# 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.

# 50/50

I was lying down on dentist chair, with my dentist busy drilling out a cavity. She tells me, “I think I did all I can but tomorrow if you feel pain of any kind then it is root canal”.

I asked her, “What are the chances I need root canal procedure?”

To this she replied, “50/50″.

I said, “50/50? How kind of odds is that? Can’t you tell now whether or not you hit the nerves and hence know whether or not I need root canal? Can’t you tell from all different cavities you have filled how likely is that I will need the painful procedure?”

With a kind expression that only dentists have she replied, “It doesn’t matter about all others. Tomorrow when you eat ice cream you either have splitting pain or not. That is all matters. So it is 50/50 for you”.

The mere fact that either I will be in pain or not (2 states) was her reasoning for 50/50.

She is my dentist and I am not going to speak against here. But I am going to point you the prediction by a reporter on how likely will Amazon introduce free Kindle. His answer, as you see above, is 50/50. The fact that there are only two outcomes does not mean they are equally probable. Take for instance lottery ticket, the fact that you will either wake up as lottery winner or not does not mean your chances of winning is 50/50.

50/50? That is not odds at all. That is just someone making things up when they don’t know and/or don’t know how one assign probabilities to future events. So in Kindle case whether or not we see free Kindle a 50/50 prediction covers both cases. The reporter can always say, “I told you so”. Such a prediction is not actionable and does not constitute probabilistic thinking.

So how would make probabilistic predictions? Let me take you back to the dentist case. Suppose she has seen 1000s of people with condition close to mine and knows the outcome after she filled their cavity. Or she has access to a database of similar cases documented by many other dentists. Then based on the outcomes (prior knowledge) she could say, “of the 1000 people who has such a cavity filled 503 of them did not need root canal, so your chances are 50/50″.

What does it mean in the case of making business predictions like predicting Free Kindle? Let me take you back to the Pinterest article I wrote and the companion article.

Imagine enumerating all possible future scenarios of Amazon’s device strategy and computing the outcome. These scenarios are the equivalent of many different futures based on numerous variables like cost of production, demand, market forces, amazon’s other opportunities etc. Then we simply need to look at in what percentage of the scenarios does a free Kindle deliver more profit to Amazon than charging for it.  Only after such an analysis can we say anything about the chances of a Free Kindle.

Since no one has done such a scenario analysis, to say “50/50 chance of Free Kindle next year”, is simply pointless.

And by the way, next time you hear 50/50 it is perfectly okay to assume the speaker has no clue because in 99.9999% of the cases no one has done the scenario analysis to make such probabilistic prediction and if they have they likely will not use an expression like 50/50.

# I don’t know about your conclusion but how you arrived at it is flawed

It has become common place in the tech and other marketing blogs – popular bloggers  making bold predictions about the future of a firm or about a technology. They state it with utmost confidence without any uncertainty. All black and white, no room for gray areas.

They back it up with scant evidence which is mostly selectively chosen to support their preconceived notion. Or these evidences are anecdotal or manufactured based on their own biased reading of the market. They fail to seek any evidence to the contrary or refuse to ask questions on what evidence will disprove their theory.

On top of this poor evidence seeking behavior we see even poorer analysis. All set up to support their foregone conclusion.

The latest in that mold comes a blog post that boldly proclaims how an enterprise software business that

• hauls in \$35 billion in revenue
• has \$29 billion in cash
• has almost limitless borrowing power
• has a market capitalization of \$142 billion

is really doomed.

So writes Sarah Lacy in her blog.

I am not going to argue with the conclusion. I do not know what the future holds. But let us raise some questions on her  analysis (if we can call that):

1. What does doomed mean? \$35 billion in revenue wiped out? Cut in half?
2. What are the assumptions and preconditions for this outcome? Remember, a model is only as good as its assumptions.
3. What time frame are we talking? 5 years? 10 years? 20 years? Did she consider how long will it take to lose any portion of the \$35 billion revenue or any of the \$29 billion cash? What other technology and market shifts could happen during that period?
4. One way to make a prediction is to run a scenario analysis and state in what percentage of the scenarios (and under what conditions) a particular outcome is possible. An example of such an analysis is the one by Jeremy Siegel when he predicted Dow will hit 15000. Has she run through such an analysis? What are the alternative scenarios and how likely are they?
5. Let us consider a case where the revenue drops by 10%. Will the decision makers stand still and let the slide continue all the down to 50% and finally to 0?
6. How many millenials will go on to install what she calls as skunkworks implementations? And if they do, will the business in question standstill and not use their cash pile and borrowing power to buyout these smaller solution providers?
7. The story  is titled,   “\$142 billion market cap business is really doomed”, backed by anecdotes quoted from selective memory and adorned with flashy narratives sounds plausible, but is it probable?

If the analysis is flawed or if the analysis is just a figment of one’s imagination, if you and I cannot reproduce the same results with the data set (in this case there is no data set) we have to reject the conclusion regardless of how plausible it sounds or how popular the blogger is.