“One of my favorite VC quotes comes from Bill Kaiser of Greylock. He once said, “when I hear about a company once, I often ignore it, when I hear about it twice, I pay attention, when I hear about it for the third time, I take a meeting”.

“It happened to me this week. I met with Reshma who runs seedcamp, the european version of Y Combinator, on Monday and she told me about Zemanta which came out of last year’s seedcamp. Then I saw this blog post about Zemanta on Techmeme the next day. And then on Thursday, Alex Iskold, founder of our portfolio company Adaptive Blue, introduced us to Andraz, one of the founders of Zemanta.

“Three hits in one week is absolutely a “pay attention” notice. So this morning I am trying Zemanta out. The image and most of the links in this post were automatically provided by Zemanta.”

Put yourself in Fred’s shoes. His inbox is overflowing with intros to companies that are as good as yours. He has to ignore most of the intros and focus on a few of them. One of Fred’s best filters is the quality and quantity of the people referring your company. And, by the way, Fred went on to invest in Zemanta.

How to get multiple intros

This is what we tell entrepreneurs who use AngelList and StartupList: we recommend doing all of these at the same time,

1. Email investors directly if they allow it. But first read their profile to see if you’re a good fit. Don’t contact them if you don’t fit their interests — you’re not the exception that proves the rule.
2. Use one of the referrers they suggest. But don’t spam referrers — you should either know the referrer or the referrer should be open to cold calls.
3. Use Facebook/LinkedIn and ask mutual friends for an intro. You should either know the mutual friend well or the mutual friend should be open to cold calls.
4. Send your elevator pitch to StartupList and, if you’ve got a good pitch, we’ll send it to the investors on AngelList — or, if you prefer, specific investors you suggest. 4 weeks in, we’ve already done intros between 15 startups and 25 investors — and gotten 1 startup funded. Even better, with StartupList, the investors come to you.

The idea here isn’t that you should keep bugging investors — the idea is that it often takes three tries to get a meeting.

In my first post, A brief history of your investors (and their investors), I wrote about the history of venture capital. I described how the economy and stock market drives investments into venture capital and startups. I also covered how the basic incentive structures are affected by these drivers.

I ended with a suggestion that cash is gaining power relative to other assets and a suggestion that this will shift the balance of valuation and terms in favor of the root sources of capital (limited partners and above). In this second part, I’ll discuss why I think this is happening and what it means for venture investors and entrepreneurs.

Speculative returns are a major component of total returns

The coming decade is not going to be a bull like the 1980’s or 1990’s. Why is this important? Because it’s going to turn money into a “scarce” commodity and therefore drive down valuations, erode returns, remove under-performing venture funds, and reduce company exit valuations.

The 1980’s and 90’s were incredibly bullish. The annualized return from the public stock market was 16.8% from 1982-2000. That is huge. If you dive into the 16.8%, the fundamental return was 9.9% annualized.

What’s the remainder? I’ll call it speculative return. The speculative return was 6.9% between 1982-2000. And what is speculative return? It’s the expansion of the starting and ending P/E from 1982 to 2000. We started 1982 with a P/E of 8.0 and finished 2000 at 26.4!

As I wrote in part one, the increased supply of money drove these large returns. M3 money supply started its ballistic rise in the early 1980s. The total debt market went from $4T in 1980 to about $52T at the end of 2009. So the credit boom and decreasing interest rates fire-hosed cash into all markets. And that’s how a speculative return of 6.9% a year was driven. Other drivers included the baby boomer demographic, the technology boom, geopolitical stability, and the boom in international trade from globalization.

If we look back in time, the preceding time period of 1966 to 1981 had a total return of 5.9% (including dividends of course). However, the fundamental return was 11.1% and the speculative return was -5.2%! We started 1966 with a P/E of 17.8 and finished 1981 with a P/E of 8.0. And to add a little more color, the 1950-1965 post-WWII time period had a total return of 16.1%. That was comprised of a 10.0% fundamental return and a 6.1% speculative return. And, looking forward a bit, we can see the 2001-2005 time period had a total return of -1.3% with a -6.9% speculative return.

This data suggests that the speculative return component is a huge driver on total returns. And that it has a fairly long half-cycle time. It’s in the vicinity of 16-18 years if we do the analysis since the early 1900s. In a short 5 year period, speculative return can comprise 55% of the total return. And, over a 40 year period, speculative return drops to near 0%.

Benjamin Graham said it best when he said that the stock market worked like a voting machine, but in the long term like a weighing machine. If you are building a long term company, that is the good news. The not-so-good news is that the short term pain of being part of the voting machine could be very significant.

2001-2020 will have negative speculative returns

Okay, its 2010. Could the speculative return dynamics since 2001 have ended? Umm — probably not. Take a look at this table of important drivers that compare 1981 (the start of the mega 20 year bull period) to now.

To me, these are very sobering statistics. They paint a completely different picture than at the start of the last bull cycle of 1982-2000. My judgment is that these statistics are going to seriously suppress speculative return. The -6.9% of 2001-2005 will get worse and total returns will suffer. In 2021, statistics will show that the 2001-2020 time period had good fundamental returns. But horrific speculative returns.

Valuations go down and diligence goes up for startups and VCs

What will this mean for the U.S. entrepreneurs and investors? In short, we are not going to be “partying like its 1999” for quite awhile. (Who knew that the artist formerly known as Prince could forecast market peaks?)

The implications of negative speculative returns will be huge. The number of venture firms and their personnel will shrink. And probably hit bottom sometime this decade. Venture firms will be under significant pressure to outperform their peers and outperform their limited partners’ common benchmark indices like NASDAQ. Limited partners will feel the same type of pressure as they too source their capital from sources that will be under tremendous economic pressures. Angel firms (translation: angels who are institutionally backed) will feel the same pressure. Angel investors (individuals investing their own capital) will become more risk averse.

How will these pressures affect entrepreneurs? As a whole, valuations will stay suppressed and will probably come down further over the future years. Revenue multiples and “discount to public market multiples” will re-enter and dominate the late stage financing lexicon. Early stage companies will also feel this suppression with smaller venture rounds. Capital-intense startups that need to raise large initial Series A financing rounds will be particularly affected.

The amount of time spent in due diligence will go up and get more rigorous and detailed. Of course, there will always be companies that are exceptions. But as a rule, the suppressed return environment will force all parts of the money chain to spend way more time in diligence. Way more time and energy for limited partners to raise capital. And the same for venture investors. And the same for angel firms.

Startups will feel this diligence pressure next as they are the next stop on the money supply chain. My guess is that new service providers will emerge to help both investors and entrepreneurs with these diligence processes. How they will be paid is an open question.

Since individual angels use their own cash, they won’t be directly affected. But they will probably diversify their portfolio by making smaller investments on average. And put less of their total portfolio in startups so that they can have greater portfolio liquidity. In aggregate, they will put less money into startups.

Some startups and VCs are going to disappear

Okay, so valuations down and diligence up for every part of the money supply chain. We can all work through that.

Where matters are going to get tricky is that parts of the money supply chain will disappear. A venture investor or angel firm may run out of cash in a fund and need to raise a new fund. A startup company has a similar problem.

If you are a startup company, a pure non-dilutable asset is your time. Raising a new financing round requires time. Since we’ve already established that investor due diligence time will increase, the last thing an entrepreneur will want to do is spend that time talking with investors who don’t have cash to invest. Or who can only invest with particularly harsh terms because of their own liquidity needs.

In the next and final part of this series, I will detail the questions you should ask your potential investors. These questions will assist you in ensuring you are talking to the right investors for your company.

In closing, here’s the “New York Daily Investment News” front page from the early part of the Great Depression to remind us that history may not repeat exactly. But it does rhyme.

For intros to advisors and co-founders, I always tell people to sign up for [Startup Digest] — a weekly curated list of the best events in 27 cities — and start going to lots of events.

Good things happen to you at events

I don’t go to a lot of events anymore because I “wouldn’t be here working.” But I went to a lot of events when I moved to Silicon Valley 5 years ago. And great things happened to me at these events. I met Mike Arrington and ended up crashing at his place for a few months when I had no place to stay and very little money. I was re-introduced to David Cowan and ended up working with him as an EIR at Bessemer.

Going to an event can create its own luck. From the archive of the blogger formerly known as pmarca (a.k.a. Marc Andreessen):

“In Chance II, something else has been added — motion.

“Years ago, when I was rushing around in the laboratory [conducting medical research], someone admonished me by asking, “Why all the busyness? One must distinguish between motion and progress”.

“Yes, at some point this distinction must be made. But it cannot always be made first. And it is not always made consciously. True, waste motion should be avoided. But, if the researcher did not move until he was certain of progress he would accomplish very little…

“A certain [basic] level of action “stirs up the pot”, brings in random ideas that will collide and stick together in fresh combinations, lets chance operate.

Events are the place to meet people who won’t meet with you

People who aren’t available over email or one-on-one go to events to make themselves available. Mark Suster writes,

“One area where I have made in-roads is in the “I’d like to buy you a coffee for 15 minutes and get some career advice” emails from people I don’t know. I really do like to help people so in the early days I took some of these. I simply can’t fit in the time any more. So I often advise these people to find me at a conference and I promise to spend time with them there. I’ve already allocated that time as “general networking time.” I’ve developed a system for the polite “no” in this context.”

Sure, we would all like to get a 30-minute phone call with Mark, but I think you form a deeper psychological bond if you can talk to him for 10 minutes in person.

So if you’re looking for intros to advisors and co-founders, sign up for [Startup Digest], start going to events, and create some luck.

The first startup has been funded through StartupList. The investor is Matt Mullenweg and it’s a Y Combinator company. We’re not releasing the name of the startup right now but let’s call it Startup #0. This is a big milestone and we want to thank Matt, the AngelList investors, and the startups who’ve applied to StartupList for making it happen.

Power Brokers: Our new referral program

We also want to thank you for referring startups to StartupList and AngelList. And we want to single out Rob May (@robmay), founder of Backupify, for referring Startup #0. So we’ve created a referral program to thank you for your referrals. It starts with a list Power Brokers: people who’ve referred high-quality startups to StartupList — check it out.

Here’s what we do for the power brokers. If we select a startup you’ve referred for StartupList, we highlight your name to all the investors on the list (for example). This is an awesome way to build a relationship with the investors on AngelList (and us). Second, we’ll highlight your name in announcements about the startup (see the fine photo of Rob above). Third, we’re brainstorming other ‘thank-you’s’ for the power brokers. An invite-only conference with the angels on AngelList? A demotivational poster? Please share your ideas in the comments or email me.

If you refer a startup to StartupList, please tell them to fill in your name in the field for referrers in the application.

New investors on AngelList

We’re working through the investors who’ve applied to AngelList. There’s 54 investors on the list so far and about 25 of them have asked for intros to StartupList startups — here’s a few examples:

Ann Miura-Ko from Maples Investments

Jon Callaghan (Investor in Meebo)

Michael Dearing (Angel in Aarvark)

These are just a few of the new angels who’ve asked for intros… go browse all the investors and tweet them a hello.

And you don’t need StartupList to get in touch with the angels — you can contact many of them directly or, better, through the referrals they list. But you should still apply to StartupList because it often takes three tries to get a meeting.

Twitter Widgets

I’ve collected Twitter testimonials from AngelList members in our Twitter favorites:

And this Twitter list of AngelList members is always fun:

Startups: apply to StartupList here. Angels: join AngelList here. Everyone: thank you for being part of this.

Thanks to KISSmetrics for supporting our interview with Sean Ellis. If you want an intro to KISSmetrics, send me an email. I’ll put you in touch if there’s a fit. Thanks. – Nivi

Hiten Shah from KISSmetrics recently sat down with me to discuss how to optimize funnels with their upcoming analytics tool, KISSmetrics.

You may know Hiten from his Crazy Egg days and survey.io, which Hiten and I discussed in How to measure product/market fit with survey.io.

SlideShare: How to optimize your web apps with KISSmetrics
Audio: Interview with chapters (for iPod, iPhone, iTunes)
Audio: Interview without chapters (MP3, play anywhere)
Transcript: See below

Prerequisites

You’ll get more out of this interview if you also read:

1. Our interview with Sean Ellis parts 1 and 2.
2. Our previous interview with Hiten: How to measure product/market fit with survey.io.

Outline

Here’s an outline and transcript of the interview. The interview and transcript are about 23 minutes long and we’ve highlighted some of the juicier bits for you.

1. After fit, prepare for growth
2. KISSmetrics helps you optimize your funnel
3. KISSmetrics helps with all optimization steps
4. First user experience isn’t necessarily the paid user experience
5. KISSmetrics is most valuable after fit
6. How Cloudfire uses KISSmetrics during fit
7. Startups aren’t a science — but we’re getting closer
8. How other people use KISSmetrics
9. KISSmetrics is on it’s third iteration
10. Why KISSmetrics’ hasn’t launched
11. KISSmetrics tracks actions on a per-user basis
12. KISSmetrics lets you calculate customer LTV
13. Get more startup advice

Transcript

Music: Squarepusher

Trent Wolodko: Hey there everybody, my name’s Trent and I’m new to Venture Hacks. I’m going to be helping with producing content for the site so that the guys can concentrate on bringing you good advice faster and more furiously than ever. Today Nivi speaks once again with Hiten Shah from KISSmetrics.com. He spoke with Hiten recently about survey.io and today  he’ll be talking to him about optimizing your web apps using the tools over at KISSmetrics.  Right off the bat, these guys make a lot of references to Nivi’s interview recently with Sean Ellis about bringing your product to market as well as his former interview with Hiten regarding survey.io. So, you may  want to check out those interviews as well on the site. The guys have a lot of ground to cover as you can see, so without further adieu, I’ll throw it over to them…

Babak Nivi: So the second part of the interview with Sean is what activities you do to prepare for growth. Right?

Hiten Shah: Yeah.

After fit, prepare for growth

Nivi: One of the insights that I got from the interview was that you do a whole bunch of stuff to get to fit. That’s step one. Then step two is that you do a whole bunch of stuff to get ready to prepare for growth.

Hiten: Yeah.

Nivi: And Survey.io is great for the product/market fit stage, where you’re trying to get the fit. And you guys also have a product called KISSmetrics, which helps you prepare for growth, or to put it another way, to optimize.

Hiten: Yup, and that’s our main product.

Nivi: So, what does it do?

KISSmetrics helps you optimize your funnel

Hiten: First, it’s an analytics product, pure and simple, but the difference is that it’s meant to do one thing really well, and the thing it does really well is help you optimize a funnel – help you understand what your funnel looks like for whatever type of product you have.

Nivi: So, Sean has a few steps in the optimization stage, right?

Hiten: Absolutely.

KISSmetrics helps with all optimization steps

Nivi: Economics: put into place a business model and optimize it. For example, change your price or change the way that you charge, completely, whether it’s by the year or by the pound or however. So that’s the economic step. That requires optimization.

There is the positioning step – how you describe the product – which also requires optimization.

And then there is the step of essentially moving people through the funnel and getting them to the finish line. That requires optimization.

Hiten: Yup!

Nivi: How do I use KISSmetrics in any of those?

Hiten: You can use them in all of them, because when you have economics in place it means that you’re charging customers and that you need to understand things like how different price points affect your revenue. In essence, it’s a funnel. You’re still funneling people through a sign-up process, and you might be A/B testing two different things.

So we facilitate some level of A/B testing today, because you can pass in the data and visualize it, but in the future we’re going to start adding more components around A/B testing and optimization. Right now it’s a hard focus on helping people understand their funnel, but then that goes into both of those areas we were talking about.

So, in the economic stage it’s just about when you’re testing different pricing plans and things like that, and understanding if there are any differences, also, in different channels of customers depending on where they came from, and if your revenue is different for those based on the price points.

Then on the promise side you’re probably doing a lot of things around testing landing pages and things like that, so you’d build different funnels for different landing pages and different cohorts of users, and we completely facilitate that, as well.

Nivi: So you let me – and maybe you can just send me a couple screen shots that I can put in – compare two cohorts, how many people get to the finish line based on their channel, based on the positioning that they saw on the landing page, based on the final price or business model that they saw that we offered them, and also based on what Sean calls that “first experience.”

Hiten: Yeah, first user experience.

First user experience isn’t necessarily the paid user experience

Nivi: The first user experience, which is just kind of the funneling. I mean, it’s everything.

Hiten: Yeah, but it’s primarily how to optimize your on-boarding process of first-time customers so you can make that more efficient – efficient meaning highest conversion rates – and so that you can spend on marketing and you understand what the value is of each user.

Nivi: What does that mean to you, other than a conglomeration of the landing page and all the steps between that and putting the credit card in or signing up?

Hiten: In every product there are a number of steps people take to get gratified with the product. Gratification, usually in freemium products, happens during the free period, most likely, that instant gratification, and then they have another reason to go pay. So it’s all about understanding how people are moving through those stages of their usage of your product. So the idea is to track all the different events of gratification and optimize against that. And there are always steps in between before they’re gratified.

For the first gratification, they have to sign up. They probably have to fill out a form. They probably have to install something, or depending on what type of product it is there are a number of things they need to do before they feel satisfied that this is a must-have product. And once they hit that point, then you’re going to have an ask of asking them to upgrade, and making sure they hit a point where…. It’s really about you trying to understand what point they hit before they’re willing to give you money, and understanding the different things you can do around that to encourage them to give you money.

This all has to do with, if you have free plans, you’ve optimized your first user experience. Now you really need to understand what the prompts are and where you put them to get them to pay you money. So it’s all about prompt optimization.

An example is if you have an analytics product and you have a free plan, but you want to charge users, you probably need to put prompts in different places, as customers do different things with the product, to get them to pay, basically.

Nivi: In order for KISSmetrics to be useful to me, should I be done with the product/market fit stage. In other words, should I have 40% must-have users, or can I use KISSmetrics earlier, effectively?

KISSmetrics is most valuable after fit

Hiten: The short answer is that I think KISSmetrics is most valuable after product/market fit. But, this is one of those things where creating a startup and the steps to creating a startup aren’t defined yet. People look at them in all different ways.

I think both Sean Ellis and Eric Ries are making a lot of noise and movement, and there are a few other people like Dave McClure that are doing all of this, to get people to think of it more as a methodology, and there are different stages to a company and different things you should do at each stage. So if you follow one of those methodologies or one of those mindsets or theories, what you really want to do is wait until you have product/market fit before you optimize first user experience.

My own example is that we’re not optimizing our first user experience for KISSmetrics right now. We’re getting users in, but we’re not optimizing it because, really, that’s not the important part right now. The important part for us is getting enough users and building and iterating on the product to make it so that it’s something they can’t live without. And that’s not a quantitative thing; that’s not something where if I can measure how they go through a funnel, it’s going to necessarily help them. It’s more about things I need to add in the product, and it’s a little more of a fuzzy goal. Honestly, hitting product/market fit, in my opinion, is a pretty fuzzy goal.

Nivi: Tell me how Ash, who has written about this…What’s the name of his company? CloudFire? Is that right?

Hiten: Yeah.

Nivi: How is he using KISSmetrics actually in the product/market fit stage? Is that right?

How Cloudfire uses KISSmetrics during fit

Hiten: Yeah. What he’s actually doing is combining a few ideas. Dave McClure has something called AARRR!! that’s a framework that he came up with years ago. He’s also one of our advisors, for a good reason. And he spread a lot of the religion of metrics, so a lot of people say he’s the church of metrics.

But basically, what it does is break down your business metrics for a startup into 5 different things.
Acquisition, which is metrics around how you acquire customers, what channels are profitable or what channels are bringing in customers and how you’re bringing them in. So it’s more like market.

Nivi: So you define acquisition.

Hiten: Yeah.

Nivi: One definition might be: they signed up.

Hiten: Yes, exactly. They signed up, and where they signed up from.

What Ash has done is taken acquisition and made a funnel for it, and he calls it the acquisition funnel in KISSmetrics.

Then the next one is Activation, which is how to get them to have their first gratifying experience. They’ve activated with the product. They’ve used it. They’ve experienced it.

Nivi: And in his case it’s something like, they downloaded my software and uploaded some photos with it. Something like that.

Hiten: Exactly! Exactly. It’s like they activate it, which means they downloaded it and uploaded a photo. They had the first experience of using it.

And then the next one is Retention, which is that they came back or they used it multiple times or some factor of that. It depends on what your startup is. So for him it may be that they used it once, but then they used it a second time and that would be retention.

Nivi: In thirty days, say, they use it again.

Hiten: Yeah. So he’s got another funnel around that.

And then the next one is Referral. That one is more like, are they referring other users? And that’s a whole thing in itself; it’s ‘forward to a friend’ and ‘contact importer’ kind of stuff. There are all kinds of things around referral.

Nivi: Tweet about it.

Hiten: Yeah. That also gets into virality and all of that kind of stuff.

And then the last one is Revenue. So he’s got some revenue funnels around all of the prompts. I don’t know if this is exactly what he’s doing, but this is what I would imagine he would be doing. I don’t look into his account; I just talk to him and read his blog posts.

Nivi: That’s good.

Hiten: He’s probably got a funnel that’s focused around the prompts that he’s using to get people to pay. So he’s actually using it [as an] analytics tool, in general. People end up using it whatever way they want, and that’s one of our challenges, which is that we really need to figure out what type of use-cases we want to support.

Nivi: Question number 6 or 7.

[laughter]

Hiten: Yeah. You got it. So we’re not even there yet of giving out that survey, but we will be soon enough, hopefully.

Nivi: I think it will be interesting to see how his activation and retention numbers tie into the must-have segment.

Hiten: Agreed. Yeah.

Nivi: Probably mostly on the retention, if I’m thinking about it right now correctly, because for the person to even be a user that you would survey with Survey.io, you’d probably want them to already be activated.

Hiten: You might even want them to be a retained user, and hit the retention step.

Nivi: Yeah, you’d want them to be retained. Right.

Startups aren’t a science — but we’re getting closer

Hiten: So I think he’s actually using it in a very interesting way that we’re probably going to study a little bit deeper, because Dave’s an advisor and we buy into his theories. Actually, our first iterations of this product, which not many people saw, each of those was a view in our system, so we’re actually very intrigued by what he’s doing with our product. But again, I don’t think startups are a science yet, and I think we’re getting closer and closer to it, so it just really depends on what people subscribe to. All we can provide is a tool at the moment, and then hone in on these different use cases of it.

Nivi: Right. One thing that Sean actually mentioned in the interview is that he doesn’t see a lot of correlation between the recommendation figure and “success of the startup.”

Hiten: Right.

Nivi: Whereas he sees a fair amount of correlation between the success and the must-have figure. So that’s one thing to keep in mind for Ash, perhaps.

Hiten: Sure.

Nivi: And then, too, just in your case, like you were saying, with KISSmetrics, the way you’re bringing it to market is very feature oriented right now, and then you’ll figure out the positioning based on qualitative conversations with customers.

Hiten: You’ve got it. Right now it’s more about product and how to tweak the product. I hate to call it features because for us, actually in the market we’re in, every analytics product has hundreds of features. We actually want to provide the features that matter, so that’s kind of the difference.

It’s basically iterating on the product and making it so people like Ash love it. There are probably a number of things… He might already love it. He might be a very unique case where he’s been looking for something like this. But a lot of other people, I get on calls with these people or talk to them, and….

Nivi: So how do other people use it?

How other people use KISSmetrics

Hiten: How they’re using it is that there are several Facebook applications that are using it. They’re using it to optimize their own funnels and just optimize the conversion rates between each step, and things like that – normally what you would do with funnel optimization.

And then other folks, like SaaS businesses that are using it, are buying traffic from AdWords and they’re passing in the dollar value for their customers at one of the steps, and they’re just basically optimizing the funnel, but based on revenue because that’s what their main goal is and they’ve already got their revenue set up.

Nivi: So, visualize the funnel and visualize cohorts, and compare them.

Hiten: Yeah. And some of this stuff is still things we need to do better in our own product. We’re very early. We released this version on November 1st, so we’re not even a month-and-a-half into it.

Nivi: You guys have basically realigned the company around this product, is that right?

Hiten: We’ve realigned the company around this product through doing our own customer development this whole time – “this whole time” meaning this is our third iteration of the product and this is the one that we’re being a lot more public about.

Nivi: What were the first two?

Hiten: With our first one we had assumptions around doing metrics for Facebook applications and social network applications. And what we realized there was that we had a very constrained view, and it was based on Dave McClure’s AARRR!! stuff that we just talked about. What we realized about that was that people started asking all kinds of questions that our interface could not answer for them.

So we took what we learned there and we built a second product, which was very super-flexible event tracking, and we had no funnel analytics or anything like that, so this kind of stuff would help you with tracking engagement of users. If you were a game it would help you track game plays by gender and all of those kinds of things. And if you were a SaaS app, it was more about if Ash needed to measure what types of photos people were uploading or what kinds of things they were storing on his system, we would provide metrics around that kind of stuff.

Nivi: And what was that called?

Hiten: It was KISSmetrics. All of these are KISSmetrics, we just didn’t publicly release them.

Nivi: OK, got it!

Hiten: And so we used these other two products to kind of learn about the market and figure out what our target should be. And it was super-generic products, especially the second one. The first one was very targeted towards a market, then we kind of ran away from that and said, let’s get Facebook apps, but let’s also get SaaS applications; let’s get blogs using our product. And that was a very highly flexible product.

In some ways it would have been really fun to market that product and deal with it, but we were just heading down a path, in terms of sales and marketing and things like that, that we didn’t want to. As a business our DNA was more around building a really simple solution.

We have a previous analytics tool that the people in our company built called Crazy Egg, and it’s a simple tool, and we wanted to go back to that thinking and say, analytics is something where people log into analytics systems, and a lot of the time if they’re not expert users they’re very confused about what they’re looking at, and then they end up just using it as a trending tool just to see general trends.

And the kinds of features that we felt were most important were conversion goals, conversion rates and funnels. So with all this learning from our previous products we basically decided that we should hone in on that proposition, and instead of tacking funnels onto an analytics product, like the other one, we would just make that the product. And it was a big bet. I’m actually super excited about it and very happy that we went in this direction because I think we can really make an impact on the market and produce something really useful for customers.

Nivi: The feature is the product, here.

Hiten: The feature is the product, and that’s a trend. I mean, Twitter is just a feature, right?

Nivi: Right.

Why KISSmetrics’ hasn’t launched

Hiten: A lot of people have said, “Oh, you guys are creating a lot of buzz. We know your brand, but what the hell is your product, and when are you going to launch?” And for us it’s not about that. It’s not about launch; it’s not about things like that. It’s about building a product that people love and iterating until you get that.

Nivi: Yeah, and this is a good example of why you probably don’t want to launch until the business is working.

Hiten: Yeah, exactly. We don’t even have our economics down, so we’re not charging anyone right now, and everyone can come in and use the product. At some point we want them to help us figure out what the value is and what we can charge for it.

The way most analytics packages charge, people are penalized for being successful, and we want to see if we can change that a little bit. I don’t know what it means yet. I don’t know what it means for our pricing model, but I’d love to make it so that if you get more traffic we don’t necessarily charge you more. It sounds really fucked up and weird, but yeah. Excuse my language, but we want a better pricing model if we can find it, that’s more aligned with our customers. So there might be more features that they pay for, and it might be access to data or something that’s not exactly like: you get more paid views or you get more growth and we charge you more money, and it scales up like that. Even for us, that’s not necessarily the most scalable business model.

Nivi: Sean talked about using action tags with AdWords, in the interview, to track users on a per-user basis. We talked about Google Analytics, which doesn’t really track users on a per-user basis. What does KISSmetrics do differently here?

KISSmetrics tracks actions on a per-user basis

Hiten: The things we’re trying to solve are if someone comes to the site multiple times and then they sign up, let’s say on the third or fourth time, we’re allowing you to identify that user and we’re trying to attach all of those times that they came, previously, to that customer. What that means is that we’ll understand the first touch point they had with you and where it came from, and we’ll be able to attach that for the history of that user using your product. And that’s a much different view than most analytics have, which is either page-view tracking or event tracking. This is actually, honestly, people tracking, which means that we’re trying to actually track people.

Eric Ries has a great quote on this which is, “Metrics are people, too,” and we firmly believe that. If we can track things on an individual-user basis without getting into a lot of privacy concerns, because you’re already doing this in your data base, so you just have to have the proper terms-of-use for it, we can provide a much more valuable analysis on your user base. And as long as we’re not doing anything in aggregate there’s not any real privacy implication, while with Google Analytics I feel like it’s very hard for them to do that because they have a huge business that has nothing to do with analytics, in a lot of ways.

Nivi: Right. Does tracking people on a per-user basis let you calculate LTV, and to put it another way, cost of customer acquisition, and does Google Analytics not let you do that?

KISSmetrics lets you calculate customer LTV

Hiten: I’m not going to say that Google Analytics does not let you do that. We definitely think there’s a lot of value in the product, and we actually use Google Analytics in a lot of our products, and they do different things than we do. But legally, you cannot pass personally identifiable information into Google Analytics. So, if you’ve got 100 conversions in Google Analytics, you might have 110 in your data base simply because you’re not able to identify each individual user and they might lose some of the data.

And they’ve got, my guess is, 100 million sites using the product. That’s a high load. We’re probably never going to have 100 million sites using our product. There are a lot of issues that come around dealing with that many customers with a free product.

Nivi: Yeah. What I’m just trying to understand is what the requirements are to calculate cost of customer acquisition. Have you guys thought about that at all?

Hiten:  Yeah, absolutely. We want to make it as simple as you passing a dollar value every time an event occurs where someone’s paying. And then in our interface, it’s not like magic, but it’s magic. We can show you revenue by referrer, revenue by channel, revenue by plan type, and all of those kinds of things that typically you would have to do a lot of data base queries in your own data base, your user data base, to get all this data out.

And honestly, this is some of the pains we had with our previous products. So, for the first 6 months, we never tagged referrer for each customer that came in, so we couldn’t boil it down to what referrers were creating the most revenue for us. Once we started doing that it was enlightening. So KISSmetrics is actually based on a lot of the pains that we had with our previous SaaS product.

Nivi: So the dollar value that you pass to KISSmetrics will let you calculate the LTV, to some degree, of the customer, right?

Hiten: Yeah, that’s the goal.

Nivi: But not on the cost side. It doesn’t tell you how much it cost you to get the guy.

Hiten: Not today.

Nivi: Right.

Hiten: That’s a really good question, and that’s something that we do want to answer in the future. Obviously it depends on what type of customers we get and how they want to pass in the data. Is it automatic? Is it manual? Things like that. But it’s a problem that we’re thinking about a lot because then you can have a full picture in one dashboard. It’s all about that one report to rule them all, which doesn’t exist in analytics today. And if it does exist, it’s an internal report that’s built by your development team, and that’s kind of the problem we’re trying to solve.

Nivi: Cool! Thanks again for spending the time talking and also sponsoring the interview with Sean and making it free for everyone.

Hiten:  Absolutely! Our pleasure.

Get more startup advice

Trent: You’ve been listening to Hiten Shah, from KISSmetrics.com on how to optimize your web apps. And if you want to find out more, you can check out the KISSmetrics.com website. You can also follow Hiten on Twitter @hnshah, or check out his personal site at hitenshah.name.

Be sure to also have a listen to Nivi’s interview with Hiten regarding Survey.io, and the two-part interview with Sean Ellis on bringing your product to market, on the VentureHacks.com website.

Of course, if you’re into all other sorts of good advice for startups, head on over to Venture Hacks and just soak it all in.

Thanks again for stopping by. See you next time!

What you charge for your product is simultaneously one of the most complicated and most important things to get right. Not only does your pricing model keep you in business, it also signals your branding and positioning. And it’s harder to iterate on pricing than other elements of your business. Once you set a price, coming down is usually easier than going up.

Should I charge for my MVP?

Most people choose to defer the “pricing question” because they don’t think they (or the product) are ready. Something I hear a lot is that a minimum viable product is by definition (embarrassingly) minimal. How can you possibly charge for it?

A minimal product is not synonymous with a half-baked or buggy product. If you’ve followed a customer development process, your MVP should address the top 3 problems customers have identified as important and it should do it well. You can ensure that by dedicating 80% of your efforts to improving existing features versus cranking out new ones.

Steve Blank bakes price exploration right into the initial customer interviews. Price, like everything else, is built on a set of hypotheses that needs to be tested early. Steve suggests you ask potential customers if they’d use the service for free. This is to gauge if the product’s value proposition is compelling at all. You then ask if they’d use the service for $X/yr. How do you come up X? You can simply roll the dice and adjust along the way, or use Neil Davidson’s excellent guide to software pricing to start with a more educated guess. Once your MVP is built, Steve asks you to sell it to your early customers. There is no clearer customer validation than a sale.

Sean Ellis, on the other hand, argues that achieving initial user gratification (product/market fit) is the first thing that matters and suggests keeping price out of the equation so as not to create unnecessary friction:

“I think that it is easier to evolve toward product/market fit without a business model in place (users are free to try everything without worrying about price). As soon as you have enough users saying they would be very disappointed without your product, then it is critical to quickly implement a business model. And it will be much easier to map the business model to user perceived value.”

Both Steve and Sean advocate removing price from the equation — but at different points. Steve removes price during the customer discovery process but suggests you charge for your MVP. Sean removes price from the MVP and suggests you charge after product/market fit. I can see the merits of both approaches and wondered which was right for my product: CloudFire: Photo and Video Sharing for Busy Parents.

Why not use freemium?

On the surface, freemium seems like the best of both worlds: Get users to try your service without worrying about price, then up-sell them into the right premium plan later. However, many people make the mistake of giving away too much under the free plan, which leads to low or no conversions. It’s human nature — we all want to be liked.

More important, we don’t yet have enough information to know how to price or segment the feature set. I made this mistake with my first product, BoxCloud: an early visionary customer called me up and said, “I really like your product and want to pay for it but your pricing doesn’t require it.” After a few more iterations of segmenting the feature set, I decided to forgo the free plan and simply offered premium plans with a trial period. Sales went up and so did the quality of feedback, which I attribute to the difference between feedback from customers versus users.

(Hiten Shah shared a similar story with me around his experience with Crazy Egg. Even 37signals has greatly deemphasized their free plans to almost being fine print on their pricing pages.)

Lincoln Murphy just published a timely white paper on “The Reality of Freemium in SaaS” which covers many important aspects to weigh when considering Freemium, such as the concept of quid pro quo where even free users have to give something back. In services with high network effects, participation is enough. But most businesses don’t have high enough network effects and wrongly chase users versus customers. What I particularly liked in this paper is Lincoln’s recongition that “Freemium is a marketing tactic, not a business model.”

I strongly feel that, especially for SaaS products, starting with free and figuring out premium later (all too common) is backwards. If you know you are going to be charging for your product, start by validating if anyone will pay first. There is no better success metric and it leads to less waste in the long run. Focusing on the premium part of freemium first lets you really learn about your unique value proposition — the stuff that will get you paid. You can then come back and intelligently offer a free plan (if you still want to) with more intelligence and the right success metrics clearly defined. Even if you think you have a one-dimensional pricing plan like I did (e.g. number of projects), you’d be better served testing it with paying users because pricing experiments take a much bigger toll than other types of experiments.

Testing price in interviews

How did I put all this to test? The biggest mind shift in following a lean startup process is going from thinking you know something to testing everything you think you know.

So I followed Steve Blank’s advice and built some pricing questions into my initial face-to-face customer interviews. Because CloudFire is a re-segmented product in an existing market, potential customers referred to competitor pricing. This had to be balanced against the perceived value of our unique value proposition – saving time with faster and easier sharing of lots of photos and videos. Through these interviews I determined that, like their sharing needs, my potential customers valued simple hassle-free pricing and $49/year for unlimited photo and video sharing was a fair price they were willing to pay. That is what I charged them once my MVP was ready.

Testing price on the web

I wanted to run the same set of pricing tests with web visitors that I did during my interviews. Short of split testing a free and paid version of the MVP, which is technically illegal (update) and unfair to paying customers, I decided to split-test 3 different products with 3 different prices:

1. $49/yr for unlimited photo and video sharing
2. $24/yr for unlimited photo sharing
3. FREE for 500 photos

All plans have a 14-day free trial with the exception of the free plan which is free forever. Here are the variations I tested:

Original: Single unlimited plan

This is the simple option I discovered during customer discovery interviews. It served as the control.

Variation 2: Multiple plans

I segmented the product into 2 offerings: unlimited photos+video and unlimited photos only. I wanted to test price sensitivity and gauge interest in video sharing. Not many people I interviewed were currently taking lots of videos but they all wanted to be taking more.

Variation 3: Freemium

This has the 2 plans from above along with a limited free plan. Yes, this is a freemium plan. I wanted to measure if a limited free plan would disproportionately drive the right type of traffic (busy parents in my case).

Variation 4: No Price During Introductory Period

I added a fourth variation to test Sean Ellis’ advice on removing price till product/market fit, but I tested this differently. I was not comfortable offering the full product for a price and for free at the same time. So rather than including this page with my A/B tests, I instead tested it with new parents I interviewed.

The Results

First Place: The original single plan — second place in conversions and best overall performer. Surprisingly, the original page was the best overall performer.

Second Place: Variation 3: Freemium – most conversions but second place overall. Not surprisingly, the freemium variation drove the most conversions but only outperformed the original by 12% and had the lowest retention. Referral stats combined with random polling/emailing revealed a majority of the users that signed up were just curious (and not parents).

Third Place: Variation 2: Multiple plans – least conversions and worst overall performer. People reacted least favorably to the two paid plans.

Non-starter: Variation 4: No price during introductory period. Parents I interviewed did not understand the introductory period without explanation and were reluctant to try the service without knowing how much the service was going to cost. Probing further, they weren’t willing to invest the time building up web galleries and inviting others only to find that the service might be priced out of their expectation.

What I learned

It does pay to align pricing with your overall positioning. Our unique value proposition is built around being “hassle-free and simple” and people seemed to expect that in the pricing model as well. A lot of our existing customers were already paying for their existing sharing service so the leap from free to paid was not a big one. While Sean suggests removing price before fit for consumer facing products, he suggests always charging for enterprise customers to gain their commitment. This is another case where pricing needs to be explicit. Using Cindy Alvarez’s model, our customers appear to be Time-Poor, Cash-Rich. Offering no-hassle free trials was sufficient to remove the commitment risk. Money back guarantees might be another way to further lower this risk.

The biggest lesson learned, though, is how accurate my initial customer interview findings were, compared to all the hypotheses that followed. Pricing is more art than science and your mileage will vary, but whenever possible get out of the building, talk to a customer, and consider testing price sooner rather than later.

What do you think? Why do you think these variations finished the way they did? What other variations would you like to see us try? How else do you think we could increase conversions? I’m looking forward to discussing your responses in the comments.

“The blockbusters are oftentimes the ‘ugly ducklings.’ Cisco is probably the best example in the VC world. Everyone passed except for Sequoia. In the book world, almost everyone passed on JK Rowling.

“It’s just hard to predict the future looking into the rear-view mirror, yet everyone wants to pile onto the brand names [VCs] and they in turn pile into the hot deals in droves pushing up valuations to unsustainable levels.

“It doesn’t help the poor entrepreneur who just wants a little capital to back his dream. He has no clue whether or not it will be another Google, yet in order to raise VC funding he has to make promises that only hucksters can make. The VCs in turn make those same promises to LPs and some LPs actually believe the nonsense. What a shame.”

Emphasis added. Read Ho’s full comment. Also see Simeon Simeonov’s How to raise money without lying to investors.

Why should you read this post? So you know what questions to ask your potential investors about their investors (their limited partners). You need to understand how your investors are compensated, how they’re motivated, and how they’ll act in critical situations — so you know if you and your investor’s commitments are well aligned.

By the end of this two-part post I’ll provide a list of questions to ask investors to help determine if they’re the right investors for you. This post focuses on the history of venture capital. The second post will focus on the present and future of venture capital and how it will affect startups.

Some say that startups should raise smart money but not dumb money. Others say all the money is green, so there’s no difference. The issue is more complex than that. And this complexity involves the capital sources that your investors use as well as the terms of their agreements with limited partners (LPs).

Angels invest their own money, VCs invest their LP’s

Venture capital firms have limited partners. These limited partners come in all shapes and sizes from all over the world. And in turn these limited partners are frequently entities that are funnels for other sources of money.

Traditionally, angel investors were individuals that would use their own personal capital to make an investment. Recently, “seed stage” venture firms have emerged that have between one to three partners. These seed stage firms are usually backed by institutional limited partners or even by venture firms. They are really small venture firms that present more like angels because there are usually one or two partners running the seed stage firm.

To understand how these angel and VCs roles came to be, we need to look back in history.

1960’s and 1970’s: LPs realize they can seek alpha in VC

Let’s go all the way back to the 1960’s and 1970’s — early days in technology angel and venture investing. There were very few venture firms at the time. Most of them were formed by former technology executives who had had enough of operating but wanted to stay involved in startups. So they took some of the cash they made during their operating careers and started investing in companies that were in their work or social domain. And soon, corporate financial services and university endowments realized that these former execs-turned-angel investors had good instincts about which companies to invest in.

Meanwhile, these corporate financial services and university endowments started to apply financial asset allocation theories to their portfolio. It’s well known in financial theory that the allocation of capital in a fund across asset classes is the single most important decision that affects the fund’s return and volatility. In addition to having investments in liquid public equities, they also had investments in oil & gas funds, real estate, commodities, and other asset classes. These endowments (the early limited partners) started looking for “alpha” so they could outperform other limited-partner managers. So they created a new asset class with the highly descriptive name of “Alternative Assets.”

The LPs saw that angel investors and early venture firms made some spectacular returns. For example, MCI and Amgen came out of early small funds like Charles River Ventures and Genentech came out of  Kleiner Perkins. So the LPs got smart and said, “Aha, let’s allocate some of our alternative investments to venture capital.” In a way, it became the asset they could invest in to differentiate their performance from their competition. And to also earn a healthy bonus for themselves for “winning.”

These financial services companies and endowments would back these angel investors in a firm structure that usually took the form of a limited partnership — which, not incidentally, is totally different than an operating company. A limited partnership has limited partners and general partners; the limited partners are the major sources of capital. Historically, the limited partners would get 80% of the fund’s returns and the general partners would get 20%. The funds were small — for example, Charles River Ventures was $5M in 1970 and Kleiner Perkins was $7M. Most of the capital came from the limited partners.

General partners would typically get a 2% annual management fee. At that time, venture firms had small headcounts and the 2% fee on the small fund was used to cover business expenses like rent, travel, administrative staff, and meals. The fees were not a way to earn a living. And through the 1970’s, venture capital marginally outperformed the horrific public market.

Early 1980’s: Weak public markets tighten VC money

In the early 1980s, the 15-year public equity disaster came to an end. The public market literally went sideways from 1966 to 1981. If you invested $1 in the public stock market in 1966, you would have $1 in 1981. Worse, if you accounted for inflation, you only had 70 cents!

Because of the weak public market, startup valuations were low and investment syndicates were a must. Founders looked for deep pockets, not just easy pockets. In short, money was tight, not a commodity.

Investors were concerned that there would be few exits and worried that, even if companies became cash flow-generating businesses, they wouldn’t necessarily become high margin, high value companies. A whole host of terms became important in the term sheet like redemption rights, dividends, and participating preferred.

1982: Low interest rates and successful technology companies lead LPs to invest even more in VC

Starting in 1982, interest rates fell from 15%+ to 5%, paving the way for a lot of available capital as well as a lot of profit generation. These profits fed back to limited partners, who, in turn, pumped the profits back into venture capital through their “alternative assets” allocation, which was now 5% of their manageable funds. So the terms of limited partner’s investments in venture firms loosened up a bit, which also permitted venture firms to loosen up the terms of their investments into startups.

At the same time, technology companies like Apple and Microsoft were printing money. It was in the air that technology companies would disrupt and create new industries. And our government was printing actual paper money and creating a lot of debt to fund the deficits spurred by high defense spending and other spending. So there was a lot of new liquid capital.

All this extra capital boosted the size of venture funds. There were serious and large limited partners that wanted to place a lot of capital into these well-performing venture funds. These limited partners were university endowments, insurance companies, charitable foundations, as well as “fund-of-funds,” which were channels that would pool the capital of small limited partners that didn’t have the capital base or reach to invest in these venture firms.

1982–2000: $120B goes into venture firms in 2000 alone

Meanwhile, the 1980s and 1990s marched on, with the public market taking off and seeing an annualized 18% return into 2000. Limited partners were also happy because they were getting good returns, and venture firms were growing and specializing by domain. By 1996, venture firms had total capital of $12B+ and the average venture fund was about $120M.

In the next four years, the public market went ballistic and became a huge bubble. This was partly a social phenomenon, but it was also because of the Fed, which provided easy money at low interest rates. Also, the Fed, fearing a market collapse caused by Y2K problems, was pumping the market with liquidity in 1999.

Valuations on the private market skyrocketed across all stages and private companies were raising capital at billion dollar valuations fairly early in their development. The time frame also accelerated: investments were made within days to weeks and diligence was measured in days.

By 2000, limited partner’s returns were turbocharged and top venture firms saw annualized returns of more than 200% — far in excess of the public market. In fact, from 1995 to 2000, a total of $180B was invested into venture capital and approximately $325B was returned to limited partners. And what did the limited partners do? They put in more money. In fact, they invested $120B into venture capital firms in 2000, a tenfold increase in five years.

Limited partners were very happy with their returns from venture firms and did whatever it took to place their capital into these firms. It was like a feeding frenzy to raise a fund. My partners and I raised a $300M fund in three weeks during that period. It was just really easy. Limited partner allowed select venture firms like CRV to have better carry (25%-30%) and terms between entrepreneurs and VCs loosened up again.

2002-2009: $205B goes into VC but only returns $220B to LPs

So far so good — until we hit the year 2000, when the public market cratered and limited partners and the broad populace scrambled to defend their portfolios. Cash liquidity decreased and limited partners got very concerned about how some venture firms were being run. They also began to worry about the ballooned size of most venture funds.

But that worry didn’t last long enough, as the Fed began pumping more money into the system to “save” the United States from a recession. The great housing bubble started to grow and the public market began a strong rise in 2003, a rise that buoyed the public market portion of the limiter partners’ funds. LPs began to feel and think more positively, resulting in more capital flowing back to venture capital with good terms intact. Limited partners also began to characterize the bubble as the result of macro forces that were then tamed by Alan Greenspan. As a result, the limited partners rationalized away their worry about venture firms and we soon found ourselves returning to $30B/year going into venture capital each year. This was more sane, but not totally sane.

As the public markets improved, limited partners (who are now both domestic and international capital sources) worried that too much of their portfolio was in the public market and started pumping more money into venture capital firms. From 2002 to 2009, a total of $205B was invested into venture capital, and approximately $220B was returned. You can do the math. That is not a good return for the whole asset class.

What happened? A big part of the problem was that the public market never really responded like it did in the 1990s. The stock market blowout left collective scars across retail and institutional public market investors. There was no crazy bubble period, which was a problem because the amount of venture capital in total was still way too high given the total return. And while venture capital returns did slightly outperform that public market over the decade ending in 2009, the public market returns and private venture capital returns diverged in terms of the median return per company.

LPs: “The fund I invest in better be investing in the next Google.”

Along the way, something interesting happened to the shape of the return curve. In the 1980s and 1990s, venture capital returns were usually Gaussian with respect to their portfolios. Yes, there was Benchmark’s investment in eBay and a couple of others that created 1000x returns. But most of the exits were shaped around $300M exits. A big exit was a 10x return.

In the 2000s, the middle of that curve got blown out, making a lot of portfolios look bipolar. Most portfolio companies returned less than the capital that was put in. And a few companies generated astronomical results: Google measured in the 1,000X to 10,000X multiple. Limited partner data showed that eight venture funded companies a year were generating the vast majority of venture firm profits, and that’s out of a universe of three to four thousand companies funded every year.

Pretty soon this was common knowledge across the limited partner community, and the top limited partners — in terms of size and longevity — were looking for these “return the fund” opportunities when venture firms talked about their investments with them. If you couldn’t speak about a potential “billion dollar opportunity,” limited partners thought you really weren’t in the game. This data started a conversation across the limited partner community, which usually included the words “the fund I invest in better be investing in the next Google.” And limited partners liked to bring that up in their conversation with venture firm’s partners, “Which one of these companies can return the whole fund?”

Additionally, a decade had passed since the 2000 blowout. The decade returns for venture capital in aggregate were weak and limited partners started to reassess how, who, and what venture firms to invest in. And then the savage bear market that started in late 2007 started to ravage the limited partners’ portfolios again. This was the start of limited partners asking tough questions and looking for proof of how returns would get generated over the next decade.

2010: The balance of power shifts to limited partners?

Limited partners face the issue of diminishing returns because they also have shareholders. Likewise, the venture firms and seed stage firms that are down the pipeline from these limited partners are also subject to these hard questions.

Today, we’re beginning to see a shift of money turning back into a commodity. But without the massive government bailouts, it turns out that money supplies like M2 are declining, indicating that the core economic environment is one of deflation. This means that cash is gathering more value than any asset, and this macro shift will shift the balance of valuation and terms in favor of the root sources of capital.

In the next post, I will review what this means for direct investors and what it means for portfolio companies. I’ll also include a list of questions to ask your potential investors to ensure that they’re the right investors for your company.

Video: Old Face Andre talks economics with Omar

Another business lesson from The Wire — about intellectual property: “It ain’t about right, it’s about money.”

Thank you to TechCrunch, Business Insider, and ReadWriteWeb for describing these lists better than we could.

Thank you to the well-known investors, successful entrepreneurs, and up-and-coming entrepreneurs discussing these lists on Twitter.

Widget: Twitter discussion about AngelList and StartupList

New angels on AngelList

75 new angels have applied to AngelList. We’re going through the applications one-by-one. I added some great investors today:

Chris DeVore from Founders Co-op
Robin Klein in London
Thomas Korte, an investor in Heroku
David Shen, an investor in bit.ly
David Rose, a New Yorker with 75 angel investments
Shervin Pishevar, founder of SGN
Pejman Nozad, an investor in Dropbox

And I didn’t even know these three entrepreneurs were angels until they applied to AngelList:

Jeremy Stoppelman, founder of Yelp
Joe Greenstein, founder of Flixster
Aaron Patzer, founder of Mint

Silicon Valley has a tradition where entrepreneurs start investing as soon as they can — and that tradition is spreading to the rest of the world.

Startups are getting intros

Since we released StartupList on Twitter a few weeks ago, we’ve introduced 7 startups to 11 investors. With their permission, here’s a list of investors who’ve gotten intros:

Jeff Clavier (Angel in Mint)
Bill Lee (Angel in Posterous)
Mike Hirshland (Polaris)
Chris Yeh (Angel in AppJet)
Naval Ravikant (Angel in Twitter)
Jeff Fagnan (Atlas)
Brian Norgard (Angel in ad.ly)
George Zachary (Charles River)
Alex Finkelstein (Spark Capital)
David Cohen (Techstars)
Matt Mullenweg (Angel in DailyBurn)

How to get on StartupList

We’ve received 50 new pitches from startups. If you’ve got a good pitch, don’t worry about getting lost in the noise. A good pitch will stand out — and we will find a way to help that startup through StartupList or something else. If you’ve got a great startup on your hands, people will insist on helping you out.

Yesterday, we published guidelines for applying to StartupList. We said we look for the same things that early stage investors look for: traction, social proof, and team. But we didn’t mention another critical piece of the puzzle: the quality of the pitch itself.

Spend time writing and re-writing an awesome elevator pitch. Our elevator pitch template is a good place to start. Then get feedback from good writers — writers who have fans. You have 100% control over the quality of your pitch and there’s no reason not to kick its ass.

Apply to StartupList. Browse AngelList. And enjoy the weekend.