Xobni Man Walking Some thoughts from an entrepreneur

My startup self was born on March 14, 2006. It started asexually, created by its only parent, Y Combinator. I was such an infant. I didn’t know how to do anything other than write code. I could write code really well, but that was it.

During my early years I ran into walls and tripped on toys. We spent five months making a product that nobody really wanted. We missed a key hiring opportunity. I was the only one doing software development.

My teens were filled with growing pains and an identity crisis. Talking to investors constantly, doing a financing, setting up an office, and starting to act like a “real” company. *Wince*

My startup persona is a little older now. I’m learning more than ever, and making more mistakes than ever before. But I am taking more measured risks and making fewer rash decisions. I have some familiarity with the startup world, and have an easier time facing reality. I feel like I’m in my early twenties startup wise. There’s still a long way to go, and it will probably never stop.

People like Paul Graham, Josh Kopelman, and Rob Hayes have been around a while and pretty much know how to kick ass at everything. They’re senior citizens.

Vinod Khosla is 100 years old. You know he knows how to do everything, from chasing ladies (raising money) to retired travel in foreign countries (sitting on the boards of Fortune 500 companies).

I might be completely off the mark when I say I’m in my startup early twenties. Making a statement like that is a huge risk. There’s a reasonable chance that I’m way off the mark, and that five years from now I’ll be laughing at myself. But, hell, guessing is fun.

I’ve been thinking about why programmers/engineers are bad at selling things – products, ideas, themselves.

First, engineers suck at spin. They deal in facts, not emotions. The suspension bridge is going to stay up or collapse. The software works or it doesn’t, and no amount of framing, rhetoric, or rapport will change the facts. An engineer who spins things to themselves or others would be a bad engineer; facts are king.

It gets worse. Not only do engineers focus on facts, they focus on the negative.

Programmers survive by paying attention to the ugliness in their code. If you wake me up in the middle of the night and ask what I’m dreaming about, I’ll probably tell you what the two ugliest parts of our code are, and how I’m going to fix each of them.

So when someone asks me about the code, my instinct is to describe the bugs. They’re just the first order of business! We have a natural tendency to look for and focus on the things that are not perfect.

Redemption

Luckily entrepreneurs know that they need to be able to wear many hats. If you’re already an engineer, you just need to learn how to sell the company, what you’re doing, and the product to people when you’re hiring, raising money, or talking to customers. You need new skills to do this selling.

It’s quite possible to pull it off.

I went to a talk by Bob Metcalfe at MIT in aug-05 where he talked about selling. He recalled his personal journey of learning how to sell. Remember, Bob came into the entrepreneurship world from MIT and Xerox PARC.

Bob said he went through four stages of learning how to sell:

1. Build a better mouse trap and the world will beat a path to your door
2. Once he realized that doesn’t happen, he’d argue with the customer. “You really need this product.” He would win the argument, but that left the customer with a bad taste in their mouth. He told the customer they were wrong.
3. So that didn’t work. He switched to Suffering fools gladly. Tell them what they want to hear. Over promise and under deliver.
4. Finally, nirvana. Listen to the customers, understand their problems, and make sure you can create value for them. Under promise and over deliver.

I’d say I’m at about stage 2.5. When I’m talking to a recruit, my basic pitch is “Your life would be better if you joined us. We kick ass and you’d find much more fun/responsibility/learning here.”

I think this can improve. I’m working on it.

The first screen on any web analytics package is visitors over time. That’s a horrible graph! Traffic over time is really produced by two forces, new visitors and stickiness. These two forces are what really matter.

Stickiness

The left and right graphs below are the familiar visitors over time. The middle graph is stickiness; it says how long the average user continues to visit. For example, the blue line expresses that five days after their first visit, only 10% of users are still coming back. The magenta line is better; five days after their first visit, 70% of users are still visiting.

Three Web Sites

Imagine three web sites – blue, magenta, and black.

Blue is a photo social networking site that requires visitors to sign up before seeing anything. The average new visitor sticks around for 0.13 days, so their Total Visitors closely resembles New Visitors. A couple days into launch they meet with investors and yell “Look! We’re getting loads of traffic!” The traffic is all from Digg, TechCrunch, and Reddit, though; pretty soon they will be old news and their traffic will die, as seen on the Total Visitors graph.

The best stickiness you can get is a constant 1.0, right? Then you would retain all of your traffic and total visitors would be the integral of all new visitors over time. [1]

No, you can do better! Black is facebook. Not only does the average new visitor stick around, but she brings friends and your traffic explodes!

The median web site is actually worse than all three shown here. The median TechCrunch’ed web site will have 5% of new visitors show up the next day. It’s a sad state of affairs.

The Secret

The secret is that New Visitors aren’t that hard to come by. If you’re a startup building a web site, you can get coverage on blogs. If you have the right luster you can get on Scoble, The Today Show, and ABC Nightline. If you’re in a boring market then you can use adwords. If you’re competing with Girl Scout cookies then you can hang a sign outside the Safeway.

It’s easy to get excited by huge traffic spikes when you first launch. You haven’t accomplished anything, though; tomorrow the spotlight will be on another startup. You need both new visitors and stickiness to build a successful business. Stickiness is harder to get, but nobody measures it!

What This Means

All subscription businesses run on one metric – Average Revenue Per User (ARPU). [2] When a decision is to be made, the first question to ask is How will this affect our ARPU?

If you’re building a web site, stickiness should be your key statistic. You should write code to measure it before launching. After you have launched, put it on an LED display outside your elevators.

Announce it at all of your meetings. When making a decision, ask How will this affect our stickiness? And actually measure and follow up! You make what you measure.

Stickiness Built In

Some products have stickiness built in. Social networks encourage you to bring in your friends. Desktop applications have a persistent presence after being installed, so the user doesn’t have to remember a web site name. PayPal has some of your money in your PayPal account, so of course you’ll come back. Interesting writers like Joel on Software or Paul Graham promise to give you new thoughts occasionally, so you’re compelled to always come back. And so on.

So, how can you make yourself stickier?

Notes

[1] The math is simple; total visitors is just the convolution of new visitors and stickiness. For those who had the delight (or misfortune) of a Signals and Systems class in college, this is just a system where new visitors is the input and stickiness is the impulse response.

[2] Thanks to Joe Kraus for the example.

I’m now sure that most advice is wasted.

I’ve ran into several problems recently, after which I thought Gosh, so-and-so’s advice was right! The problem is that I never remember so-and-so’s advice when I’m actually in the situation. There’s a bug somewhere between hearing the advice and recalling it in the situation.

I’ve been trying to fix this bug. I’m going to try to imagine two things whenever I hear advice: the experience that generated it, and myself in a situation that calls for the advice.

I think this exercise will help me generate better questions for the advice giver, too.

(This post is partially derived from some things I said at Startup School 2007.)

Startup founders really need advice, especially first timers. It’s like you’re put in a big dark room with booby traps, and your only hope of survival is to draw upon the wisdom of those who came before you.

The good news is that you’ll be bombarded with advice. It comes from everywhere – investors, advisors, blogs, books, users, and rap artists. [1] Most startup advice, though, is not relevant to your current situation. You might be thinking about finding a cofounder, but the blog you’re reading is discussing corporate bylaws.

What you’ve got to do is build up a hash table in your head to store the out-of-context advice. The hash table is a map from situations to what you should do in each situation.

The person giving advice has a hash table in their head, mostly built by learning the hard way. The point of advice is to copy their hash table over to your hash table. [2] This copy is very hard to do. How can you internalize all of that knowledge?

I can give you two hacks.

First, use repetition. Download the audio versions of Paul Graham’s essays, burn them on a CD, and listen to them over and over again. Read books once, identify the ones you like, and then read those again and again, every six months.

The second hack is to get the stories. Punch lines are harder to remember than plots like “We faced this problem, chose this action, and it blew up.”

My two favorite startup books are 100% stories. Jessica Livingston’s Founders at Work is filled with interviews of successful founders recalling the Good Ole Days, in full color.

My other favorite startup book is High Stakes No Prisoners by Charles Ferguson. [3] It’s the story of Vermeer, the company that made Frontpage and sold to Microsoft for $140M. The book is only moderately insightful; it is a real win because Charles tells the story in gross detail. He names names, and trash talks everyone for their ugly actions, including himself. It’s remarkably raw.

No Storage

Of course, the real win is if you don’t have to do the hash table copy from books and other data sources. Instead, when you face a challenging situation, just ask your advisors. Essentially, do a lookup in their hash table instead of making a copy.

Having access to these kinds of resources is incredibly valuable. It’s yet another reason that you should be in Silicon Valley to start a startup: experienced advisors are at hand. [4]

Notes

[1] For example, Master P relates some wisdom: “Nigga told me, ‘C, leave that dope, cause rappin is yo thang.’”

[2] This blog post is an example of such a transfer. Most of my thoughts are unoriginal, though, so you’re getting a second hand copy.

[3] Used copies are available on Amazon for seventy nine pennies.

[4] Having great investors also helps. And, okay, maybe Boston qualifies too. : )

I was listening to my MP3 collection, and heard one of the theme songs from Aladdin. Its lyrics at the end of the song personify the life of an entrepreneur:


One jump, ahead of the hoofbeats,
One hop, ahead of the hump,
One trick, ahead of disaster,
They’re quick, but I’m much faster!

Here goes,
Better throw my hand in,
Wish me happy landing,
All I’ve gotta do is juuuuuuuump!

Here’s the audio:

One Jump Ahead

About two years ago, I was in the shower. [1] I had an idea: genetic algorithms modify data so as to optimize for something you care about. Lisp encourages programmers to think of programs as data. Why not use genetic algorithms to find the optimal program to achieve something? For example, find the program that is the best at playing checkers! [2]

I enthusiastically proposed the idea to my machine learning professor at our next meeting. She said Ah, that’s a great idea. It’s called evolutionary programming. People have written PhD theses on it. They haven’t gotten much traction because the search space is way too large, unless you’re working on a small problem with a specialized programming language.

Well, then. There goes that idea!

I felt proud to have thought of the idea, but moreso I felt disappointed that the idea had already been explored and found fruitless.

Whenever I have a new idea, I find myself hoping that I’m the first to think of it. You’ve got to check with alumni, though, to make sure you’re not spending cycles rediscovering something that’s already known.

We’ve met several times with Eric Hahn to discuss what we’re doing. Eric is the universally acknowledged email expert in Silicon Valley. He did cc:Mail, Lotus, Netscape, Lookout Search, and probably a few others. He has been a great source of wisdom when it comes to our product.

The other lesson from this experience? Take plenty o’ showers.

Notes

[1] Okay, wise guy…

[2] Paul Graham has spoken to this exact method of coming up with ideas. It’s actually kind of eerie. He talks about making familiar mental gestures against a new substrate in the shower. In my case, I was in the shower, thinking about programs as data, and the genetic algorithm gesture got invoked.

I encourage most startups or would-be startups to apply to Y Combinator, our first investors. Most people are really excited about the possibility of getting YC funding.

Of the people who are lukewarm, the number one objection I hear is that the valuations are too low. The number two objection people give is that “We’re too late stage for them.”

Baloney.

When someone says that the valuations are low, they’re comparing YC to a traditional investment. The rule of thumb usually doesn’t apply because the stage is super early (more on this later) and YC isn’t investing very much. But even from a higher level, valuations are a tactical matter; you should be thinking about grand strategy when making this decision. If Y Combinator funding increases your expected outcome by 2x, you should be willing to give up 50%, right?

I have a lot of empirical evidence about the entrepreneur’s upside so far. On average, Y Combinator will increase your expected outcome by at least 5x. They usually take 2-10% of your stock in exchange. That’s a great deal! (I think it’s really 20x or higher, but I’m being on the safe side.)

How do they increase your outcome so significantly? It’s not about the money at all; it’s about the advice and connections. These things seem fluffy before you’ve experienced their significance. I applied to Y Combinator mostly to be around Paul, not to get introductions to important people. I didn’t have any use for important people. Boy, have things changed. I can also say very empirically that the network really matters. More details and proof on that in a few months.

YC doesn’t just casually introduce you to investors, reporters, and potential employees. They really work their tails off to make it happen. Your job is to only give equity to people who are going to contribute to the company. You don’t want to give shares to investors who are just going to give you money and not help you out. You want to give shares to investors who will give you their cell phone number, who will write emails to recruits encouraging them to join, and so on.

I mentioned that YC also gives you great advice. There was a YC dinner last summer when we went to Paul and said Paul, we’re ready to launch. He said, very simply, No you aren’t. I went home demoralized. As Steve Jobs said, It was awful tasting medicine, but I guess the patient needed it. We weren’t ready to launch.

I could go on and on about this, but I’ve got to get to work on the second objection: We’re too late stage for YC.

This objection might be true for some. YC doesn’t invest in $20M series B rounds. Usually, though, I hear this retort from people who have been working on the product without funding for one or two years. They’ve built a lot of technology, and are afraid that their valuations won’t reflect that.

I can see why people might think this way. For years YC’s web site said that they take 5-7% of the company. Only recently did it change; now it says 2-10%. (You can use the Internet archive to see old versions of their site.)

What’s going on here? YC is seeing more teams that have already made something, and they’re accommodating those situations. Their #1 goal is to work with great people and companies, not to own the largest possible percentage. So, no, you probably aren’t too late stage for them.

The founders of Weebly were working on their site for a year before taking YC money. They were on TechCrunch the day of their YC interview. They still took the money, and I am sure they haven’t regretted it.

Sam Altman from loopt was the speaker at the Y Combinator dinner last night. (Sam is a close friend and one of the best entrepreneurs I know.) During Q & A, Paul Graham asked: “What do you wish you’d known when you were at these guys’ early stage?”

I asked myself the same question. What do I wish I could tell my past self?

I think we’ve done a great job so far. That being said, if I had known these things when we began, Xobni might be worth twice its current value.

Equity

First, offer significant equity to rock stars you find early in the company. When we met Gabor Cselle in August, we were in love. We asked our friends how much we equity we should offer, and the feedback was all over the board. We took the wrong advice and offered him too little.

We recently corrected that mistake, and Gabor is now joining the company about seven months later. I’m really happy about this, but we left a lot of value on the table by not getting him earlier.

Conflicts of Interest

Matt and I passed up three introductions to a firm because of their investment in another email company.

The truth is that there are going to be other companies in your space, but if they’re not competing directly then it might be foolish to turn down a meeting with their investors. As John Doerr said, “No conflict, no interest.”

We eventually realized we were wrong. We took the fourth introduction, and haven’t regretted it.

So don’t be so paranoid about conflicts of interest as long as you’re not a direct competitor.

Idea Due Diligence

We knew we wanted to leverage hidden email data. The most obvious thing to do with hidden data is to display it with graphs and tables, so we spent the first six months working on that product.

It turned out that Xobni Analytics didn’t generate enough recurring value for individuals. Whoops.

We didn’t know how to judge an idea, so we started with the most natural idea. Instead, we should have forced ourselves to answer “Why do people want this?”

This goof only slowed us down by about 30% because we used bottom up programming. Most of those six months were spent building out the baseline platform that we’ll need for any product in our space.

Saving Grace?

The common theme of these mistakes is that we eventually woke up and recovered. I’d love to say this implies that we’re brilliant at spotting our mistakes. Instead I’m compelled to ask myself: what big mistakes are we making now that we don’t know about?