450000000 is a lot of users. Metcalfe's law says that the value of a communication network is proportional to the square of the connected users. Then add to this the amazing growth they are seeing. I don't think people quite realize what this means.
EDIT: In general, it baffles me how many armchair opinionators there are. Isn't it more interesting to try to understand why FB paid $16B, rather than saying why they shouldn't have?
But seriously: I've had people on conference calls when they couldn't understand the language, but someone wanted them to see slides. The fact they could join the call (in that case on Skype) definitely made the network more valuable.
You might argue that the fact you can't speak to some of them drops the value per user, and that is true to some extent. But the fact they are on there and attract other people (some of whom you can speak to) adds value for you, too.
PS: For large networks X Log X is probably much closer to reality than X ^2. Just compare the amount of internet bandwidth between NY to California vs the bandwidth between the US and China.
Metcalf's law is not suggesting "The value of the network to any random node within the network." Because that measure is very subjective in the ways you say.
Instead, it's "The total value of the network to all nodes". Because the more people in the network, the higher the probability that the nodes you DO care about are also connected.
I'd extend the rule further and suggest that the value of a network scales as some Odlyzko and Tilly suggest, but with an additional negative function subtracting value (v) from the network:
v = n(log(n) - f(n)
v = n(log(n)) - kn^2
Moreover, let's look at some group sizes which might allow us to estimate for k in various contexts.
For software team size, it's very typical that a core engineering group has a size of 5-10 members, more or less. This suggests that k is about 0.2 for software development: every added team member exacts a cost, within a single group, of about 20%. This sees value grow for 1 < n < 8, then fall with larger n, hitting negative values at about n=14.
For an elementary school classroom where the ideal class size seems to be around 22-25 students, k would be around 0.08.
For Dunbar's Number, the number of relationships people can manage (100 - 300, typically set at 150), k is between 0.028 (100), 0.2 (150), and 0.113 (300).
For city sizes, it's likely that different cities offer different matches of positive and negative factors. k of 0.0005 gives value max at n ~ 10,000, k of 0.0006 is ~ 100,000, and 0.000007 is around 1 million.
To scale to 1 billion users with net positive value means you have to keep k to less than 0.00000001. That is: any one member can have only a 1 in 10 million chance of being annoying to other members.
Or you could just design a network where new users don't annoy existing users, and reduce it down all the way to zero.
Sure. But that is just one of many possible cost-reducing mechanisms. I've expanded on this idea at more length here: http://www.reddit.com/r/dredmorbius/comments/1yzvh3/refutati...
In the case of cities, physical distribution means that even within a given city, the interactions of its citizens, while potentially very high, are generally reasonably low. It's less my direct contacts (likely within a fair approximation of Dunbar's Number) that are high, than my 2nd and 3rd order possible contacts which are high.
In a small town, those 2nd order connections are inherently constrained to the size of the population: my 300 direct contacts may expand to the 3,000 or 30,000 of a small town, but not the million or more of a large metropolis.
Similarly, for more complex organisms, you also have more complex immune systems. An interesting (and staggering) recent fact I ran across is that the individual cell mortality rate among ocean lifeforms is about 20%. Per day. If you're a cell in the ocean, you've got 1:5 odds of not being here tomorrow, because of the viral load:
So it is with other complex networks: higher levels of complexity require far more aggressive immune systems.
> The value to huge communications networks isn't in making connections but in avoiding making them at all.
The social networking startup I'm at is focused entirely on that problem. It's taken a lot of work to have the scope of possible interactions be the entire network, while at the same time, limiting the interactions to any given user to just those with a positive value.
Since the algorithms are not omniscient, we also have a ruthlessly efficient feedback mechanism whereby users can indicate when they have received a negative value communication with just a single swipe.
There's tons of interesting problems in this space. :)
we also have a ruthlessly efficient feedback mechanism whereby users can indicate when they have received a negative value communication with just a single swipe.
That's helpful, though you've really got to recognize that a huge part of the problem is assessing indirect feedback.
If you've seen Derek "Veritasium" Muller's "The Problem with Facebook" videos, one of the challenges is that interactions with FB content are hard to gauge. Dating sites have a similar challenge, in that feedback on interactions ("how did the date go") are rarely collected. As opposed to YouTube where a huge signal is "did the user stay on the page for the duration of the entire video". If I watch 5 seconds of a 3 minute video (or 30 seconds of a 60 minute vid), odds are I wasn't very impressed.
There's also the challenge of sorting out abuse of moderation systems, particularly those trying to get legitimate (but unpopular) voices banned or restricted.
The good news is that there are some people whose interactions are so widely negative (spammers and trolls) that you can attack them head on and reduce the cost coefficient significantly (recognizing that the cost constant is constructed of both a specific value and the number of connections). Spam is as annoying as it is because a single spammer affects so many other users.
The flipside is that you can increase the network value by finding people others really want to connect with. Here I see G+ as being horribly naive (also YouTube) in repeatedly making recommendations that I'm absolutely not interested in, without offering me an opportunity to say "don't show me this person" or "don't show me this product / video / category" ever, ever again. One of my long-standing challenges to the "deep data" (snooping) perspective is: rather than compile a massive dossier on my and attempt to bother me by way of it, when you do find me in an intentional mood to find something, get really good at figuring out whether you're offering me what I want or not. Why Google should know my location to within 2 feet every 60 seconds of the past five years ... but not be able to tell me the dot pitch of the monitor I'm shopping for, strikes me as a stunningly obtuse mismatch of data focus.
That said: yes, the ability to dismiss stuff I don't want to see and be bothered with is absolutely useful. As I'd repeatedly said at G+: let me say "not now", "not this hour", "not today / this week / month" (essentially: timeouts). And of course "not ever". G+'s blocking feature is also grossly inadequate. Some people, yes, I simply don't want to deal with. For others, I just don't want to see their insipid posts, or deal with their insipid comments on my own posts.
After all, that's the main way that we can consider a per-user cost to be "high".
...but did they really put a LLS line through that dataset?
Even if that was the major metric in determining a price, there's often a very large difference between the number of users currently and the expected number 1 year or 5 years out.
You don't buy a company because of the number of users it has right now. You buy it because you think you can do something better with it, or to be defensive in a field, or to expand your own customer lists.
What's more, not all users are created equal! When Flickr for instance was aquired, what percentage of users were paying users?
I think its hard to make any real generalizations here, but better than a chart of cost per user, I think this is probably closer to a chart of how badly Company X feels it needs some audience. It's some formulation of growth, panic, defensiveness, etc.
Well, it does matter, after a fashion, and it's a one (of many) metrics that an acquisition can be considered on.
I found it interesting to look through the list for obvious winners and loses, and actually, that would be a much more useful plot: showing the cost-per-user on one axis against, say, the 3-5 year investment performance on the other.
Actually, better than that even would be to look at a number of dimensions and plot these against one another looking for patterns in the data. R is particularly good at this with its scatterplot matrices:
- When investing in or acquiring companies, people may use this metric in addition to price/sales or in rare cases for tech companies price/EBITDA valuation multiples. It forms the basis for comps analysis which bankers, M&A types and VCs do all the time.
- Companies use the ratio in similar ways when fundraising or talking to acquirers. "Based on how many users we have and comparable transactions, we're worth $X million"
- While you may buy users looking forward, you also may look at how you can "monetize" existing users, i.e., if we can get $X per existing user, that would be worth Y.
It is of course not the only metric that an acquirer evaluates (at least the good ones), but they do look at it as do investors, bankers and startups. So good or bad, it is here to stay.
Probably a lot more than there are now. As a former paying user of Flickr, Yahoo really stopped all development for about five years. Now, I have no reason to upgrade to a pro account.
I am amazed at how well that particular calculation works some times although I understand the statistics.
> number of users it has right now.
> or to be defensive in a field
It's sort of close, but we're still talking about a swing of >30% there -- $5 billion dollars in WhatsApp's case.
There are many things missing from "cost per user" but I think the first thing to ask is "user doing what"?
"User doing what?" is a decent start. But of course, we have a host of other considerations to ponder: technology, platform, integrations, competitive blocking and tackling, etc.
FWIW, I still can't wholly grok the WA purchase price. The price seems largely to have been driven by competitive bidding between FB and Google. Also a healthy sprinkling of defensiveness and fear, mixed with a dollop of "because we can." Factor in some "we believe our stock is overvalued, and as such, we will realize a discount on price correction," and I suppose you arrive at a figure that will end up south of FB's nominal purchase price. The one thing I can say is that Instagram looks likes a ridiculous bargain in retrospect, and especially in comparison.
That said, to whatever extent we do want to talk about user counts, it's worth noting some history. Around the time Facebook had an estimated $15 billion valuation, on the heels of Microsoft's advertising partnership/investment in 2007, Facebook had about 50 million active users. WhatsApp has 465M+. Apples and oranges, to some extent. But still. Worth considering.
I'd forgotten completely about YouTube. At under $2B, that must have been one of the best acquisitions of the whole bunch.
YouTube for 1/10 the cost of WhatsApp.
I tend to think more about acquisitions as capital per employee, as in, that's how efficient they are at creating value. It's more interesting that comparing users because of the "users doing what" question. I'm disappointed i can't find graphs about those. Last companies I checked (but I can't remember which ones), the capital was around $1m per employee. Except for WhatsApp.
When was the last time you paid Google for search results? Meh, can't be making that much money.
Nice way to show I was wrong :)
If our patience is worth any money, Youtube is extracting it in spades now.
On a related note, 11K per user is insane. What did Yahoo see in Broadcast.com?
There's probably a bug in the visualization library getting the thousands coma as a decimal separator, too bad :)
and does it include the youtube legal deals and fines? probably not
There's no accounting for inflation. Comparing a nominal 1999 dollar amount with a nominal 2014 dollar amount is like comparing a euro amount with a dollar amount without doing an conversion (which would actually still be more accurate).
Now is a good time to be in the yacht business.
Aardvark, Jaika, Dodgeball, Picasa, Broadcast.com, FriendFeed (missing), maybe Flickr as well as more recent acquisitions like DeepMind ($900M to Google, zero users) were bought for some combination of the team and the technology.
Some other dimensions I'd be interested in are the number of employees, year, current number of users, and (for public companies) current market cap. For acquisitions I'd be curious if there are any decent objective metrics of the "success" of the acquisition.
WhatsApp has a huge set of "new" users on new and old devices. So, how again is a WhatsApp user on some random (non-smart) phone in India, as "valuable" to Facebook as one sitting in the Valley with their iWhatever.
I quoted a source at $80 per actual users base on the Monthly Uniques.
When a service has a lot of users, it has no room for growth and cost per user will be small.
When a service has a few users, but has potential to grow exponentially, it will have high cost per user.
Economically there's a large difference between services with respect to their monetization. A service where each user pays $1000 will have higher cost per user than a free service.
So yeah, users not necessarily the most relevant metric there. Which, upon reflection, makes YouTube even more of a steal than it looks in the chart.
The real target were the feed subscribers, or maybe they are where can I check the data?
The same goes for Youtube, how many visitors did they have? They bring in the ad revenue.
Compared to Facebook/Whatsapp where you can usually only participate by being a user.
In most cases it's not just the users they're trying to get, but also the talent (with the idea that most of them aren't one hit wonders).
Acting like startup acquisition is a single-metric game seems like a gross oversimplification. This chart only proves it by showing there is little correlation between user count and buy price.
Apparently it was a Q&A site that asked questions to friends and friends-of-friends.
This chart is the sweetest of schadenfreude — so many terrible acquisitions for huge sums of money.
I think the answer is that we suck at fighting for our own interests. A traditional union might not be the answer, but something like the SAG might be the way to go. It's absolutely inexcusable that computer programmers don't, for example, have the right to have a representative when negotiating with management or HR.