is comprised of a group of very bright individuals that are all very metrics driven, and they have been clear thought leaders in developing the appropriate tools to drive their business. I’d also like to thank John Clancy, who until recently was President of Iron Mountain Digital, a $230m SaaS business, and Alastair Mitchell, CEO and founder of Huddle.
Let’s start by looking at the high level goals, and then drill down from there:
Key SaaS Goals
- Profitability: needs no further explanation.
- MRR Monthly Recurring Revenue: In a SaaS business, one of the most important numbers to watch is MRR. It is likely a key contributor to Profitability.
- Cash: very critical to watch in a SaaS business, as there can be a high upfront cash outlay to acquire a customer, while the cash payments from the customer come in small increments over a long period of time. This problem can be somewhat alleviated by using longer term contracts with advance payments.
- Months to recover CAC: one of the best ways to look at the capital efficiency of your SaaS business is to look at how many months of revenue from a customer are required to recover your cost of acquiring that customer(CAC). In businesses such as banking and wireless carriers, where capital is cheap and abundant, they can afford a long payback period before they recover their investment to acquire a customer (typically greater than one year). In the startup world where capital is scarce and expensive, you will need to do better. My own rule says that startups need to recover their cost of customer acquisition in less than 12 months.
(Note: there are other web sites and blogs that talk about the CAC ratio, with a complex formula to calculate it. This is effectively a more complicated way of saying the same thing. However I have found that most people cannot relate well to the notion of a CAC ratio, but they can easily relate to the idea of how many months of revenue it will take to recover their investment to acquire a customer. Hence my preference for the term Months to Recover CAC.)
- Months to recover CAC: one of the best ways to look at the capital efficiency of your SaaS business is to look at how many months of revenue from a customer are required to recover your cost of acquiring that customer(CAC). In businesses such as banking and wireless carriers, where capital is cheap and abundant, they can afford a long payback period before they recover their investment to acquire a customer (typically greater than one year). In the startup world where capital is scarce and expensive, you will need to do better. My own rule says that startups need to recover their cost of customer acquisition in less than 12 months.
- Growth: usually a critical success factor to gaining market leadership. There is clear evidence that once one company starts to emerge as a market leader, there is a cycle of positive reinforcement, as customers prefer to buy from the market leader, and the market leader gets the most discussion in the press, blogosphere, and social media.
Two Key Guidelines for SaaS startups
The above guidelines are not hard and fast rules. They are what I have observed to be needed by looking at a wide variety of SaaS startups. As a business moves past the startup stage, these guidelines may be relaxed.
In the next sections, we will drill down on the high level SaaS Goals to get to the components that drive each of these.
Three ways to look at Profitability
- Micro-Economics (per customer profitability): Micro-economics is the term used to describe looking at the economics of your business on a single customer level. Most business models (with a few exceptions such as marketplaces) are based around a simple principle: acquire customers and then monetize them. Micro-economics is about measuring the numbers behind these two essential ingredients of a customer interaction. The goal is to make sure the fundamental underpinnings of your business are sound: how much it cost to acquire your customers, and how much you can monetize them. i.e. CAC and LTV (cost of acquiring a customer, and lifetime value of the customer). In a SaaS business, you have a great business if LTV is significantly greater than CAC. My rule of thumb is that LTV must be at least 3x greater than CAC. (As mentioned elsewhere in this blog, your startup will die if your long term number for CAC is higher than your LTV. See Startup Killer: The cost of acquiring customers.)
- Overall profitability (standard accounting method): This looks a the standard accounting way of deriving profitability: revenue – COGS – Expenses. The diagram also notes that Revenue is made up of MRR + Services Revenue. Since MRR is such a critical element, there will be a deeper drill down to understand the key component drivers.
- Profitability per Employee: it can be useful to look at the factors contributing to profitability on a per employee basis, and benchmark your company against the rest of the industry. Expenses per Employee is usually around $180-200k annually for businesses with all their employees in the US. (To calculate the number take the total of all expenses, not just salaraies, and divide by the number of employees.) Clearly to be profitable in the long term, you will want to see revenue per employee climb to be higher than expenses, taking into account your gross margin %.
Drill down on MRR
MRR is computed by multiplying the total number of paying customers by the average amount that they pay you each month (ARPU).
- Total Customers: a key metric for any SaaS company. This increases with new additions coming out the bottom of the sales funnel, and decreases by the number of customers that churn. Both of these are key metrics, and we will drill down into them later.
- ARPU – average monthly revenue per customer: (The term ARPU comes from the wireless carriers where U stands for user.) This is another extremely imporant variable that can be tweaked in the SaaS model. If you read my blog post on the JBoss story, you will see that one of the key ways that we grew that business was to take the average annual deal size from $10k, to $50k. Given that the other parts of the pipeline worked with the same numbers and conversion rates, this grew the business by 5x. We will drill down into how you can do the same thing a little further on.
Drill down on Micro-Economics (Per Customer Profitability)
Our goal is to see a graph that looks like the following:
To achieve this, lets look at the component parts of each line, to see what variables we can use to drive the curves:
As mentioned earlier, customer profitability = LTV – CAC.
Drill down on LTV
Drilling down into the factors affecting LTV, we see the following:
LTV = ARPU x Average Lifetime of a Customer – the Cost to Serve them (COGS)
It turns out that the Average Lifetime of a Customer is computed by 1/Churn Rate. As an example, if a you have a 50% churn rate, your average customer lifetime will be 1 divided by 50%, or 2 months. In most companies that I work with, they ignore tracking the average lifetime, but instead track the monthly churn rate religiously.
The importance of a low churn rate cannot be overstated. If your churn rate is high, then it is a clear indication of a problem with customer satisfaction. We will drill down later into how you can measure the factors contributing to Churn Rate, and talk about how you can improve them.
Drill down on CAC
The formula to compute CAC is:
CAC = Total cost of Sales & Marketing / No of Deals closed
It turns out that we are actually interested in two CAC numbers. One that looks purely at marketing program costs, and one that also takes into consideration the people and other expenses associated with running the sales and marketing organization. The first of these gives us an idea of how well we could do if we have a low touch, or touchless sales model, where the human costs won’t rise dramatically over time as we grow the lead flow. The second number is more important for sales models that require more human touch to close the deal. In those situations the human costs will contribute greatly to CAC, and need to be taken into consideration to understand the true micro-economics.
I am often asked when it is possible to start measuring this and get a realistic number. Clearly there is no point in measuring this in the very early days of a startup, when you are still trying to refine product/market fit. However as you get to the point of having a repeatable sales model, this number becomes important, as that is the time when you will usually want to hit the accelerator pedal. It would be wrong to hit the accelerator pedal on a business that has unprofitable micro-economics. (When you are computing the costs for a very young company, it would be fair to remove the costs for people like the VP of Sales and VP of Marketing, as you will not hire more of these as you scale the company.)
When we look at how to lower CAC, there are a number of important variables that can be tweaked:
- Sales Funnel Conversion rates: a funnel that takes the same number of leads and converts them at twice the rate, will not only result in 2x more closed customers, but will also lower CAC by half. This is a very important place to focus energy, and a large part of this web site is dedicated to talking about how to do that. We will drill down into the Sales Funnel conversion rates next.
- Marketing Program Costs: driving leads into the top of your sales funnel will usually involve a number of marketing programs. These could vary from pay per click advertising, to email campaigns, radio ads, tradeshows, etc. We will drill down into how to measure and control these costs later.
- Level of Touch Required: a key factor that affects CAC is the amount of human sales touch required to convert a lead into a sale. Businesses that have a touchless conversion have spectacular economics: you can scale the number of leads being poured into the top of the funnel, and not worry about growing a sales organization, and the associated costs. Sadly most SaaS companies that I work with don’t have a touchless conversion. However it is a valuable goal to consider. What can you do to simplify both your product and your sales process to lower the amount of touch involved? This topic is covered at the bottom of a prior blog post: Startup Killer: the cost of acquiring customers.
- Personnel costs: this is directly related to the level of touch required. To see if you are improving both of these, you may find it useful to measure your Personnel costs as a % of CAC over time.
Drill down on Sales Funnel Conversion Rates
The metrics that matter for each sales funnel, vary from one company to the next depending on the steps involved in the funnel. However there is a common way to measure each step, and the overall funnel, regardless of your sales process. That involves measuring two things for each step: the number of leads that went into the top of that step, and the conversion rate to the next step in the funnel (see below).
You will also want to measure the overall funnel effectiveness by measuring the number of leads that go into the top of the funnel, and the conversion rate for the entire funnel process to signed customers.
The funnel diagram above shows a very simple process for a SaaS company with a touchless conversion. If you have a conversion process involving a sales organization, you will want to add those steps to the funnel process to get insights into the performance of your sales organization. For example, your inside sales process might look like the following:
Here if we look at the closed deals and overall conversion rates by sales rep, we will have a good idea of who our best reps are. For lower performing reps, it is useful to look at the intermediate conversion rates, as someone that is doing a poor job of, say, converting demos to closed deals could be an indication that they need demo training from people that have high conversion rates for demos. (Or, as Mark Roberge, VP of Sales at HubSpot, pointed out, it could also mean that they did a poor job of qualifying people that they put into the Demo stage.)
These metrics give you the insight you need into your sales and marketing machine, and those insights give you a roadmap for what actions you need to take to improve conversion rates.
Using Funnel Metrics in forward planning
Another key value of having these conversion rates is the ability to understand the implications of future forecasts. For example, lets say your company wants to do $4m in the next quarter. You can work backwards to figure out how many demos/trials that means, and given the sales productivity numbers – how many salespeople are required, and going back a stage earlier, how many leads are going to be required. These are crucial planning numbers that can change staffing levels, marketing program spend levels, etc.
Drill down by Customer Type
If you have different customer types, you will want to look at all the CAC and LTV metrics for each different customer type, to understand the profitability by customer type. Often times this can lead you to a decision to focus more energy on the most profitable customer type.
Drill down into ROI per Marketing Program
My experiences with SaaS startups indicate that they usually start with a couple of lead generation programs such as Pay Per Click Google Ad-words, radio ads, etc. What I have found is that each of these lead sources tends to saturate over time, and produce less leads for more dollars invested. As a result, SaaS companies will need to be constantly evaluating new lead sources that they can layer in on top of the old to keep growing.
Since the conversion rates and costs per lead vary quite considerably, it is important to also measure the overall ROI by lead source:
Growing leads fast enough to feed the front end of the funnel is one of the perennial challenges for any SaaS company, and is likely to be one of the greatest limiting factors to growth. If you are facing that situation, the most powerful advice I can give you is to start investing in Inbound Marketing techniques (see Get Found using Inbound Marketing). This will take time to ramp up, but if you can do it well, will lead to far lower lead costs, and greater scaling than other paid techniques. Additionally the typical SaaS buyer is clearly web-savvy, and therefore very likely to embrace inbound marketing content and touchless selling techniques.
From Alistair Mitchell, CEO of Huddle: “Just calculating CAC can be extremely complicated, given the numerous ways in which people find out about your service. To stop getting too bogged down in the detail, its best to start with a blended rate that just takes your total spend on marketing (people, pr, acquisition etc) and split this across all your customers, regardless of type or source. Then, once you’ve got comfortable with that, you can start to break CAC down by the different customer types and elements of your inbound funnel, and start measuring specific campaigns for their contribution to each customer type.”
Drill down into Churn Rate
As described in the section on LTV, Churn Rate has a direct effect on LTV. If you can halve your churn rate, it will double your LTV. It is an enormously important variable in a SaaS business. Churn can usually be attributed to low customer satisfaction. We can measure customer satisfaction using customer surveys, and in particular, theNet Promoter Score.
If you are using longer term contracts, another key metric to focus on is renewals. From John Clancy, ex-President of Iron Mountain Digital: “
Non-renewals add to churn, but they can have different drivers. We spent a lot of time examining our renewal rates and found that a single digit improvement made a huge difference. Often times the driver on a non-renewal is economic – the internal IT department has mounted a campaign to bring the solution back in house. SaaS businesses need to identify renewal dates and treat the renewal as a sales cycle (it’s much easier and less expensive than a new sale, but it deserves the same level of attention) Many SaaS businesses make the mistake of taking renewals for granted.”
A good predictor of when a customer is about to churn is their product usage pattern. Low levels of usage indicate a lack of commitment to the product. It can be a good idea to instrument the product to measure this, looking for particular features our usage patterns that are correlated with stickiness, or a likelihood to churn.
Another measurement tool that can be very useful in understanding churn is to look at a Cohort Analysis. The term cohort refers to a group of customers that started in the same month. The reason for doing this is that churn varies over time, and using a single churn number for all customers will mask this. Cohort analysis shows:
- How churn varies over time (the green call out below).
- How churn rates are changing with newer cohorts, (the red call out below) For example in the early days of your SaaS company, you may have serious product problems and lose a lot of customers in the first month. Over time your product gets better, and the first month churn rate will drop.
Cohort analysis will show this, instead of mixing all the churn rates into single number.
Here’s a comment on Cohort Analysis from Alastair Mitchell, CEO of Huddle: “I actually think this is more important than churn, for the simple fact that churn varies over the lifetime of a customer cohort, and just looking at monthly churn can be very misleading. Also, given the importance of payback in a year – you really want to look at churn over the course of a 12 months cohort. For instance, in the first 3 months of a monthly paying customer you will see high churn (3 is a recurring ‘magic’ number in all of retail), then reduced churn (sometimes even positive churn) over the next 3 months less and then probably more stable spend over the next 6 months. The number you really care about is the % of customers spending after 12 months (not necessarily on a monthly basis) as that’s what matters for your CAC payback calculations.”
Two variables that really matter
As we saw above, there are two variables that have a huge effect on a SaaS business: funnel conversion rate, and churn, and it is not a bad idea to graph them as shown below.
Drill down into ARPU (Average Revenue per Customer)
ARPU is often different for different customer categories, and should be measured separately for each category. It can usually be driven up by focusing on:
- Product Mix: adding products to the range, and using bundles, and cross-sell and up-sell
- Scalable Pricing: there are always some customers that are willing to pay more for your product than others. The trick is developing a multi-dimensional pricing matrix that allows you to scale pricing for larger customers that derive more value from the product. This could be pricing by the seat used (Salesforce.com), or by some other metric such as number of individuals mailed in email campaigns (Eloqua).
If you are using scalable pricing, it will be valuable to measure what the distribution is of customers along the various axes. You could imagine taking an action to do after more seats inside of existing customers as a way to drive more revenue. etc.
Drill down into Cash
We already discussed Months to recover CAC as a key variable. There is another way to affect Cash: which is using longer term contracts and incenting your customers to pay for 6, 12, 24, or even 36 months up front in advance. This can mean the difference between needing to raise tons of venture capital and giving away ownership, or being able to grow the business in a self-funded manner. Given the cost of capital, you can often calculate what discount makes sense. (If capital is cheap and freely available, it doesn’t make sense to give much discount.)
If you do use longer term contracts, it will be important to measure “Discretionary Churn”. Since some of your customers are locked in and cannot churn, they could artificially lower your overall churn numbers. The way to understand what is really going on is to look at the discretionary churn, which is the churn rate for all customers that are at the point where they have the option to churn, removing those whose contracts would have prevented them from churning.
Cash Management and forecasting
Cash is one of the most important items to get right in any startup. Run out of cash, and your business will come grinding to a halt regardless of how good any of your other metrics may be. One of the most important ways to run a SaaS company is to look at CashFlow profitability (not recognized revenue profitability). What is the difference: If your business only gets paid month by month, there will be no difference, but if you get longer term contracts, and get paid in advance, you will receive more cash upfront than you can recognize as revenue, so your cash flow profitability will look better than your revenue profitability, and is a more realistic view of whether you can survive day to day on the money coming in the door.
Here is another comment from Alastair Mitchell of Huddle on this topic: “SaaS companies tuning their model should think not just in terms of the months to recover CAC, but also the topline amount of cash required to get to cashflow profitability (or the next funding round). This is probably the single biggest mistake I see in early stage companies. They don’t look ahead, using these metrics, to figure out that if the time to repay CAC is 12 months, then in aggregate they are going to need 12 months of CAC spend PLUS the number of months required of further growth to cover their operating costs (mostly engineering) BEFORE they are even cashflow positive (let alone revenue profitability). Most businesses I see fundamentally miss this and end up short; frequently through under-estimating the time to recover CAC, and churn. The readers of this blog should be focused on cashflow profitability, not revenue profitability. (Hence why your point about annual/upfront contracts is so important)”
Drill down into Growth
Focusing on Growth as a separate parameter can be highly valuable. It is the nature of a SaaS business to grow MRR month on month, even if you only added the same number of customers every month. However your goal should be to grow the number of new customers that you sign up every month. You can do this by focusing on:
- Improvement in the overall funnel conversion rate
- Lead Generation Growth
- Growth in Funnel Capacity
The first two have been covered already. The last bullet: Growth in Funnel Capacity is an often overlooked metric that can bite you unexpectedly if you don’t pay attention to it. In my second startup, I had a situation where sales growth stalled after growing extremely rapidly for a couple of years. The problem, as it turned out, was that we had stopped hiring new sales people after reaching 20 people, a number that felt very large to me, and had maxed out on sales capacity. We started sales hiring again, and a couple of years later the business hit a $100m run rate. I witnessed a similar phenomenon at Solidworks, when after 2-3 years of phenomenal growth, their growth slowed. It turned out that their channel sales capacity had stopped growing. Solidworks started measuring and managing something that would later turn out to be a critical metric: channel capacity in terms of the number of FTE (Full Time Equivalent) sales people in their channel, and the average productivity per FTE. This has helped propel them to over $400m in annual revenues.
Another great way to grow your business is by adding new products that can be up-sold, or product features that can lead to a higher price point. Since you already have a billable contract, it is extremely easy to increase the amount being charged, and this can often be done with a touchless sale.
There are a series of less important metrics that can still be useful to be aware of. I have listed some of these in the diagrams below:
After posting the above, I received a note from Gail Goodman of Constant Contact, noting that they include the cost of on-boarding a customer in CAC, not LTV as I have shown. Given that they are a public company with significant accounting scrutiny, this is likely the right way to do things.
If you have kept reading this long, it likely means that you are likely an executive in a SaaS company, and truly have a reason to care about this depth of analysis. I would very much like to hear from you in the comments section below to see if I have missed out on metrics that you think are important.
The main conclusion to draw from this article, is that a SaaS business can be optimized in many ways. This article aims to help you understand what the levers are, and how they can affect the key goals of Profitability, Cash, Growth, and market share. To pull those levers requires that you first measure the variables, and watch them as they change over time.
It also requires that you implement a very metrics driven culture, which can only be done from the top. The CEO needs to use these metrics in her staff meetings, and those execs need to use them with their staff, etc. Human nature is such that if you show someone a metric, they will automatically work to try to improve it. That kind of a culture will lead to true operational excellence, and hopefully great success.
Apr 21 2010 by Cameron Chapman
Computers have wedged themselves into every facet of our lives—they are what we would use as the symbolic representation of the modern world.
But did you know that the history of computers dates back to the 1800s?
Indeed, the history and evolution of computers is quite extraordinary—and with many early computing technology innovations tied to defense contracts, much of this information were kept secret from the public for decades. In this article, we explore the development and progression of computers.
Mid-1800s-1930s: Early Mechanical Computers
The first computers were designed by Charles Babbage in the mid-1800s, and are sometimes collectively known as the Babbage Engines. These include the Difference Engine No. 1, the Analytical Engine, and the Difference Engine No. 2.
The Difference Engine was constructed from designs by Charles Babbage. Photo by Allan J. Cronin
These early computers were never completed during Babbage’s lifetime, but their complete designs were preserved. Eventually, one was built in 2002.
While these early mechanical computers bore little resemblance to the computers in use today, they paved the way for a number of technologies that are used by modern computers, or were instrumental in their development. These concepts include of the idea of separating storage from processing, the logical structure of computers, and the way that data and instructions are inputted and outputted.
Other important mechanical computers are the Automatic Electrical Tabulating Machine—which was used in the U.S. Census of 1890 to handle data from more than 62 million Americans—and the first binary computer: Konrad Zuse’s Z1, which was developed in 1938 and was the precursor to the first electro-mechanical computer.
1930s: Electro-Mechanical Computers
Electro-mechanical computers generally worked with relays and/or vacuum tubes, which could be used as switches.
Some electro-mechanical computers—such as the Differential Analyzer built in 1930—used purely mechanical internals but employed electric motors to power them.
These early electro-mechanical computers were either analog or were digital—such as the Model K and the Complex Number Calculator, both produced by George Stibitz.
Stibitz, by the way, was also responsible for the first remote access computing, done at a conference at Dartmouth College in New Hampshire. He took a teleprinter to the conference, leaving his computer in New York City, and then proceeded to take problems posed by the audience. He then entered the problems on the keypad of his teleprinter, which outputted the answers afterward.
It was during the development of these early electro-mechanical computers that many of the technologies and concepts still used today were first developed. The Z3, a descendent of the Z1 developed by Konrad Zuse, was one such pioneering computer. The Z3 used floating-point numbers in computations and was the first program-controlled digital computer.
Other electro-mechanical computers included Bombes, which were used during WWII to decrypt German codes.
1940s: Electronic Computers
The first electronic computers were developed during the World War II, with the earliest of those being the Colossus. The Colossus was developed to decrypt secret German codes during the war. It used vacuum tubes and paper tape and could perform a number of Boolean (e.g. true/false, yes/no) logical operations.
Another notable early electronic computer was nicknamed “The Baby” (officially known as the Manchester Small-Scale Experimental Machine). While the computer itself wasn’t remarkable—it was the first computer to use the Williams Tube, a type of random access memory (RAM) that used a cathode-ray tube.
Some early electronic computers used decimal numeric systems (such as the ENIAC and the Harvard Mark 1), while others—like the Atanasoff-Berry Computer and the Colossus Mark 2—used binary systems. With the exception of the Atanasoff-Berry Computer, all the major models were programmable, either using punch cards, patch cables and switches, or through stored programs in memory.
1950s: The First Commercial Computers
The first commercially available computers came in the 1950s. While computing up until this time had mainly focused on scientific, mathematical, and defense capabilities, new computers were designed for business functions, such as banking and accounting.
The J. Lyons Company, which was a British catering firm, invested heavily in some of these early computers. In 1951, LEO (Lyons Electronic Office) became the first computer to run a regular routine office job. By November of that year, they were using the LEO to run a weekly bakery valuations job.
The UNIVAC was the first mass-produced computer.
The UNIVAC was the first commercial computer developed in the U.S., with its first unit delivered to the U.S. Census Bureau. It was the first mass-produced computer, with more than 45 units eventually produced and sold.
The IBM 701 was another notable development in early commercial computing; it was the first mainframe computer produced by IBM. It was around the same time that theFortran programming language was being developed (for the 704).
A smaller IBM 650 was developed in the mid-1950s, and was popular due to its smaller size and footprint (it still weighed over 900kg, with a separate 1350kg power supply).
They cost the equivalent of almost $4 million today (adjusted for inflation).
Mid-1950s: Transistor Computers
The development of transistors led to the replacement of vacuum tubes, and resulted in significantly smaller computers. In the beginning, they were less reliable than the vacuum tubes they replaced, but they also consumed significantly less power.
These transistors also led to developments in computer peripherals. The first disk drive, the IBM 350 RAMAC, was the first of these introduced in 1956. Remote terminals also became more common with these second-generation computers.
1960s: The Microchip and the Microprocessor
The microchip (or integrated circuit) is one of the most important advances in computing technology. Many overlaps in history existed between microchip-based computers and transistor-based computers throughout the 1960s, and even into the early 1970s.
Micochips allowed the manufacturing of smaller computers. Photo by Ioan Sameli
The microchip spurred the production of minicomputers and microcomputers, which were small and inexpensive enough for small businesses and even individuals to own. The microchip also led to the microprocessor, another breakthrough technology that was important in the development of the personal computer.
There were three microprocessor designs that came out at about the same time. The first was produced by Intel (the 4004). Soon after, models from Texas Instruments (the TMS 1000) and Garret AiResearch (the Central Air Data Computer, or CADC) followed.
The first processors were 4-bit, but 8-bit models quickly followed by 1972.
16-bit models were produced in 1973, and 32-bit models soon followed. AT&T Bell Labs created the first fully 32-bit single-chip microprocessor, which used 32-bit buses, 32-bit data paths, and 32-bit addresses, in 1980.
The first 64-bit microprocessors were in use in the early 1990s in some markets, though they didn’t appear in the PC market until the early 2000s.
1970s: Personal Computers
The first personal computers were built in the early 1970s. Most of these were limited-production runs, and worked based on small-scale integrated circuits and multi-chip CPUs.
The Commodore PET was a personal computer in the 70s. Photo by Tomislav Medak
The Altair 8800 was the first popular computer using a single-chip microprocessor. It was also sold in kit form to electronics hobbyists, meaning purchasers had to assemble their own computers.
Clones of this machine quickly cropped up, and soon there was an entire market based on the design and architecture of the 8800. It also spawned a club based around hobbyist computer builders, the Homebrew Computer Club.
1977 saw the rise of the “Trinity” (based on a reference in Byte magazine): the Commodore PET, the Apple II, and the Tandy Corporation’s TRS-80. These three computer models eventually went on to sell millions.
These early PCs had between 4kB and 48kB of RAM. The Apple II was the only one with a full-color, graphics-capable display, and eventually became the best-seller among the trinity, with more than 4 million units sold.
1980s-1990s: The Early Notebooks and Laptops
One particularly notable development in the 1980s was the advent of the commercially available portable computer.
Osborne 1 was small and portable enough to transport. Photo by Tomislav Medak
The first of these was the Osborne 1, in 1981. It had a tiny 5″ monitor and was large and heavy compared to modern laptops (weighing in at 23.5 pounds). Portable computers continued to develop, though, and eventually became streamlined and easily portable, as the notebooks we have today are.
These early portable computers were portable only in the most technical sense of the word. Generally, they were anywhere from the size of a large electric typewriter to the size of a suitcase.
The Gavilan SC was the first PC to be sold as a “laptop”.
The first laptop with a flip form factor, was produced in 1982, but the first portable computer that was actually marketed as a “laptop” was the Gavilan SC in 1983.
Early models had monochrome displays, though there were color displays available starting in 1984 (the Commodore SX-64).
Laptops grew in popularity as they became smaller and lighter. By 1988, displays had reached VGA resolution, and by 1993 they had 256-color screens. From there, resolutions and colors progressed quickly. Other hardware features added during the 1990s and early 2000s included high-capacity hard drives and optical drives.
Laptops typically come in three categories, as shown by these Macbooks. Photo by Benjamin Nagel
Laptops are generally broken down into a three different categories:
- Desktop replacements
- Standard notebooks
Desktop replacements are usually larger, with displays of 15-17″ and performance comparable with some better desktop computers.
Standard notebooks usually have displays of 13-15″ and are a good compromise between performance and portability.
Subnotebooks, including netbooks, have displays smaller than 13″ and fewer features than standard notebooks.
2000s: The Rise of Mobile Computing
Mobile computing is one of the most recent major milestones in the history of computers.
Many smartphones today have higher processor speeds and more memory than desktop PCs had even ten years ago. With phones like the iPhone and the Motorola Droid, it’s becoming possible to perform most of the functions once reserved for desktop PCs from anywhere.
The Droid is a smartphone capable of basic computing tasks such as emailing and web browsing.
Mobile computing really got its start in the 1980s, with the pocket PCs of the era. These were something like a cross between a calculator, a small home computer and a PDA. They largely fell out of favor by the 1990s. During the 1990s, PDAs (Personal Digital Assistant) became popular.
A number of manufacturers had models, including Apple and Palm. The main feature PDAs had that not all pocket PCs had was a touchscreen interface. PDAs are still manufactured and used today, though they’ve largely been replaced by smartphones.
Smartphones have truly revolutionized mobile computing. Most basic computing functions can now be done on a smartphone, such as email, browsing the internet, and uploading photos and videos.
Late 2000s: Netbooks
Another recent progression in computing history is the development of netbook computers. Netbooks are smaller and more portable than standard laptops, while still being capable of performing most functions average computer users need (using the Internet, managing email, and using basic office programs). Some netbooks go as far as to have not only built-in WiFi capabilities, but also built-in mobile broadband connectivity options.
The first mass-produced netbook was the Asus Eee PC 700, released in 2007. They were originally released in Asia, but were released in the US not long afterward.
Other manufacturers quickly followed suit, releasing additional models throughout 2008 and 2009.
One of the main advantages of netbooks is their lower cost (generally ranging from around US$200-$600). Some mobile broadband providers have even offered netbooks for free with an extended service contract. Comcast also had a promotion in 2009 that offered a free netbook when you signed up for their cable internet services.
Most netbooks now come with Windows or Linux installed, and soon, there will be Android-based netbooks available from Asus and other manufacturers.
The history of computing spans nearly two centuries at this point, much longer than most people realize. From the mechanical computers of the 1800s to the room-sized mainframes of the mid-20th century, all the way up to the netbooks and smartphones of today, computers have evolved radically throughout their history.
The past 100 years have brought technological leaps and bounds to computing, and there’s no telling what the next 100 years might bring.
Also follow these links for great timelines: