## What would \$100 Billion Valuation for @Evernote Look Like?

In a recent article in Inc magazine, Evernote CEO, Mr. Phil Libin, wrote

” there is a good chance that it will be worth \$100 billion in a few years”

You likely want to ask what “good chance mean”.

Mr. Libin wrote this in the context of  Evernote’s current one billion valuation and comparing it valuation of The New York Times. Mr. Libin’s makes a very valid point that such comparisons are point less and valuations are based on future expected value from a business’ growth.

I agree.

Most public companies have relatively predictable levels of growth, so their valuations are heavily based on the current values of their businesses. In other words, few investors expect The New York Times‘s profits to grow tenfold in the next few years.

Such valuations on future growth are valid as long as they are computed by taking into account all possible future scenarios and not just the most optimistic outcomes. In many cases, and I don’t mean it is the case with Evernote, we not only overestimate the size of positive outcomes but also overestimate the chances of such outcomes. In such cases the valuations become segregated from reality.

Back to the \$100 billion valuation for Evernote. What would it look like?

Let us say it gets the same revenue multiple of 5.51 (say 5 for ease of math) as Google. That would mean \$20 billion in yearly revenue. Where would that come from?

From its current sources I estimate that Evernote makes \$63 to \$84 million a year from 34 million users (1.4 million paying subscribers). If the current business model is the only option that would mean one of following (or combination)

1. Every customer generates \$45 a year, meaning 444 million paying customers (13 times current user numbers and 31 times current paying subscribers)
2. 50% paying customers, meaning  888 million users
3. 100 million customers (not users), meaning \$200 a year revenue per customer – that means either their subscription price goes up or they found other ways to monetize customer. \$200 a year just from subscription does not make sense (NYTimes yearly subscription costs \$195 and it did not find 100 million subscribers). Regarding other revenue sources even Google and Facebook have not found a way to get \$200.

Even if Evernote does deals like Moleskine tie-up that generate \$4-\$6 million a year, that is a larger number of deals to get to \$20 billion a year sales.

That leaves other sources of revenue that are not yet known from its current strategy. Which means one must consider higher uncertainty in such large outcomes given insufficient information.

Mr. Libin said, “there is a good chance”. Given what is known today and the uncertainties I am not sure what “good chance” means.  But given the current valuation of \$1 billion, investors seem to think the expected value of the valuation (considering all good and bad chances) is \$1 billion. Or in other words, the numeric value of good chance is much less than 1%.

A question you must ask is,

Is there also ‘good chance’ of \$200 million valuation? (See: Zynga)

Finally  I am not going to run a complete scenario analysis here as I have done for other valuations before. That is left as a homework for you.

## Let Us Do Expected Value Math on \$0 Price

Once again the power of \$0 price is in the news. This time in The Wall Street Journal, featuring research published in Journal of Consumer Research. It is the previously famous Hershey’s experiment from Dan Ariely’s work,

“In one of their experiments, participants were offered a choice between a cheaper lower quality chocolate (Hershey’s) and a more expensive higher quality one (Ferrero Rocher). The price of the chocolates was manipulated between subjects in the following manner: two cents & 27 cents, one cent & 26 cents, and zero & 25 cents. Results showed that whereas there was roughly an even split between the two chocolates in the first two conditions, 90% chose Hershey’s when it was free, indicating a discontinuity in the cost-benefit evaluations. In other words, consumers over-reacted to the free chocolate.”

As it had been said before, “something magical happens at \$0 price”.   So a strong case is made for giving your product away for free regardless of the experimental conditions and its applicability to your particular scenario. You are told that Free is Free marketing. But no one bothered to do the math for you on what is the expected value of free. Let us do just that here.

Let us assume the costs are all sunk since you already bought the chocolates. From the text in bold above you can see that:

1. When the price was 1 cent for Hershey’s and 26 cents for Rocher,  the choice was even, that is 50%. So the expected value of the customer is  (0.5 * 1 + 0.5 * 26) =  13.5 cents
2. When the price was 0 cent for Hershey’s and 25 cents for Rocher, the choice was 90% Hershey’s and 10% Rocher. So the expected value is (0.9*0+0.1*25) = 2.5 cent

So which option would you choose? One that has an expected value per customer of 13.5 cents or 2.5 cents.

If you believe the free customers generate other revenue, then each one has to make up additional 12.2 cents from whatever means it is.

Just by giving up the 1 cent on the price you could lose much more than 1 cent. In this case, you lost  11 cents and left with the hope that you will somehow make up for it.

What does this say about the wildly famous Freemium model? The Freemium model is about having a free version to get users and hope that they will convert over to paid premium version. Simple calculation from Hershey experiment shows presenting a free version is much worse than presenting a low priced version alongside premium version.

Isn’t it time you do some hard math and reject fads and pseudo economics of social media gurus?

## Ignoring the downside

I was talking to someone the other day about house prices. He was convinced that house prices have hit the bottom and there is only one direction it can go – up. He was convinced that the home prices will go up by 25% in the next five years. This is the classic optimism bias. We are all trained to just see the upside and ignore the downside. It is not a certainty that house prices will go up by 25%, there is a whole variety of scenarios, each with different probabilities. In statistics speak this is called a probability mass function.

Even when people take into account this uncertainty and assign a lower than 100% chance for the upside, they continue to look at only the gains and ignore losses. For example, let us say someone wants to buy a house for \$1 million. If  think that there is a 80% chance the house prices will go up by 25% in the next five years, they compute the return as .8*.25*1,000,000 = \$200,000.

But what about the remaining 20% of the cases? If the prices remained same or rose slightly in these cases  then it is not a problem. What if the prices go down by 10% in the remaining 20% of the cases? Their net return is now

200,000 –  0.2*0.1*1,000,000 =  \$180,000

Be it buying a house or starting a venture, by overestimating the upside and by ignoring the negative impact of downside, we all take bigger risks than the numbers warrant.

Next time someone in your business says, “there is a 80% chance we are going to get 25% return on this investment”, ask them how they arrived at the 80% number and what will happen in the remaining 20% of the case.