The Wall Street Journal has a long article about this today, noting “The End of the Email Era.” But most of that article is spent focusing on how Twitter and Facebook, which is to say, status updates and the streams, are replacing our need for much of what email has provided in the past. Only very briefly do they mention Wave. And I think that overlooks something.

For many of us, email is simply not cutting it the way that it used to. It’s a sedentary beast in a fast-moving web. It uses old principles for management, and this is leading to overload. I think the key statement in the WSJ is this:

We all still use email, of course. But email was better suited to the way we used to use the Internet—logging off and on, checking our messages in bursts. Now, we are always connected, whether we are sitting at a desk or on a mobile phone.

That’s absolutely true. But that also implies that we want some sort of always-on communication connection. I don’t think that’s the case. I think we want the option to communicate in real-time at will, but also the ability to communicate at our leisure at times. I would consider this to be a desire for a “passive-agressive” method of communication. Perhaps it would be better stated as a “passive/active” method of communication, but passive-aggressive sounds better, so we’ll go with that.

I would consider email to be a passive form of communication. I don’t mean that you don’t respond to it, I mean that you don’t have to respond to it right away. Instant messaging is at the other end of the spectrum. If used correctly, it’s supposed to be an “aggressive” or “active” form of communication in which you respond immediately. Twitter is very passive because the use of it is such that people don’t even necessarily expect a response of any kind, even if they point a message at you. Facebook is a mixture of all of those things (more on that below).

Google Wave is attempting to be a passive-agressive form of communication. You can actively (aggressively) engage in threads in real-time, or you can sit back and let messages come to you at your leisure (passively). Having used the product for a few months now, and after using it quite a bit more actively with my friends these past few days, I really think that Wave is onto something with this method of communication. I would argue that Google Wave’s new message alert system needs to be somewhat reworked or re-imagined, but I do think the desire to blend passive and agressive methods of communicating is there.

We’ve been slowly building up to a system like this. Gmail has for a while offered users a nice blend of email and instant messaging on the same page. And while it is nice that there is also the option to archive all your chats for searching purposes later, there is no good way to say, see that you missed an IM if you have a computer with Gmail open at home while you’re away and checking it remotely. You also can’t check these easily via IMAP on your phone, and the like.

And while there is the option to reply to emails by chat if that person is online, there’s no real integration between the email message and the IM message, they exist as two totally separate things. It seems like we’re at the point now where that shouldn’t have be the case.

Others, like Yahoo Mail, are now trying to tack-on status updates and the stream to email services too. The result is a Frankenstein-like service.

Facebook is another interesting example in that, as I mentioned, it combines all of these elements: Email, IM, status updates, and a stream. But the connection between all of these things in that system is loose at best. From a unified communications standpoint, Facebook is really kind of a mess. There are whispers of changes, and I hope that’s true, but I’m not holding my breath for a service with 300 million users to do something new and drastic that will alienate a certain (probably large) percentage of its base.

That’s why Wave is interesting. It’s backed by a huge company, Google, but it’s not trying to shove this upon all of its Gmail users. Instead, they’re going to slowly roll this out and see how users end up using it. And maybe more importantly, they want to see how developers start using it.

And that’s really a key that a lot of early users are overlooking. Right now, when people hear “Google Wave,” everyone seems to want to place emphasis on the “Google” part of it. But the truth is that the grand goal of the team behind the project is to emphasize “Wave” as both a platform and a new communication standard.

Whether Google Wave succeeds is really irrelevant. More important is if the idea of Wave does. Again, the idea of passive-aggressive communication.

Wave, the Google web-based client, will only ever appeal to a certain number of users. Does anyone really think that Twitter would be where it is today if they only had twitter.com? No. Wave desktop apps, and mobile apps, internal company Waves, and public Waves; it’s the platform, not the product, that’s interesting. Or, more to the point, it’s the key communication idea behind it.

[photo: flickr/matheus sanchez]

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A chronological system for indexing information breaks down quickly once the amount of information received reaches a certain critical point. Active users of email constantly moan about the information overload they experience, and the information is only a load because it is difficult to sort through and manage in modern systems. According to the cognitive theory of choice complexity, that feeling of load multiplies with each incremental increase in choices and decisions having to be made. In the email world this leads to a complete breakdown, and the trend of email bankruptcy (deleting all email and starting again).

Chronological order became more common on the web as social networks, such as the Facebook, blogs, feeds, feed readers, FriendFeed and services such as Twitter designed around the same paradigm – leading to most recent being most important. Some call it real-time, others call it information overload.

A default view of chronological order presents a natural barrier to the number of information sources that can be managed effectively (Scoble somehow broke the barrier, he is an exception). With only a few dozen feeds, a hundred or so emails a day and following one hundred or so people on Twitter, I find myself constantly behind and not being able to manage. When I am reading these sources, I find myself simply scanning for what is most relevant and most important – for eg. I will quickly reply to an email from a co-worker, while leaving others to slowly creep into the abyss of my archive.

Chronological order needs to be abandoned in favor of relevance. Without relevance, our ability to manage large sets of information is inefficient. The technology for relevance exist today, for eg. spam filters are able to tell us what we definitely don’t want to read. Real world information retrieval and organization is based on relevance, either what somebody else believes is relevant to us, or what we decide is relevant. Newspaper stories are not laid out in the order that events took place and libraries do not catalog their books in the order they were published.

Web applications that present relevance over chronological have proven to be popular. Techmeme hacked RSS, and instead of reading 50 feeds I can have Techmeme read 20,000 for me. Community-powered sites such as HackerNews are similar, they float up the latest content based on what a like-minded community finds interesting. The TiVo hacked television by taking chronological out of the picture and applying relevance.

Email applications have attempted to hack what is essentially relevance into the traditional chronological order. Old desktop email clients introduced folders and filters. Gmail introduced labels, adding a star to a thread and grouping multiple emails into a thread. Yahoo Mail attempts to highlight emails that it believes are from people close or important to you.

I hand over a lot of information to the applications that I use every day, but I am getting nothing in return (other than ads that creep me out). Every time I click a ‘like’, or I re-tweet, or I bookmark a page, or I spend time reading a post, that information can be stored somewhere and used to figure out what information is most important to me. I would happily exchange that part of my privacy for the ability to save a few hours each day and the pain of having to personally sort through all this information.

The ingredients for a personalized aggregator of all information exist today. A working solution would allow me to funnel far more data into my stream, and to not only discover more, but become more efficient. The second by second and minute by minute chronological order paradigm is broken, and like QWERTY, is a legacy from a world where systems were not smart enough to determine relevancy and real networks did not exist.

Original backwards post here

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This is a guest post by Robert J. Moore, the CEO and co-founder of RJMetrics, a on-demand database analytics and business intelligence startup that helps online businesses measure, manage, and monetize better. He was previously a venture capital analyst and currently serves as an advisor to several New York startups. Robert blogs at The Metric System and can be followed on Twitter at @RJMetrics.

A few weeks ago, my former employer led a $100 million investment into Twitter and I must admit that I was quite jealous of my former colleagues. Chances are they got the opportunity to do some very cool analytics on Twitter's data.

Rather than wonder about what I missed, I decided to figure out what I could from the outside looking in. Using some statistical trickery, the Twitter API, and my RJMetrics dashboard, I uncovered a ton of astonishing new information about Twitter. Here are some highlights:

• Twitter's user growth is no longer accelerating. The rate of new user acquisition has plateaued at around 8 million per month.
• Over 14% of users don't have a single follower, and over 75% of users have 10 or fewer followers.
• 38% of users have never sent a single tweet, and over 75% of users have sent fewer than 10 tweets.
• 1 in 4 registered users tweets in any given month.
• Once a user has tweeted once, there is a 65% chance that they will tweet again. After that second tweet, however, the chance of a third tweet goes up to 81%.
• If someone is still tweeting in their second week as a user, it is extremely likely that they will remain on Twitter as a long-term user.
• Users who joined in more recent months are less likely to stop using the service and more likely to tweet more often than users from the past.

Read on for some detailed charts a deeper dive into the data.

How We Did It

In most cases, this kind of outside-looking-in exercise wouldn't be possible. Twitter, however, is a special case for a few reasons:

• The company is pre-revenue, so its value is wrapped up in user activity and engagement
• A Twitter user's activity data (tweets, followers, etc) is all public by default
• Twitter's API allowed me to automatically download up to 20,000 data points per hour
• Twitter uses auto-incrementing ID numbers (1,2,3,4…) for both users and tweets
• The central limit theorem tells us, among other things, that a large enough random subset of a large data set will behave like its parent set with a high degree of statistical confidence

In the end, our sample size consisted of about 85,000 users and just over 3 Million tweets. By piecing all of these things together and pulling the data into the RJMetrics Dashboard, I was able to chart loads of information about Twitter's user base and user behavior. I've looked around, and this appears to be the largest public analysis of Twitter's user base online. Enjoy!

Number of Twitter Users

This analysis leverages the fact that Twitter uses auto-incrementing ID numbers for both users and tweets. We identified the range of IDs that were consumed by the system in any given month and the percentage of them actually tied to real Twitter accounts. ("Dead" IDs are likely canceled accounts, SPAM accounts, test accounts, etc.) In combination, these numbers give us a reliable approximation of how many new users joined Twitter each month:

This shows us the exponential growth experienced by Twitter in 2009. In Q3, this plateaus at a rate of about 8 million new users per month. A chart of total cumulative users is below:

Hockey, anyone? As of September 1st, the actual number of live Twitter accounts was just above 50 million.

Average Number of Followers

According to the data, the average Twitter user has 42 followers. It's interesting to see the distribution of users by the number of people following them:

As you can see, the vast majority of users have ten or fewer followers, and over 20% have no followers at all! As we know, most users have been on the system for less than a year and, as shown in the chart below, the number of followers is proportional to the user's time since joining:

Number of Tweets

It's also interesting to look at the number of status updates, or "tweets" made by the average user. Obviously, the number of tweets from any given user grows over time (per the trend shown in the chart below):

When we look at the distribution of tweets by user, we see a very surprising trend: over 75% of all Twitter users have tweeted fewer than ten times.

"Protected" (Private) Twitter Profiles

Before moving onto analyses at the tweet level, it's important to note that some of the users we identified have "protected" their tweets, meaning we were able to see how many followers they had and how many times they had tweeted, but were unable to download specific tweets (and, more importantly, tweet times).

The chart below shows how many users in our data set are "protected" by the month they joined. The overall number sits around 10% (and dropping):

Also interesting is how "protected" Twitter users differ from public users. As shown in the charts below, protected users tend to tweet far more often, but have far fewer followers:

Power Users

Another limitation of the API is that it can only return the 3,200 most recent tweets for any given user. This is obviously not a big deal for most users, but there are some users out there who have passed that mark. Our sample data set showed that less than 0.02% of Twitter users have sent more than 3,200 tweets. These users will have incomplete data sets in our study, but the population is so small that they should not have any meaningful impact on our conclusions.

Tweets by Source

It's interesting to see how different tweeting methods have risen up over time. Below I show the most popular methods and what percent of Twitter traffic came through them each month since 2007:

The web clearly dominates this list. Let's exclude it to get a closer look at which other sources are driving tweets:

Twitterriffic has clearly seen better days, and text messages (txt) have been declining as a channel, as well. Meanwhile, TweetDeck appears to be aggressively gobbling up market share.

Time Between Tweets

Since we know the timestamp of every tweet in our sample data set, we can study the time between tweets and the recency of tweets from the userbase.

Remarkably, the average time between any two tweets from the same user is exactly 24 hours.

The chart below shows the average amount of time between tweets for a user's first ten tweets (when applicable). The x-axis contains the time of the tweet in question, and the value is the average amount of time since the previous tweet.

Surprisingly, the time between Tweets actually drops as users do more tweeting. However, this could be biased by the fact that most users have tweeted fewer than ten times. To clear things up, let's look at the average time between tweets based on how many times the user has tweeted:

Indeed, as you might expect, users who send more tweets also tweet more frequently, and the dropoff is quite significant.

Probability of Incremental Tweets

Since there is such a huge dropoff in tweeting activity up until the 10 tweets mark, we thought it might be interesting to look at the "probability of an incremental tweet" based on how many tweets a given user has completed. This can be calculated with just a few clicks in RJMetrics:

As you might expect, with every Tweet a user performs, their chance of tweeting again goes up.

Active Tweeters

We know that Twitter has 50 million registered users, but we also know that the vast majority of them have tweeted fewer than ten times. Let's investigate just how many of these registered users are actually actively tweeting.

Using our tweet data, we can identify what percent of the user base sent out at least one tweet in any given month. This "unique tweeters" statistic is charted below (to get a fair statistic we excluded protected accounts from our denominator):

The number seems to hover in the 25% range. In other words, only about 1 in 4 registered users is actually tweeting in any given month. (Although it's worth noting that some users may only be using Twitter to read others' tweets, meaning they are not full-fledged "zombie" accounts.)

Notice the bump in early 2009, right around the time when new user growth began to accelerate aggressively. This suggests the obvious: on average, a newer user is more likely to tweet than an older user. When new user growth exploded in early 2009, the concentration of new users became denser, driving this average up. To illustrate this (and get a better look at how users behave over their lifetime), we turn to cohort analysis.

Cohort Analysis

A cohort analysis is a great way to look at user behavior and loyalty over time. Each line in the chart below represents a different "cohort" of Twitter users based on the month they joined (we chose 7 cohorts from different time periods to avoid clutter). In the chart below, we monitor what percent of the users in each cohort come back to tweet again in each month after having tweeted in the first month. Obviously, month 1 is 100% by definition:

This is quite a telling chart:

• There is an expected usage dropoff in month 2, but after that point usage holds predictably steady. This is great news for anyone trying to forecast user activity early on in a new user's lifetime.
• The newer cohorts, despite being significantly larger in size, actually consist of more loyal users. The two highest lines are also the two most recent, meaning that users who joined in 2009 are actually more likely to keep tweeting after their first month than those who joined in the same month in 2008.

Since the dropoff in Month 2 is quite pronounced, let's zoom in and look at weekly cohorts to see if we can see how usage drops off at the weekly level:

We see a similar pattern here, although more recent cohorts don't stand out as much as in the monthly analysis. Again, however, the dropoff in the second period doesn't seem to further decline as time goes on. This means that by the second week of a cohort's lifetime, Twitter can reliably predict its users' future behavior as a group.

Another cohort analysis that might be interesting is to look at how many tweets a cohort makes each month after joining. This metric will incorporate both the dropoff in usage from the users who churn in the first month and the uptick in activity from users who stay on the platform:

Wow! This is a remarkable image. Despite the massive dropoff in users after the first month, the tweeting activity from the users who are left is so voluminous that it makes the "tweets per month" of each cohort average over 100% (and, as before, the more recent cohorts are the more loyal)!

In other words, the users who stick around actually tweet so frequently (and at such a rapid pace compared to their first month) that they more than make up for the lost activity of those who churned after the first month. This is a very powerful and unexpected statistic.

Conclusion

Everyone has their own feelings about Twitter's reported $1 billion valuation. I hope this article gave you a taste of what its new investors likely considered before coming up with that number.

To learn more about RJMetrics and our original blog posts including the business intelligence rap and our twitter followers guide, check out our website and follow us on Twitter @RJMetrics.

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But the idea has lingered (really), and now Google is taking a shot at their own version of the service. It’s called Sidewiki, and it just launched.

Sidewiki is part of Google Toolbar (and it will be built directly into Google’s Chrome browser). Users activate the service by clicking on a button and a sidebar appears to the left of whatever website is being viewed. The user can then leave a comment on the entire page or a selected piece of text, and share the URL via email, Twitter or Facebook (stalwart Google partner MySpace is left off for now, but Google says they’ll add more partners later). Users can also embed videos into the Sidewiki.

Other users can read and vote comments up or down. All those votes will create a user ranking for each individual that will determine where their comments fall on the Sidewiki. The higher the ranking, the higher comments appear. The goal, Google says, is to help move better content up and move spammy stuff to the bottom where it won’t be seen. Website owners who have claimed their sites always have the right to the first comment on any URL they control.

Google says Sidewiki is absolutely separate from last year’s SearchWiki, and comments/votes won’t be aggregated.

Besides the sites I listed above, TechCrunch50 startup DotSpots, which launched publicly last week, is very similar to Sidewiki. Its no surpise, then, that Google VP Marissa Mayer liked Dotspots so much when it first demo’d in 2008: “It’s a really beautiful idea and I really like anything that pushes the web forward in that way.”

Will this work? It’s unlikely that websites will have the same visceral reaction today that they did to Third Voice a decade ago. And Google solves the chicken-and-egg problem nicely by building this into Toolbar. The real question is whether they can control spam, which has plagued SearchWiki. And I guess the other real question is, how long until they put ads in it?

Google Toolbar users will be upgraded over the next several weeks automatically and offered the use of Sidewiki. Those of you who want to try it immediately can find the download link here. Google is also making an API available for developers to access and use the data.

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Now, on the profile for any Twitter user you follow, you will notice a little mobile phone icon surrounded by a circle. If the circle is clear, SMS updates for that user is off. If there is a green background, SMS updates are on. You can also hover over the icon to see if they are on or off. Clicking on the icon will turn them on or off. On your Following page, you will also see this same icon under the “Settings” column that you can toggle on and off.

Both of these tweaks provide a much more obvious way of handing SMS alerts. And it makes them much easier to switch on and off. Previously, there was no way to control this on individual profiles and you had to drill down into your followers to toggle them on and off.

Maintaining and expanding SMS support is important for Twitter is other parts of the world, where it remains a simple and effective way to communicate with the service. SMS remains a colossal rip-off for what it is, but in some areas, people who want to use Twitter may not really have many other options, as not all countries have affordable iPhones with data plans.

The next step will be for Twitter to turn on SMS track functionality, which will allow you to get pinged every time a certain keyword is mentioned on Twitter. But hey, one step at a time.

Note: If you aren’t seeing SMS updates working, make sure you check your Settings -> Devices area. Here, you should see your mobile phone number and in the Device Updates drop down, this should be set to “On” if you want to see updates. This is the place that you can also set Twitter to only send you text messages when you get direct messages.

Also, be careful. When I turned on Twitter notifications for the first time in a while, I was bombarded by text message tweets from hundreds of users I didn’t even realize I had turn the feature on for (not only is this annoying, but again, it’s a rip-off depending on your messaging plan).

The functionality still needs some UX tweaks — where’s the “set all to off” button? — otherwise it’s simply easier to keep them all off rather than going through each one to make sure they’re off.

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What are the top Twitter apps on the iPhone? It’s hard to say because the iTunes store doesn’t have a Twitter category (Twitter apps are lumped in under “Social Networking). But AppsFire, the iPhone app sharing service, might have an answer—at least for the early adopter crowd who tend to use AppsFire (i.e. people like you, dear reader). AppsFire looks at apps actually downloaded and kept on people’s iPhones. Based on a sample of 1,500 AppsFire members, the top ten Twitter apps on the iPhone are:

1. TweetDeck
2. Tweetie
3. Twitterific
4. TwitterFon
5. IM+
6. Twitelator Pro
7. Twitterrific Premium
8. iTwitter
9. ShoZu
10. Boxcar

Notice that 7 of the top ten Twitter apps are paid apps. The only free apps are TweetDeck (No. 1), Titterific (No. 3), and TwitterFon (No. 4).

AppsFire also collected some data on what percentage of users download Twitter apps compared to Facebook apps (see slideshow below). While it found a total of 32 different Facebook apps on user’s iPhones, compared to a whopping 102 Twitter apps, more users had a Facebook App on their device.

The No. 1 Facebook app, of course, is the official Facebook app for the iPhone, whereas Twitter does not have any official iPhone app. A full 70 percent of users in the AppsFire sample had the Facebook iPhone app, while only 63 percent had a Twitter app. So much for diversity.

The one thing AppsFire does not know, however, is which apps are actually used more often. You can help answer that by answering the poll below:

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