Startup Dilution Explained as Simply as Possible

I had the opportunity to run Microsoft Azure’s Startup Accelerator in 2013. For 2 programs, we helped about 10 startups accelerate their businesses during a 3-month startup boot camp. Since Azure was the sponsor, there was a bit of technology work done, but the biggest gains for the startups were: 1) in their understanding of their customer and their business model; and 2) in their understanding of the mechanics of startup funding. To this day, I still find dilution over several investment rounds to be one of the most super-complex and super-intriguing concept in the startup funding space.

Why Do We Need Dilution for Startup Fundraising?

Before jumping into the mechanics, let me ask you a question: why do we need to dilute investors at all? If you were only going to raise a single round of funding, you do not need to dilute investors at all. You would say “I will give you this much of my company in exchange for that much capital.” Eventually, you either will come to terms of selling X% of your asset for $Y, or you will not, but dilution does not enter the equation.

Dilution only becomes an issue when you are going to raise capital over multiple rounds.

Why Are Multiple Round Investments Required for Startups?

For simplicity’s sake, let’s assume you have a really clear capital requirements ($100,000 needed today, $200,000 needed in 12 months, and $300,000 needed 24 months), and the only unknown is valuation. Certainly it would be logically possible to raise all of the money ($600,000) today, but no investor would want to give you capital for a super-risky startup so that you could park most of the cash in a bank for a couple of years. So that is not a realistic option.

You could also agree on valuations today, and then collect the money in 12 and 24 months as it is needed. But what if the startup flounders for 12 months: are the investors still required to invest at the initially agreed upon valuation? If you give the investors an option to renegotiate because the startup is not “killing it,” then wouldn’t any rational investor argue for a lower valuation in 12 months so that could increase their equity for the same investment? Certainly, you could imagine a bunch of performance criteria and contractual provisions that would establish the future valuation, but given the unpredictable nature of a startup this would be an impossible task to do well.

Multiple Rounds & Dilution to the Rescue

“Let’s All Negotiate Timing, Capital to Raise, and Valuation at Each Round”

Because it is so difficult to foresee what is going to happen in terms of capital requirements and valuation with startups, raising multiple rounds of capital, and diluting founders and earlier stage investors with each round of funding, has become the norm.

Turning back to the scenario ($100,000 needed today, $200,000 needed in 12 months, and $300,000 needed  in 24 months), we establish a first round where we are just trying to raise $100,000. Back of the napkin, startup investors are going to want to acquire between 20% and 40% of the company for EVERY SINGLE ROUND OF INVESTMENT. The way to think of this is not that your company is worth $500,000 because you are raising $100,000, but that your company better be worth $500,000 if you hope to raise $100,000.

First Round: Founders Diluted by 1st Round Investors

Let’s keep the math simple and say: the startup has pre-money valuation of $400,000; we are raising $100,000; and this gives us a post-money valuation (pre-money valuation + amount raised this investment round) of $500,000. Investors will end up with 20% equity (amount raised from investors this round / post-money valuation, or $100,000/$500,000 = 20%). The founders ownership in the company is diluted by 20%, so that their initial ownership (100%) is diluted to 80% (100% * 80% = 80%…the math will get more interesting next round:).

Pre-Money Valuation  $400,000
Amount Raised  $100,000
Post-Money Valuation  $500,000
Founder Ownership 80%
Round 1 Investor Ownership 20%

Second Round: Founders and 1st Round Investors Diluted by 2nd Round Investors

Fast forward 12 months, and the business needs to raise $200,000. The 2nd round investors may be the same set of investors or a new set of investors. The math is easiest if you assume they are different (and if there is an overlapping investor, she will be entitled to the sum of their first and second round of equity).

Assuming that the second round of investors want to acquire 20% of the company, we will see dilution in action. The 2nd round investors will get 20% of the equity, leaving the founders and the 1st round investors with 80% of their original equity positions.

 Round 1  Round 2
Pre-Money Valuation  $400,000  $800,000
Amount Raised  $100,000  $200,000
Post-Money Valuation  $500,000  $1,000,000
Founder Equity 80% 64%
Round 1 Investor Equity 20% 16%
Round 2 Investor Equity 20%

This may seem like a bad outcome for the 1st round investors, because they have been diluted from 20% to 16%, but they are better off. Now they own 16% of a $1,000,000 asset (or $160,000) instead of 20% of a $500,000 asset (or $100,000). The key for the early stage investors is that the asset needs to be growing faster than they are being diluted (when the dilution exceeds the increase in valuation, so that the early investors are financially worse off, this is called a down-round, or a bummer).

Third Round: Founders, 1st & 2nd Round Investors Diluted by 3rd Round Investors

Another 12 months, and we need to raise another $300,000. You know the drill, so here is the table.

 Round 1  Round 2  Round 3
Pre-Money Valuation  $400,000  $800,000 $1,200,000
Amount Raised  $100,000  $200,000 $300,000
Post-Money Valuation  $500,000  $1,000,000 $1,200,000
Founder Equity 80% 64% 51%
Round 1 Investor Equity 20% 16% 13%
Round 2 Investor Equity 20% 16%
Round 3 Investor Equity 20%

Startup is Sold (Hooray!)

Let’s assume that a year after the last round, we sell the company for a cool $3 million dollars. The funds are distributed in accordance with their final round equity positions.

Round 3 Sale
Sale Price  $3,000,000
Founder Equity 51% Founder Payout  $1,536,000
Round 1 Investor Equity 13% 1st Round Payout  $384,000
Round 2 Investor Equity 16% 2nd Round Payout  $480,000
Round 3 Investor Equity 20% 3rd Payout  $600,000

Even though the Round 1 investors are heavily diluted, they end up with a 284% ROI (after invested $100,000), while the Round 3 investors had no dilution, but a “measly” 100% ROI.  There are differences in the time value of money (1st round investors have their money tied up for 3 years), but that does not cause the difference in return. The main reason that the early investors get outsized gains is because they took significant risk. As the startup becomes more mature, the odds of failure become less (even though they are still high) and the returns typically reflect this decrease in risk.

Dilution Mechanics: That Was Not So Bad

The mechanics of dilution are not too bad when you break them down as a simple math problem. Where it gets complicated is when you throw in reality: what are your actual capital requirements (amount and timing of cash required to grow your business), what valuation will maximize your startup’s overall probability of success (not just closing this round), which investors are going to be most helpful to your company (if you are lucky enough to be able to choose), and how much time should you invest hunting investors instead of generating non-dilutive capital (aka – customer revenue:).

I hope that you found this useful. If you have any questions, drop me a comment. Thanks, and good luck favorably diluting your early investors!



Why does Google care so much about bounce rate?

Google significantly promotes this statistic – the bounce rate – making it available in most of its reports and even on the accounts overview page (the only other statistic shown is the number of sessions). Google focusing on bounce rate makes a bit of sense: Google sells recommendations for a living through its advertising platforms, and a high bounce rate means that it is doing a rotten job of matching consumer intent with the advertiser content.

So what is bounce rate?

Bounce rate measures the percentage of visitors who visit one and only one page of your website.

Single Page Visitors            Total Visitors – Multiple Page Visitors

————————-   =   ———————————————— = Bounce Rate

Total Visitors                                   Total Visitors


Why provide two very similar equations for bounce rate? I wanted to highlight the easiest way to improve your bounce rate: increase the number of multiple page visitors. Preventing visitors from bouncing is hard…you are trying to accomplish a negative. Getting visitors to visit a 2nd page is a lot more actionable. To improve your bounce rate, all you need to do is get more people to click on a second page. Read that one twice. Go find the pages that are causing most of your bounce problems and see whether you have made it appealing to visit a second page. Are there too many navigational elements? Are you providing your entire website on the page? Once you start thinking about how to get people to visit a 2nd page, you will find some easy ways to improve your bounce rate.

Are there better metrics?

You bet. Having a low bounce rate as a goal is like looking to purchase a car that does not explode: it is a good minimum standard, but you should aim higher. Ideally, your metrics will include a visitor purchase where money changes hands. But starting with measuring purchase activity may not provide enough insight in what is happening between that first visit and the eventual purchase. According to Google Analytics, 98% of visitors NEVER make a purchase, so figuring out where along the path customers are dropping off can help you identify were you should focus your efforts.

Why does Google obsess with bounce rate?

The genius of bounce rate is that it is the easiest “quality” metric to measure on the planet. There is no need to ask what a consumer actually wanted, or to ask an advertiser about its campaign goals. Bounce rate allows you to abstract away all of the complexity of a real-world business relationship and ask a simple question: did the visitor click on a link after following our recommendation to visit the site?

Businesses should take time to define more meaningful goals, and revisit their goals from time to time. Typical goals will include activation (e.g., visiting a certain number of pages, reading a white paper, revisiting the site), signup (e.g., for a newsletter, a trial, or a demo), and purchase.


Note: I published this article for Payboard a while back, and I am republishing it here since the Payboard blog is no more.

Simplified Startup Business Framework – CP Squared

Summary of the Simplified Startup Business Framework: CP Squared

Customer, Pain, Competition, Product. Create something that optimizes across these 4 dimensions, and you have a great shot at building a meaningful company. Fail to deliver something that satisfies your customer pain 10x better than the competition (or for 1/10th the price), and you are on your on a well-trodden path to failure. Use this super-simple form to model your business, and ensure that you understand your fundamentals before you do anything else.

CP Squared Framework
CP Squared Framework – click to expand


I have been focused on startups since 1999, when I had an idea that was going to change the world. Unfortunately, I had absolutely no idea what I was doing, and there was no framework for helping newcomers like me understand how to build a startup.

In 2003, Steven Blank published “Four Steps to the Epiphany.” In 2008, Eric Ries built on Blank’s framework and published the “Lean Startup.” In that same year, Alexander Osterwalder released the “Business Model Canvas,” a framework for visualizing your startup business model.

I relied on these tools extensively in 2012 when I was the Managing Director of the Microsoft Accelerator powered by Techstars. And we had some pretty amazing success: 1 startup from the 1st class was acquired within a month of demo day, and another startup has raised over $45 million. Despite these successes, a number of the startups struggled, and some of them failed. After the Microsoft Accelerator, I shifted back to building my own startup called Payboard, where I hoped to make websites intelligent. Unfortunately, I also failed. Overall, I have been involved with approximately 50 different startups, and a vast majority of them have failed. And for the startups that did not fail – it was as much luck as skill, and some of the startups that had the most skill on their team ended up failing. I have spent the last few months thinking about why failure happens, why success happens, and what can be done to improve a startup’s chances of surviving and thriving.

What is the Problem with Blank, Ries, and Osterwalder?

The problem with building a startup using existing frameworks is that there are too many #1 priorities. You need to build a great team. You need to be in a growing market. You need to raise money, and raise it from the right investors. You need to take care of the noise (like incorporation, and employment agreements, and software license agreements, and payroll, and SEO, and invoicing). You need to do 1,000 essential things all at once, and it is easy to get lost in those weeds and gloss over the fact you do not have a solid foundation (yet).

Let’s take a simple (and painful) example where I took an absolutely wrong path, which eventually lead to my and my startup’s failure. About 1 year into my startup, Mat Ellis (who has been able to build an awesome company called Cloudability – and it was not luck!) told me to stop what I was doing, and go figure out who my customer was. I heard him. I still remember the conversation very clearly. But I had so many other priorities going through my head that I did not listen.

Actually, it was worse than that – I listened and I understood I was wrong, and I drove on despite knowing that he was right and I was pretty much just digging a deeper grave for my startup. Why?  I calculated that if I took Mat’s advice and I really focused on who my customer was, I would disqualify most of the companies that were presently interested in working with us (including pilots like Microsoft and Moz), and that would completely destroy our fundraising efforts. So I forged on, as many a startup has, to put one foot in front of the other and keep making “progress.” We landed pilots with Microsoft and Moz and a dozen other high-profile companies, but because I did not have my customer clearly identified, and the pain that I hoped to resolve for the customer nailed, I was basically doing underpaid consulting work. 1 year after the conversation with Mr. Ellis, my startup was dead. 2 years, untold hours, and a kick-ass startup idea wasted. Bummer.

I am not alone in failure. In fact, I think failure in tech startups is far higher than the 90% estimate. But even assuming it is just 90%, that is still too high. With as many problems that need solving, we need more startups to survive and thrive, and make money solving the most challenging problems on the planet. Even if your focus is not cash, you need to make enough money to fuel growth so you can help more of your customers. And if your startup needs non-familial investors, do not fool yourself: your focus is cash.

The Solution to Startup Failure

To this end, I would like to propose a simplified startup framework called the CP Squared Framework:  Customer, Pain, Competition, Product. 90% of your time and energy should be focused on the Customer+Pain loop. If you can articulate with specificity who your customer is, and what pain they have (and back this up with 30+ customer interviews where they are telling you they have this pain), then I believe that your odds of reaching escape velocity are 10x greater than the average startup.

Once you have a locked in Customer+Pain, then you can start evaluating your Competition+Product. Typically, we start with our product. This is natural because we want to build something. But, in order to build something of significant commercial value, you have to learn what your competition has already built. If you are a startup, and you build something just as good as the market leader, you will fail. Even if you make it 10% better or 10% cheaper, you will fail. If you are building something new, you have to achieve either 10x performance (e.g., your product is 10 times better, aka transformational) or 1/10 the price as the market leader. Nobody is looking for a nice to have feature from a startup, or a little bit of cost-savings – there is simply too much risk in working with a startup to justify these nominal returns.

One word of warning: the size of your existing market is limited by the value of the competitors’ revenue selling their product to the specified customer to address the specified pain. So if your competitors are making, in aggregate, $10 million a year selling competitive products to your target customer to address your specified pain, the most you can hope to make with your market-dominating product is $10 million (assuming you take 100% of the market – probably not likely). Sure, once you are making $10 million a year, then you can have a huge team doing a number of different things and you can educate customers that they SHOULD be doing something that they are not currently doing. But to start you need to focus on the competitors that are currently in the marketplace, and how you can grab market share from them. If there is not enough competition (that you can dominate) that is currently making money that should be yours, you are going to a near impossible journey.


You can think of this as a simplified business model canvas, but I actually think of this as a complex business model canvas: since you have fewer irrelevant blocks, you actually have to get super-clear on the blocks that matter. A simple and effective plan is far more difficult to build than a complex plan. Agreed?

If you would like to discuss how this framework could apply to your startup or business, please grab some time on my calendar at

Thanks, and have a great day.


Ps-If you think that you are not a startup, either because you are in a big company, or you have been around for a long time, you might be wrong. My definition of a startup is an organization that is looking to transform an income statement – something on the order of 10x increases in revenue.



Learn Machine Learning with Datacamp (on the Side)

I have worked with Machine Learning off and on for the last couple of years. Unfortunately, it is not part of my day job, so I need to find ways to integrate machine learning into an already busy day and week.

I stumbled across Datacamp, and so far I love what I see. After creating a free account in about 10 seconds, I was watching my first sub-10 minute video. Yes, no biggie there, but that is where it gets great: Datacamp starts providing an interactive R session, along with detailed instructions and readily available datasets, where you can actually start doing your very own work – no software required. Pretty impressive.


I would love to know more about Datacamp: who are the founders, what is their business model, and how did they get so awesome? If you know the founders, please drop a comment below.

Thanks, and if you end up testing it out let me know what you think!