How Twitter Could be 10X Bigger, 100X More Profitable, and 1000X More Awesome

Read my new article about how to evolve Twitter, on VentureBeat

 

I’ve spent many years studying, writing about, building, and funding companies (such as Bottlenose, Klout, and The Daily Dot) in Twitter’s ecosystem.

Despite the media chatter, I am still bullish on Twitter – as should be any investor who understands the social network’s fundamentals and true potential. Twitter has the highest revenue growth rate of any tech firm with over $2 billion in sales over the last year. And at today’s market cap, Twitter is an incredible bargain.

The company has enormous untapped potential to impact the world and create value for investors and partners — far more than short-term investors probably realize. But to unlock that hidden potential, some significant product and business model evolution may also be necessary.

I truly want the Twitter ecosystem to succeed. And it is in that spirit of support and optimism that I’m offering a number of ideas below that could help Twitter not only regain its former growth curve but surpass it. I’m breaking down my detailed playbook for the company into three sections:

1: Improving the signal-to-noise ratio on tweets
2: Enabling better search and collection of tweets
3: Focusing on being a network not a destination

 

My Forbes Interview – People May be Brands, but Brands are Not People

I was recently interviewed by Blake Morgan at Forbes, on the subject of “Building Influence in the Digital Age” — listen to the interview here:

Peter Drucker’s grandson Nova Spivack, CEO of Bottlenose, says that Drucker would have felt today that real influencers are not spending a lot of time on social media. In The Modern Customer Podcast this week we talk to Spivack who is an entrepreneur and investor who at six years old remembers being in line behind Jack Welch for an appointment to spend time with his grandfather, one of the most famous management thinkers of our time Peter Drucker. These memories are vivid for Spivack who today spends time thinking about the big business questions we face today. Spivack believes his grandfather felt real influence is not visible but built through face to face interaction. From personal branding and influence to building a brand’s influence, we cover it all in this podcast.

How to Solve Twitter’s Engagement Problem: Add Semantics

 

The fundamental problem that Twitter has is engagement. If engagement can be corrected, the whole Twitter ecosystem (and their stock price) will improve.

Improving Twitter engagement comes down to fixing the core consumption experience.

First of all what’s wrong with the consumption experience? In a nutshell, two problems:

  1. The signal-to-noise ratio of Twitter has declined dramatically as the service reached social saturation. There are simply too many messages from too many people in the stream — I call this “The Tragedy of the Comments” (yes, that was a pun). The issue is that since there is no economic disincentive to spam Twitter, nor any incentive for anyone to police or manage the common message space, it’s filling up with irrelevant / junk content and the amount of such content is growing rapidly. For users, it becomes very difficult to filter signal from all that noise. Users simply give up when faced with that level of overload. Why spend a lot of effort searching for needles in the haystack, when there are other ways (other apps, sites, etc.) to get a higher ratio of needles?
  2. The participation-to-reward ratio of Twitter has also declined dramatically with saturation. Because of the poor signal-to-noise ratio, the likelihood that anyone will see or respond to a Tweet you post, if you are not a major celebrity or media outlet, has declined far below the threshold of incentive. Why bother to Tweet when there is only a negligible chance anyone will see it or respond? The reward for participation has fallen too low for the majority of the user base to spend the effort.

There are several relatively easy steps Twitter can take to solve both of these problems, with better metadata and analytics in their apps. Making these changes would increase the usability of Twitter for most users and could radically improve Twitter’s metrics.

The Next-Generation Twitter App

I’m going to start this article with a concept for a next-gen Twitter app that makes it easier for consumers to track their interests in Twitter than ever before.

This app is designed around some additional metadata and filtering capabilities that I propose Twitter should set as priorities.

Here’s the basic concept of the app, and then there’s an illustration below.

This app should make it super easy to find or create new subscriptions for interests.

If you only want holiday recipes from top financial analysts, or video news about sports posted by major sports outlets, you can easily define these interests and start getting highly filtered relevant content about them, with a way to sort it by “signal” versus “noise.”

Users could be enabled to create and share their own interest subscription definitions, just like they share Twitter lists. Twitter could then provide a directory of public subscriptions, ranked by number of subscribers to each one, and this thing would take off.

A useful design pattern for this app comes from the world of RSS readers, where there was a lot of work done to come up with effective interfaces for keeping up with many different feeds of news and content. A simple 3-pane browser design pattern works best.

And of course the app could have other tabs for featured content theaters from Twitter and big paying sponsors — for example tabs could appear for any subscription interest that Twitter or a sponsor wants to promote. Users could then subscribe to the ones they want and they would be added to their interests on the left.

Here’s a simple schematic:

Now to make an app like this possible, Twitter needs to add richer metadata and semantics to Tweets. That’s the key to solving the signal-to-noise problem. Adding this additional semantic richness to the underlying data makes it possible to filter Tweets.

The best user-interface for doing this should be very “newsreader-like,” because in fact Twitter’s content is really very similar to news — it’s a stream of short updates and headlines that often point to payloads accessible via links.

Simple Semantic Tags

Currently all Tweets are just one type of message, a “status.” But what if users could simply add some tags to their Tweets, for type of message, just like they have been able to do in Flickr for example.

Flickr added the concept of “machine tags” – also called “triple tags” or “semantic tags” many years ago. Semantic tags are an elegant way to allow users to grow their own taxonomies of tag types in a totally bottom-up manner.

Simply allowing Twitter users to put their own semantic tags on Tweets would go a long way to making Twitter more useable, and would open up many huge possibilities.

For example, there could immediately be several basic tags that users could put on their Tweets such as:

  • Twitter:Type=News
  • Twitter:Type=Humor
  • Twitter:Type=Advertisement
  • Twitter:Type=Recipe
  • Twitter:Type=Playlist
  • Twitter:Type=Video
  • Twitter:Type=RFP
  • Twitter:Type=For Rent:Apartment
  • Twitter:Type=Event
  • Twitter:Type=Event:Live Event:Product Launch

Tags for types could also be added in the same way:

  • Twitter:Topic=Sports
  • Twitter:Topic=Sports:Football:NFL
  • Twitter:Topic=Business
  • Twitter:Topic=TV:Drama
  • Twitter:Topic=Place:USA:New York:New York City
  • Twitter:Topic=Entertainment

An even more powerful implementation would also support URIs in hashtags, which would enable more powerful formally defined and disambiguated semantics on Tweets, for example:

  • Twitter:Type=http://www.heppnetz.de/ontologies/goodrelations/v1#BusinessEntity

To make this even better, Tweets should supply additional space for tags in the metadata part of each Tweet — perhaps a lot more space – so that adding more metadata to Tweets (which improves the signal-to-noise of the whole Twitter ecosystem) is not disincentivized by the 140 character limit.

Twitter could make all this non-geeky by simply putting a dropdown menu to add a type and one or more topics on a Tweet. More advanced users could add their own additional semantic tags to their hearts’ content.

If these tags existed on Tweets, it would then be possible to filter them in some very useful ways. For example, you could filter for just News, or just Sports News, or just Product Offers for Software, etc.

This would also enable the creation of much richer aggregations — including marketplaces and portals — that show particular types of Tweets for various topics.

These aggregations could be created by Twitter, but why not let anyone create them on Twitter’s site and in their apps, as well as via widgets in third-party apps? The more the better. They would all generate eyeballs and engagement for Twitter.

(Disclosure: My company Bottlenose has developed a way to automatically annotate Tweets with Type and Topic metadata, but we would love it if there was lots of community generated metadata on Tweets as well).

Filtering Tweets by Score

Not all Tweets are of equal value. The value of a Tweet has three components: (a) author value: the inherited value from the author, (b) audience value: the value to the audience, and (c) reader value: the value to a specific individual.

The author value of a Tweet is determined as a function of the influence score for the author. This can be determined by their follower count, or with a more sophisticated formula that measures the influence not only of the author but of the people who follow them, as well as the average level of response that the author’s Tweets generate.

The audience value of a Tweet is determined by the actual response from the author’s audience to the Tweet. How many responses were generated, in what timeframe, and what is the influence of the people who responded or shared it?

The reader value of a Tweet is determined by the relevance of a Tweet to the interests of a particular individual, as determined by their own Tweets in the past as well as any explicitly stated interests they share with Twitter (if it were possible to do do that).

Twitter should determine the cumulative value of a Tweet for each reader based on these three components of value. This would enable a smarter ranking of Tweets by a value score, and would also enable users to filter out Tweets below a certain value. The filter could be dynamic such that if a Tweet achieves a high enough value to beat a user-defined threshold it would appear.

Thus some users might only want to see Tweets that become high-value, whereas others who want to spot early trends before they get discovered by others could set a lower threshold. Ideally it should be possible to filter for a lower and upper bound to Tweet Value, in order to get any range of Tweets within the spectrum of value scores.

Note that users could also opt to turn on or off the personalization filter – they could see Tweets ranked by cumulative value of just Author Value and Audience Value, or they could add in the Author Value component to the filter to see a more personalized ranking.

Simple Semantic Streams

Once Tweets have metadata about their value, type and topic it becomes amazingly easy to filter them. This enables a much better Twitter consumption experience for consumers.

The above mentioned new metadata about value, type and topic would enable users to create streams that are defined by rules for value, type, topic, author, geography, and more. Most users won’t have time to define these however — so it should be possible for other users to create these rules and share them with groups or the public.

For example, a great stream rule for tracking “Tech Industry News” would be easy to define and share. This rule would pull Tweets of type News, with topic Technology, from particular authors. Note that this is much better than old-fashioned Twitter lists because in Twitter lists you had to see every Tweet by list members, where here there is much more focus (and therefore less noise).

Improving Two Big Ratios

Twitter already has a few built-in rewards for participation, such as potentially getting more followers, or getting likes. However, these are no longer as directly correlated with getting actual attention as before. The participation-to-reward ratio has fallen significantly and this means participation will naturally fall as well. This creates a vicious cycle into ever worsening participation.

The real currency that Twitter brokers is attention, yet by not controlling their signal-to-noise Twitter has actually lost attention to its core product. By losing attention, Twitter literally loses currency. It is attention, after all, that Twitter sells to its advertisers.

Fixing the participation-to-reward ratio depends on first fixing the signal-to-noise ratio. Certainly more gamification features could be added, but unless signal-to-noise is solved first, they won’t solve the root problem.

Twitter has to create more currency by attracting more attention and turning that into engagement. To do that they have to make it more worthwhile to spend valuable attention in Twitter’s offerings.

Unless the way that consumers interact with Twitter is made more efficient and rewarding, simply adding more bells and whistles or focusing on live events won’t help.

By enriching the underlying metadata in Twitter, and improving ease-of-use for tracking interests and filtering in Twitter, as well as by starting to apply automated filtering for users, the proper ratio of signal-to-noise can be restored.

Accessing Twitter Beyond Twitter

The final step is to make sure that consumption of Twitter doesn’t only happen through Twitter apps, as long as standards are adhered to.

Twitter should provide an unlimited public API again, where anyone and any app can get the last few thousand Tweets for any query and reuse them for free — as long as they also show Twitter ads, which would appear randomly in the stream at a ratio of 1 ad per 30 Tweets.

Those who don’t want to display the ads could buy out the ad space from Twitter and could put their own ads there or show no ads at all.

Twitter makes money either way, and they get a much broader surface area as a result.

One more thing — Tweets via the API would all be Twitter Cards and would contain web bugs that would report views back to Twitter and drop cookies anywhere they were displayed, enabling deep audience analytics even outside of Twitter.This would make Twitter’s retargeting and audience reach vastly larger than it is today.

Suddenly there would be literally millions of outlets for Twitter, all either showing Twitter’s ads or paying for the ad space, and all reporting their activity back to Twitter.

Note also that the full commercial firehose would of course still be available via Gnip, for those who need it (such as analytics companies like my own, Bottlenose), and the firehose would not have the same advertising requirements as the public API — in other words the firehose would have no paid ad slots in it because customers are buying the firehose data. However, the additional metadata proposed above would also dramatically increase the value of the firehose data to firehose data customers, making it far more filterable.

Conclusions

The simple semantic additions suggested above are simple to implement and would go a long way to improving Twitter engagement. They would also open up vast new markets and use-cases for Twitter data. I hope the time is finally ripe for Twitter to add some of these ideas to their roadmap. The incredible potential of Twitter has hardly been tapped. The best is yet to come.

Why Twitter’s Engagement Has Fallen

I have been thinking about Twitter for many years. One of the interesting trends that many of us who share an interest in social networks have been tracking is the decline in engagement on Twitter.

Indeed this decline is not only evident from Twitter’s own metrics and reporting, but also to anyone who has been an active user of Twitter since the early days of the service.

Response Per Message Has Declined

In my own experience, Twitter used to be very different than it is today. It used to be more like an online community. In the early days of Twitter, when I Tweeted, I would get a lot of retweets, replies, and new followers.

Today, however, when I Tweet, I notice that the number of retweets, replies and new followers per message is much less than in the early days. It’s not that my content has changed (it really hasn’t), it’s that the way people use Twitter has changed.

Twitter’s Social Design Doesn’t Scale

In thinking about why this change has occurred, I have concluded that it is mainly a symptom of Twitter’s success in acquiring users. The social network design that underlies Twitter does not scale to a large audience.

The user-interface and interaction design of the Twitter Web site and the Twitter mobile app are also, for the most part, the same as they were in the early days. But the way people use Twitter, and the volume of content on Twitter, have outgrown these paradigms.

Social Tech Engagement

As Twitter has scaled over the years, each user has gradually followed more people on average. This has lead to social graph saturation — there is a huge amount of social overlap in the graph, meaning that people are more likely to get the same Tweet or news item, many times, from multiple people they follow. This leads to a lot of noise and redundancy in the content stream.

Social Graph Overlap Makes Discovery Harder

In addition, more automated bots and content sites have begun to post more content to Twitter per unit of time. The frequency of posting has increased. There is more content per unit time than before, and this continues to grow. But this is not necessarily good. More content also means more information overload.

These trends have collided, leading to a situation where the average daily number of messages that a typical Twitter user receives in their home timeline has grown dramatically.

Because of this growth in timeline message volume Twitter has become virtually unusable for discovery. Nobody can possibly keep up with all the messages in their home timelines (even in an hour, let alone a day).

A Less Efficient Publishing Channel

Secondly, Twitter has become less and less effective for publishing — at least if you want attention to what you post on Twitter. The probability that anyone will see or engage with anything you publish on Twitter seems to have declined dramatically (and this probability falls off very fast to zero, in a matter under an hour it seems).

The problem is that the follower graph in Twitter has reached a saturation point where it is almost irrelevant — following people has no benefit over not following them — the information overload in either case is just overwhelming.

Filter Failure Leads to Social Overload

When you follow hundreds to thousands of people and outlets, you get too many Tweets. It’s too irrelevant. It moves too fast. It’s simply unmanageable. There is no filter on the firehose anymore. The only solution is just to ignore it all. And that is what most people seem to be doing.

The filter used to be who we chose to follow – but that is no longer effective. Because even within that set there is just too much content coming into home timelines. As a result, in my own case at least, I almost never look at my home timeline in Twitter anymore.

Solutions Waiting in the Wings

Of course there are various ways Twitter could try to solve this. They range from for example, automatically ranking and prioritizing Tweets based on popularity, or how relevant and interesting they might be to me, or on some other metric (like how much someone paid for my attention etc.).

Any or all of these solutions in combination could help improve timeline signal-to-noise in Twitter. But so far I haven’t seen anything that solves it come about.

“Fire and Forget” Behaviors

Meanwhile, because of the declining signal-to-noise ratio, most people are using Twitter in a new mode. Whereas in the early days it was truly a conversational medium where people really paid attention to people they followed and engaged in dialogue with them, today it is more of a “fire-and-forget” medium where people simply post things into the aether, hoping that someone will see and at least Retweet them (which happens less and less).

Why is this happening? In a crowded room where everyone is shouting, the only way to be heard, even when you are talking to just a few people, is to shout even louder. And that is what I see happening on Twitter. More people and more publishers, posting more stuff, more often, in order to hopefully get noticed.

This is a self-amplifying feedback loop that results in total information overload eventually. If Twitter doesn’t solve it, their engagement will continue to fall. And at least if Twitter relies on advertising dollars from eyeballs, this is a serious problem for their business model.

The Third Party App Gap

Unfortunately, the decision made years ago, to stop all third-party innovation around the Twitter public API and eliminate third-party Twitter client apps, has made this situation worse, not better.

Although closing down the third-party Twitter app ecosystem gave Twitter more control over the advertising dollars on their content, it eliminated many apps and services that were actually helping to filter and personalize Twitter content. Ironically those same apps that were eliminated, were actually helping to sustain and grow higher engagement on Twitter.

Twitter has yet to fill this gap with their own apps and services — none of which currently solve the engagement and signal-to-noise problem effectively. But the potential is there.

It’s a bit of mystery to me at least why Twitter has not made solving this their top priority. There has been relatively little innovation or improvement to their core apps and services for many years now. Meanwhile Twitter has been acquiring companies that have little relevance to solving this problem.

Twitter Pivots From The Inside Out

It appears to me that Twitter may have shifted their strategy — they may have given up on improving internal engagement, and begun to just accept that they are more of a fire-and-forget medium going forward. In that reality, monetization strategies shift from internal to external opportunities.

For example, even if people no longer engage at all with each other inside of Twitter, the fact that they post teaches Twitter a lot about their interests, and this can be used to sell re-targeting and advertising intelligence outside of Twitter. In short, Twitter is sitting on an incredibly valuable personalization graph that they can monetize outside of Twitter.

It’s Not a Social Network, It’s An Ad Network!

Another way to monetize the content in Twitter, without increasing engagement, is to sell ads on manually or automatically curated subsets of the content, outside of Twitter. And we see this happening with Twitter’s recent move to enable their ads to run on their content in third-party sites.

But We Still Need a Social Network…

I continue to hope that Twitter will solve this with their own apps — with a new consumer experience designed for the reality of their much larger audience and super-saturated follower graph. I truly believe the world really needs Twitter, or something like it.

However, as the startup economy continues to show us, if Twitter does not solve it, someone and something else surely will.

It’s Time for an Open Standard for Cards

Cards are fast becoming the hot new design paradigm for mobile apps, but their importance goes far beyond mobile. Cards are modular, bite-sized content containers designed for easy consumption and interaction on small screens, but they are also a new metaphor for user-interaction that is spreading across all manner of other apps and content.

The concept of cards emerged from the stream — the short content notifications layer of the Internet — which has been evolving since the early days of RSS, Atom and social media.

Read the rest on TechCrunch

The Next Step for Intelligent Virtual Assistants

When we talk about the future of artificial intelligence (AI), the discussion often focuses on the advancements and capabilities of the technology, or even the risks and opportunities inherent in the potential cultural implications. What we frequently overlook, however, is the future of AI as a business.

IBM Watson’s recent acquisition and deployment of Cognea signals an important shift in the AI and intelligent virtual assistant (IVA) market, and offers an indication of both of the potentials of AI as a business and the areas where the market still needs development.

The AI business is about to be transformed by consolidation. Consolidation carries real risks, but it is generally a sign of technological maturation. And it’s about time, as AI is no longer simply a side project, or an R&D euphemism. AI is finally center stage.

Read the rest on GigaOm

Bottlenose Nerve Center 2.0 Released – Milestone for Real-Time Big Data Analytics

I’m happy to announce the release of Bottlenose Nerve Center 2.0 today. Analyzing 3 billion messages an hour (72 billion messages a day), and doing real-time predictive analytics on nearly 300 million data points an hour, it’s a big step in real-time big data analytics.  

Think about it for a moment: 3 billion messages is several times more data volume than the entire daily Twitter firehose, and we’re analyzing this much data every single hour, continuously. For one thing this level of real-time big data analytics cannot be done today with Hadoop — Hadoop is actually not capable of doing huge data aggregations this fast — so we’re using new technologies, like ElasticSearch (our team actually contributes to the ElasticSearch codebase) and Cassandra under the hood. We’re now analyzing in under a second what would take about an hour with Hadoop.

It’s an impressive technical accomplishment and I’m very proud of the incredibly talented engineering team that built this. I just want to give a shout out to my amazing co-founder Dominiek ter Heide and our product and engineering team for their work. This level of real-time analytics is truly game-changing.

Read more about the release here, see screenshots here, and check out the coverage about this release in TechCrunch!

 

 

How Bitcoins Could Restructure the World

Read my article in VentureBeat about how Bitcoins may restructure our civilization, and the need for advocacy to support this transition, if it is going to happen. Here’s an excerpt:

Bitcoin is a trend with all the ingredients necessary for changing the world. It spreads virally, funds its own growth, and can’t be controlled from any central point. Like the Web, it could eat the world.

Bitcoin could be the beginning of a massive transfer not only of wealth, but of power — a shift to a new social order. If you change the money system, you change the economy; that in turn changes society, government and industry. The shift to Bitcoins would be more than an economic shift, it would be a shift to a new social order — one built around a “freer market” economy.

Such a shift would be a lot more likely if a new grassroots organization were formed to accelerate, promote and protect the emerging cryptocurrency economy. By helping the cryptocurrency economy to fund its own evolution and defense, it would have a better chance of surviving the inevitable challenges it will soon face.

As this new digital economy emerges, the mysterious Bitcoin creator, Satoshi Nakamoto, could turn out to be one of the most important historical figures of our time.

Did Apple Buy Topsy for Contextual Awareness?

The stunning news that Apple bought social search engine, Topsy, for more than $200M has many scratching their heads. Why would Apple want social data, and why would they pay so much for it?

There has been a lot of speculation about the reasons for this acquisition — ranging from making Siri better, to making the App Store smarter, to acquiring big data expertise to develop insights on the Apple firehose.

But I think the reason may be something else altogether: Personalization.

If you want to build the next-generation of smart personalized mobile apps and services, you need a way to know what your users are interested in.

Well, what about analyzing everything they’ve ever Tweeted? That’s a pretty good shortcut.

I know from experience how well this approach can work. Analyzing consumer Tweets provides a surprisingly good window into the interests, relationships and affinities of consumers and brands.

(Disclosure: My company, Bottlenose, which focuses on trend intelligence from social firehose data, has done quite a bit of work on deriving user interest profiles from Twitter timelines, and among our 28 pending patents we have a number of applications around this.)

The Topsy deal is not about search. If Apple wanted to build a social search engine, then only searching Twitter history would not be a winning strategy. To win at social search a service has to be real-time, and should encompass many other social outlets — not just Twitter.

Topsy is much more focused on historical data than the present moment, and they only cover Twitter data. The Twitter-centrism and historical focus of Topsy are weaknesses, unless social search is not really the goal.

Topsy’s real strength is that they have indexed every Tweet in Twitter’s history. The only other known company that has done that is Twitter. This history is a goldmine for personalization. This is what I think Apple really bought.

The next frontier for personalization is contextual awareness. Mobile apps that know more about their present context, and the user, can provide even more relevant and timely suggestions.

But contextual awareness is not merely about knowing the device’s position. It’s about knowing everything about what a user is doing, who they are, and what they might think or want next.

The goal of contextual awareness is to create apps that understand not only where you are located and what you are looking at, but also what you are doing, why you are doing it, who and what is nearby, what your goals are, and what you are likely to think and feel about every person, building, device, object, app, product, advertisement, or bit of information you may encounter.

Google is already ahead of Apple in the contextual awareness game with Google Now and Google Glass. Facebook and Twitter both have huge advantages over Apple in this area as well because of all the data they have, and their large mobile footprints.

If Apple wants to compete in this arena, it needs to shore up it’s own apps and services with a way to understand each user’s present context and interests, and the history of what they have said about every location, product and piece of content they have encountered.

From Siri, to iTunes to the App Store, to Apple Maps to Apple’s alleged next-gen search, augmented reality and TV initiatives — context is king.

With Topsy’s historical Twitter data Apple can not only to make Siri smarter, it can power a whole new generation of smarter contextually-aware Apple apps and services. Apps that listen to, watch, and learn from, what users say and do.

Now all of this assumes that Twitter will just sit back and let Apple beat them to the contextual awareness Holy Grail on their own data. But will they? Apple and Twitter have a close relationship already. But could this new move by Apple change the tone of that relationship? It might. I would be willing to bet Twitter is starting to feel a wee bit claustrophobic right about now.

Twitter cannot tie itself only to Apple; Android matters too. Plus Twitter wants to control its own consumer experiences, and they need contextual awareness too. If Apple leapfrogs Twitter on the contextual awareness front, how long will Twitter continue to supply their full firehose to Apple’s Topsy team?

Twitter certainly has to walk an increasingly fine line with Apple, their $500B market cap frenemy.

Is Twitter’s Business Model Going to Work?

Twitter’s business model appears to have shifted from being a network to being a destination. The question I address here, is whether this shift in strategy is going to work, and what the implications are.

Twitter’s Declining User Engagement: Can it be Solved?

There are two primary ways that consumers engage with Twitter:

  1. Posting to Twitter.
  2. Consuming Twitter timeline content.

We are only concerned with (2) — because that’s where ads are displayed in Twitter today.

The success of Twitter’s strategy depends on whether Twitter can build apps that really engage users consistently, and that grow timeline engagement.

The Twitter timeline is where ads are shown: it’s where the rubber meets the road for Twitter’s current strategy.

The evidence is only anecdotal at this point, but so far it appears that timeline engagement in Twitter’s timeline is in trouble.

According to Twitter’s own reports, timeline views per user are in decline.

This is an all-important metric of engagement and is a key metric to track in order to project whether Twitter’s app’s will succeed in capturing user attention.

If this metric continues to fall, Twitter’s ad business is going to be a tough road.

TWITTER_PRE-IPO_REPORT_FINAL_v1.2.pdf__page_4_of_16_

At the same time as the decline in timeline engagement, the US user growth rate for Twitter is also saturating, causing growth rates to decline.

Twitter_MAUs-2

For more on these metrics, see this article that details them further.

Assuming that total user growth is nearing the saturation point for Twitter, the company has to focus on mining its existing user base for more revenue.

To accomplish this, with their current strategy, they have to find ways to increase revenue per timeline view.

Only if Twitter succeeds in finding a way to capture more timeline attention from each consumer will it really grow and monetize its advertising network in the long-term.

But so far that’s not happening. In fact, anecdotally almost everyone I speak to reports they consume less timeline content on Twitter than they did a year ago, and this is reflected in Twitter’s macro engagement metrics.

Twitter’s UX Challenge

The underlying problem is that Twitter’s user-experience has drifted away from the original elegance and simplicity that made it popular.

Twitter used to be a fast way to get a succinct list of headlines from everyone you trust.

But in the Twitter UI/UX of today, it takes a lot of mental energy — and eyestrain — to plow through the timeline for the needles in the haystack.

Getting news efficiently out of Twitter apps today takes a lot of effort — you have to read through a lot of noise — like cards, embedded videos and photos, conversation threads, sponsored posts, and ads.

Due to large surface area of cards and expanded posts, there are fewer messages above the fold, meaning you have to scroll a lot more.

Scrolling takes more work and reduces engagement with content even further.

Ironically, the company that was able to hold an entirely irrational hard-line on the 140 character limit has not been able to hold nearly as firm a line on keeping their UI/UX simple.

In the last year we’ve witnessed increasing complexity in Twitter’s official app UI. It went from simple, understated and elegant to bloated and overbearing and confusing. It went from being Twitter to being more like Facebook.

That’s fine if Twitter is trying to emulate Facebook. But my understanding was always that Twitter was trying to Twitter, not Facebook.

Twitter was supposed to be the place to get short and easily consumable bursts of news from the world.

Part of the equation that made Twitter work was a simple UX that made getting news really fast and efficient: timelines comprised of simple short textual headlines were efficient to read and consume. Twitter has strayed far from that ideal today.

If Twitter can find a way to grow timeline attention per user, then advertising within Twitter could become a true sustainable growth business for Twitter long-term, and Twitter could maintain tight control of their channel. But that’s not looking likely right now.

If on the other hand, attention per user in Twitter continues to decline this means that advertising inside of Twitter will become an increasingly tough business proposition for Twitter and for Twitter advertisers.

Twitter will either need to steadily lower advertising prices and stuff their timelines with even more ads, or take some other drastic action in order to push their ads to consumers and make sure they get attention. That’s a race to the bottom.

How Can Twitter Win?

What happens if Twitter cannot grow timeline attention? Is there another way they could still win?

Yes! Twitter could still win, but it would require a massive shift in orientation.

Twitter could actually make more money from ads that Twitter runs outside of Twitter than inside it.

To do this Twitter would have to shift to becoming a retargeting network. Twitter would have to focus on monetizing their audience outside of Twitter apps and the official Twitter.com site.

Retargeting is already widely used online, notably by Facebook.

Twitter is sitting on a wealth of rich user profiles that could be used to target ads to their users on any sites and apps that use Twitter to OATH visitors.

Interestingly, there are tens of billions of impressions outside of Twitter across around a million third-party sites. Twitter is not monetizing these yet.

Furthermore, if Twitter reversed their ban on third-party consumer apps making use of Twitter data, and built out a retargeting network across them all, they could probably double or triple this number of outside impressions, and ad impressions, fairly rapidly.

There is a hidden opportunity here for Twitter to monetize as a network rather than as an app.

Of course this would be a bit of a reversal from Twitter’s previous position that monetizing Twitter outside of official Twitter apps is against the rules of the road.

If Twitter becomes a retargeting network then they will have to open the door to third-party apps again to fully tap the potential of this strategy.

How might Twitter amplify this even more?

  1. Make all their data free (with some rules) to third-party apps, sites and services to re-use.
  2. Require that third-party developers and service providers NOT modify Twitter content or insert their own ads.
  3. Require that third-party developers and service providers MUST carry only ads that come with it from Twitter.
  4. Twitter would also share revenues on any Twitter-provided ads with third-party apps that deliver impressions on those ads.
  5. Twitter’s ad network could use retargeting via Twitter profile data to provide highly personalized ads, generating better yields.

This is a killer strategy for Twitter and I’m willing to bet that someday they will return to it when they discover that monetizing only traffic inside Twitter isn’t going to generate enough growth.

Twitter-as-Network vs. Twitter-as-App

If Twitter was a network instead of an app, then in every ecological niche — every market niche — some third party developer or Webmaster would be able to figure out how to create a particular user experience that is best-optimized to squeeze out more engagement and attention per user within that niche.

That would enable Twitter to monetize the long-tail of attention. Twitter cold not possibly monetize all these opportunities with a generic solution as well as thousands of developers working, and competing, in parallel.

But instead of harnessing the power of the masses to optimize the long-tail, Twitter is making a bet they can monetize the entire channel better by themselves.

But is that even the best bet to make? The future value of owning the entire channel from end-to-end and controlling everything is not necessarily greater than the long-tail value of all the niches that Twitter has blocked by transforming from an open to a closed ecosystem.

The long-tail of Twitter advertising opportunities is worth more than owning just the head of the tail. It’s probably a better bet, if you just look at the statistics. It’s simply more likely to work in the long-run than betting that Twitter can build a generic solution that will succeed at being all things to all people.

A Better Endgame for Twitter

Twitter could in fact own both the head of the tail and the long tail opportunity. That’s the best of both worlds.

Twitter could have a strong portal and set of official apps PLUS an open content and advertising network that encourages third-party apps to drive even more attention to Twitter ads.

In this scenario, Twitter could encourage third-party consumer apps to compete to generate the most engagement and ad sales on Twitter data, in the new Twitter network.

They could even amplify that with a promise of cherry-picking the best performers of those and blessing them with massive traffic from Twitter’s central hub and maybe even investment funds or M&A for the best of them.

This would be the Twitter ecosystem 2.0, with new rules, and a built-in monetization system for everyone to partake in.

By re-opening the public APIs and even actively investing in third-party Twitter apps again, they could be been in a position to harvest outside innovation for the best new companies and ideas.

Thousands, or even millions, of apps and services generating timeline and ad impressions for Twitter is definitely a better way to grow their ad business than going it alone.

It’s not too late for Twitter to evolve their strategy around this issue, and if they don’t it’s pretty likely that Twitter advertising is going to be a tough sell in the long-run.

 

The Future of Virtual Assistants

Check out my article in Forbes on the future of Virtual Assistants. What’s after SIRI and IVR?

All of the interesting stuff happens when data collides. A voice-based interface to a single data set is a thing of the past. A voice-based interface that talks to over 400 applications, representing over 10,000 unique units of knowledge across over 3,000 discrete products? Now we’re talking.

Creating the next generation of assistance is in fact a data federation problem. It’s a brain problem. A data routing problem. A big data problem, even.

Bottlenose Series A to Bring “Trendfluence” to the Enterprise

Bottlenose Secures $3.6 Million Series A Round of Financing to Bring Trendfluence™ to the Enterprise

BusinessWire

Enterprise Offering Discovers Real-Time, Influential Trends to Drive Marketing Campaigns, Manage Brand Reputation and Spotlight Attention in Social Communities

LOS ANGELES, July 23, 2013 — Bottlenose, the first application for Trendfluence™ discovery in social streams, today announced that the company has completed a $3.6 million Series A round of venture capital financing.

The round was led by ff Venture Capital, with participation from Lerer Ventures, Transmedia, Advancit, as well as other leading funds and angel investors. The Series A financing will fund new hires in engineering, sales and marketing to scale operations for the formal entry of Bottlenose into the enterprise market this autumn.

“We are excited to have the opportunity to lead the A round, as we believe that Nova and the Bottlenose team are building a truly compelling and disruptive business”, said John Frankel, Partner, ff Venture Capital.  “After all, we traditionally partner with companies that are changing the way people behave, and we look forward to supporting Bottlenose with all of our internal resources as the team continues to flourish and thrive.”

An early, free alpha version of Bottlenose, released in 2012, spurred interest and demand from nearly 100,000  professional marketers seeking real-time solutions for mapping trends in social networks, in a way that allowed them to see through the fog of social media. Early enterprise partners helped shape Bottlenose for enterprise use, resulting in today’s robust system for revealing Trendfluence in firehose levels of data.

Several brands and agencies—including Pepsi, FleishmanHillard, Razorfish, and DigitasLBi — leverage Bottlenose Enterprise for tracking live and emerging trends and events, directing advertising and marketing initiatives, engaging customer communities and gathering industry intelligence.

The New Science of Trendfluence™ Makes Social Listening Actionable

Bottlenose has developed a new technology for isolating Trendfluence from the noise of social streams. Trendfluence enables Bottlenose customers to proactively identify, anticipate and instigate the trends that drive their businesses.

Bottlenose applies big data cloud computing and analytics to continuously data-mine streams from social networks and enterprise data sources, to detect, visualize and monitor trends as they develop and move in real-time. As trends take shape in real-time, Bottlenose applies proprietary natural language and statistical techniques (16 pending patents) to calculate and visualize the live attention and sentiment around them.

With hundreds of millions of messages, topics, people and links analyzed to-date, and billions more being added on an ongoing basis, Bottlenose is constantly sensing the unfolding live conversation across major social networks, isolating the topics, people, issues, and content that have gathering speed, influence and shove.

Queries Return a 360 Degree View of Vital Trends Reflecting the Emotion of Your Market

The ability to detect real-time trends enables marketers to understand the emotional energy of the crowd and how that is affecting their businesses and brands, right now. It also helps enterprises discover and monitor the “unknown unknowns” on the horizon that may grow into threats, issues, or opportunities – up to hours, days, or even weeks before they are noticed by others.

Bottlenose customers gain an unprecedented ability to find and focus on the trends that matter, as or before they materialize, to inform their real-time tactics and strategies.

Major brand Fortune 500 customers are using Bottlenose to:

  • Detect emerging threats and opportunities

  • Inform advertising keyword buying strategies

  • Direct real-time content creation and curation

  • Visualize and track activity around live events

  • Monitor and predict brand health and crisis management outcomes

  • Conduct real-time market and opinion research

  • Extract customer insights and competitive intelligence

  • Cross-correlate social activity with business outcomes like stock prices, engagement, and sales

“We are thankful to have the support of forward-thinking investors and enterprise customers who share our vision and understand the growing importance of real-time discovery analytics applied to massive data streams. We’ve seen significant traction from Fortune 500’s since the enterprise version went beta in January, both in volume of inbound, and deal size.” said Nova Spivack, CEO and cofounder of Bottlenose. “Social networks have created an environment where rumors, breaking news stories, and customer sentiment can spike and spread globally in minutes. Big brands are now in an arms race to proactively detect and respond to these emerging issues in real-time, instead of after the fact.”

Previously available as a free, trial application, Bottlenose is in limited release on a subscription basis to enterprise customers. General Availability of Bottlenose is slated for autumn.

For inquires related to sales, case studies, or product offerings please visit: http://bottlenose.com/pro.

About Bottlenose:

Bottlenose is the first application for Trendfluence discovery in social and business data streams. Bottlenose provides an enterprise-grade dashboard for discovering, monitoring and acting on influential trends, beginning with social media communications affecting brands.

Bottlenose was founded in 2010 by serial entrepreneur, Nova Spivack, and Web technologist, Dominiek ter Heide. Bottlenose has offices in Los Angeles, California, New York City, and Amsterdam, the Netherlands.

Learn more about Bottlenose here: http://bottlenose.com/.

About ff Venture Capital:

ff Venture Capital is an institutional venture capital investor in seed-stage companies. Since 1999, our Partners have made over 180 investments in over 72 companies. Our exits include Cornerstone OnDemand (IPO, CSOD) and Quigo Technologies (sold to AOL for a reported $340m). ffVC has twenty employees based in New York and New Jersey and extensive resources dedicated to portfolio acceleration, including strategy consulting, recruiting assistance, in-house accounting services, communications and PR strategy, engineering assistance, a pool of preferred service providers and an executive portfolio community.

To learn more about ff Venture Capital, visit: http://www.ffvc.com

 

The Present IS the Future: Real-Time Marketing In the Era of the Stream – Part Two

In Part I of this article series, we looked at how the real-time Web has precipitated Nowism as a fundamental shift in how we understand and engage with information. Nowism is a cultural shift to a focus on the present, instead of the past or future.

One example of Nowism in action is Nowcasting, which attempts to make sense of the present in real-time, before all the data has been analyzed, in order to project trends sooner or even continuously. Nowcasting is quickly becoming a necessary and powerful function in our media, culture and society.

These ideas are being harnessed by savvy brands and companies not only in how they operate, but also in how they conceive of themselves.

Next we will look how they impact social marketing, and why brands must learn to act like media companies in this new environment.

The Three Stages of Real-Time Marketing Evolution

The Stream is more real-time than the Web. And it’s even more real-time than blogging and the early days of social networking. But it’s not only faster, it’s also orders of magnitude bigger. Instead of millions Web pages every month, we’re dealing with billions of messages every day.

There’s vastly more activity, more change, more noise, and when trends happen they are more contagious and spread more quickly. It’s therefore even more important to sense and respond to change in the present, right when it happens.

Unlike the Web, the Stream is constantly changing, everywhere, on the second timescale: It is a massively parallel real-time medium. And instead of a few channels there are billions of channels — at least one (if not many) for each person, brand, organization and media outlet on the Net — and they are all flowing with messages and data.

Keeping up with the deluge of real-time conversation across so many channels at once is a huge challenge, but making sense of so much change in real-time is even harder. Yet, even harder still is intelligently engaging with the Stream in real-time.

These three objectives represent three levels of maturity and mastery for real-time marketers.



Stage One: Week Marketing.

Today, most brands and agencies are still stuck trying to accomplish Stage One, if they are even that far along.

Stage One social marketers are focused on simply monitoring the Stream and trying to keep up with the conversations about their brand.

Some Stage One marketers are also actively trying to drive perception and optimize engagement through social media. But their ability to measure the effects of their actions and optimize their engagement are primitive at best.

The timescale of their measurement and engagement with the stream can range from hours to days, or even to the weekly timescale.

Stage Two: Day Marketing.

A smaller set of organizations have learned how to make sense of the Stream in real-time and are operating on the hour to daily timescale. They have graduated to Stage Two.

Stage Two marketers don’t merely monitor and respond, they digest and interpret. They measure and engage in sense-making and trend discovery. They generate live insights from millions of messages and incorporate these insights into their thinking and behavior on an hourly to daily basis.

Stage Two social marketers have evolved past the stage of simple reflexive response to the stage where they can interpret and reason about the Stream intelligently in near-real-time. They leverage social analytics, data mining, and visualization tools to facilitate insight, and this leads to smarter behavior, more optimal engagement and better results.

But Stage Two organization response times are still not real-time. Instead of seconds or minutes their responses often take hours or even days and that’s not fast enough anymore.

Stage Three: Now Marketing.

Stage Three marketing organizations are incredibly rare today. But there will be more of them soon.

Stage Three’s are able to monitor, make sense of, and then engage intelligently with the Stream, all in real-time, not after-the-fact.

In other words, they are consistently able to detect, measure, analyze, reason, and respond to signals in the Stream within seconds or minutes, or at most within the hour.

Stage Three organizations continuously run a real-time marketing feedback loop that works like this:

  1. Sense. First a signal is sensed: it might be a breaking story rumor, a complaint by an influencer, a change in customer perception or audience sentiment, a shift in engagement levels, a crisis, or a sudden new trend or opportunity. Sensing signals, and differentiating important signals from noise, in real-time, requires new approaches to determining relevance, timeliness, importance that don’t rely on analyzing historical data. There is no time for that in the present. Sensing has to intelligently filter signal from noise by recognizing the signs of potentially interesting trends, regardless of their content. Organizations that can do this well are able to detect emerging trends early in their life cycles, giving them powerful time advantages.

  2. Analyze. Next, the signal is analyzed in real-time to understand what drives it, and what it drives. Its underlying causes, influencers, effects, demographics, time dynamics and relationships to other entities and signals are mined, measured, visualized and interpreted. This requires new live social discovery and analytics capabilities – what’s new here is that this isn’t merely classical social analytics (measuring follower count or message volumes, or charting historical volume), it’s massive “big data” mining and discovery in real-time. It’s live prospecting around all the potentially relevant signals that are connected to each signal to get the context.

  3. Respond. Then an intelligent response (itself a signal) is generated across one or more channels within seconds to minutes. For example a reply or an offer may be sent to a customer, an alert may be sent to a team, a new piece of content may be synthesized and published, the targeting for an ad campaign may be adjusted, or the tone of social messaging may be modified, prices may be adjusted, and even policies may be changed – all in real-time. Organizations that get good at this process are able to respond so quickly they cross the threshold from being reactive to being proactive. They are able to drive the direction of trends by being the first to detect, analyze and respond to them.

This feedback loop is exemplified in real-time social advertising targeting and campaign optimization, for example. But as organizations mature to Stage Three they must learn to apply this same methodology to ALL their interactions with the Stream, not just advertising. They must apply this feedback loop across ALL their engagement with customers, the media and the marketplace.

Within a decade, all leading brands will be stage three marketing organizations.

Mapping The Ripple Effect

“Stage Three” agencies and brands need to master ripple effects to thrive in the next generation social environment.

Ripple effects are the key forces in the emerging real-time social Web. Information propagates through ripple effects along social relationships, across channels, communities, and media. Ripple effects are how trends emerge and rise, how rumors spread, and how ads and content are distributed. But we’re currently almost completely blind to ripple effects, we have almost no way to detect, measure or predict them.

The average Facebook user has 190 friends. The average Twitter user has 208 followers. Each group contains a number of influencers. Within each influencer’s social graph there are another set of even more powerful influencers. And so on and so on. When you seed a branded message on Facebook, for example, it’s not a straight trajectory. A ripple starts with the above numbers, but each new impression creates a new set of ripples.

Suddenly, your branded message is being seeded across a number of platforms — even spreading to platforms you never intended — and 99% of brands and agencies have no way of mapping this ripple effect, let alone controlling it.

But what if you could track your ripple effects? What if you could guide them? What if you could measure their effectiveness, or even predict where they are going? Suddenly there would be a wealth of new insights to pull and learn from. And this is precisely what is now possible, using emerging tools.

Problems with Existing Tools

Stage Three marketing organizations have to keep up with ripple effects in real-time, and they have to anticipate where those ripple effects are headed, in order to react immediately.

But most social analytics and engagement tools fail to show ripple effects. They provide loads of raw data — lists of messages from various social accounts and searches. But they expect humans to do most of the work of actually figuring out what’s important in those lists of messages. That is no longer realistic. Humans can’t cope with the data — it’s overwhelming.

Furthermore, most existing social analytics tools focus on simply measuring engagement via follower counts, mentions, likes, Retweets, favorites, impressions, click-throughs, and basic sentiment. But those metrics are no longer sufficient: They aren’t the trends, they are just signals that may or may not be relevant to actual trends. Not all signals are trends. The art is in finding a way to pull out the actual trends from the rest of the signals that are not in fact trends of any value.

What existing tools fail to do is actually make sense of what’s going on for you — they show you either too little or too much information, but they fail to show you what’s actually important; they’re not smart enough to figure that out for you.

Existing tools are good at finding known topics and trends (“known unknowns”) things you explicitly ask to know about in advance — but what we need in the era of the Stream are tools that show you what you don’t even know to ask for (“unknown unknowns.”) They have to detect novelty, outliers, anomalies, the unexpected — and they have to do this automatically, without being instructed on how to find these nuggets.

Existing social media analytics tools are too retrospective in nature – they show how a brand performed on social channels from the past up to the moment a question is asked. But these reports are static. They don’t show change happening, they don’t say anything about what’s next. The minute they are generated they become obsolete. It’s interesting to look at past performance, but what is really needed is more predictive analytics.

Trendcasting

We need a new generation of tools that are designed for identifying real-time ripple effects and filtering them to figure out which ones are noise and which are actual trends we should pay attention to. Better yet, we need tools that can not only identify the trends, but that can project where they are headed in real-time. Think of this as the next evolution of Nowcasting.

We might call this Trendcasting. Where Nowcasting figures out what’s happening now in real-time, Trendcasting figures out what’s happening next in real-time. 

No human can Trendcast in real-time anymore without help from software, the Stream has too much volume and velocity for the human mind to comprehend or process on its own. This is a problem that can only be solved by cloud computing against big data in real-time.

In the today’s real-time Stream, marketers cannot afford to be hours or days behind the curve. They need to know and understand the present in the present, while it is still unfolding. They need tools for Trendcasting – for finding and predicting trends. Trends are not merely raw data, they are particularly meaningful and noteworthy trajectories in the data.

Trendcasting is going to be come absolutely key. The next-generation real-time marketing platforms will provide automated trendcasting as a key feature. They will sift through the noise, find the signal, and then measure it to see if it actually matters. Trendcasting is about filtering for the trends that actually matter, because not all signal is important and not all trends are equal.

Traditionally, finding and forecasting trends has always been thought of as an exclusively human skill — but today we’re starting to automate this function. I believe Trendcasting can be fully automated, or at least dramatically improved, using massively parallel big data analytics approaches. This is the where the cutting-edge of innovation for real-time marketing will focus for the next decade. (Disclosure: My own company, Bottlenose, is focused on exactly this goal, for Fortune 500 brands).

Trendcasting tools are the next leap in a long process of measurement tools innovation that has included inventions like telescopes, microscopes, X-rays, weather satellites, MRIs, and search engines. In a sense trendcasting engines could be thought of as automated cultural measurement tools — the social equivalent of a weather satellite —  social satellites. They help us to visualize, understand and project the weather of markets, cultures, industries, communities, brands and their audiences, just like satellites have helped us understand and map the weather patterns of our planet.

Every Brand is a Media Company

The shift to real-time and the advent of the Stream changes how brands must think of themselves.

Whether they are ready or not, all brands have to learn to function more like media companies – and in particular like news networks – in order to remain competitive in the era of live social media.

For the first decade of social media the emphasis was clearly on social, but now it is shifting to media. Leading brands have learned how to be social for the most part. Now they have to learn how to act like media companies.

Consider a network like CNN: They have reporters all over the world, constantly giving them text, images, video, opinion, insights and leads. They have viewers, some of whom are also contributing news tips and stories, and opinions, all over the world across many platforms and channels.

CNN’s bread and butter is finding breaking stories first, getting the best information about them, and covering them most comprehensively and creating original news content and analysis for their audience.

CNN is a good model for what every Stage 3 brand has to learn to do in order to master Now Marketing.

Brands that want to lead in the Stream era have to gather intelligence constantly, using social media. They have to create content, share it, and engage. They have to keep their fingers on the pulse of their markets and culture in general in order to remain relevant and timely. They have to respond to a huge influx of questions, opinions, complaints, suggestions, leads. And they have to do this across many platforms and channels at once, in real-time.

The distinction between content provider and audience is dissolving. It’s now a two-way live conversation with the market, a conversation among equals. Brands have to learn to share, interact, make friends, and socialize just like people do. They have to not only create content for their audiences, they have to use their audiences as the content. And they have to do it on a massive scale.

Some brands – like Nike and Red Bull – have gone very far down this path and even think of themselves as media companies to some degree. But for most brands thinking like a media company is still a completely new orientation and set of skills.

Brands need new tools in order to think and operate like media companies. They can’t work on the weekly or monthly timescale anymore. Even daily timescales are too slow: they have to go live.

They can’t just market to their customers, they have to engage them in marketing the brand and creating media, together. They can’t just analyze key metrics anymore, they have to understand the trends that are emerging, and what’s driving change.

The Stream is here, and it’s happening in real-time. Marketers who can adapt to this shift early will be the leaders of tomorrow; Brands that are late in adopting these practices risk become nothing but historical data points.

 

Twitter is No Longer a Village

I’ve noticed a distinct change in how people use Twitter in the last year:

1. People are increasingly not using Twitter for actual two-way conversations or interactions. Instead it’s being used more for one-way “fire and forget” posting. People just post into the aether, without knowing or even caring if anyone actually reads their posts.

2. People are spending less time reading Twitter messages, they are paying less attention to what other people say. This is because it’s too difficult to keep up with what your friends are up to on Twitter: we all follow too many people now, and there are just too many messages flowing by all the time.

These two shifts are going to fundamentally change what Twitter is for, and how it is used. It is gradually becoming less of a social network where people interact, and more of a place to simply express opinions.

Maybe in a way this is a return to the original intent of Twitter — a place where you could post what you were doing. That was originally a one-way activity. However soon after those early days a community formed and Twitter became conversational and highly interactive for a while. Until it got so big that it lost that village feeling.

Twitter used to be a village — it was in fact the epicenter of the global village for a while. But now it has become a gigantic industrialized urban sprawl. A megacity. It’s lost that feeling of intimacy and community it once had.

Today Twitter is a mass market backchannel for consumers to express themselves to businesses and media providers, and for businesses to market to their audiences. It is also a place where people express themselves around live events like sports games, television shows and breaking news.

But while people and businesses are increasingly expressing themselves on Twitter, they are actually doing less listening to each other there.

Listening is on the decline because the message volumes on Twitter are now so high that it just is impossible to keep up. There are too many messages flowing by all the time. It’s information overload. There’s no point in even trying to pay attention to what people you follow are saying.

Of course people still pay attention to replies, mentions and Retweets of them — at least if they are not famous. Famous people get far too many mentions from strangers and so they usually just ignore them as well.

I’m willing to bet that you aren’t paying attention to Twitter. Your friends aren’t either. At least not like in years past.

So who is listening to Twitter if it’s not all of us? Businesses. They are listening, analyzing, and using this data to gauge perception, market and advertise. This is where the real value of Twitter seems to be headed: It’s a channel for people to express themselves around products, brands, events and content. And it’s a tool for businesses to learn about their audiences and market to them in real-time. Twitter is becoming our global backchannel.

As a side-effect of these shifts, Twitter is feeling less social every day. It’s no longer a place where people listen or pay attention to one another anymore. It’s certainly not a place where people have conversations beyond the occasional reply. Instead, it’s more like a giant stadium where everyone is shouting at the same time.

This probably means that as a publishing and messaging channel Twitter will become less effective over time.

As message volumes keep growing, what are the chances that your audience will be looking at the exact second that your message is actually visible above the fold, before it is buried by 1000 new Tweets? The chances are getting lower every day. And nobody scrolls down to look at older messages anymore. Why look back through the past when there are so many new Tweets arriving in the present?

This means that the likelihood of your intended audience seeing anything you post to Twitter is headed towards zero.

Unless of course, you’re famous. If you’re famous you can post once and get a thousands of Retweets and that might get your post noticed. But for most of us, and even most brands, most of their posts are going to be missed. They are like shots in the dark.

If you’re not famous you can still get noticed however. If you are willing to pay. You can buy visibility for your Tweets by making them into Promoted Tweets. But ads are different than conversation. And a network where people have to advertise to each other to be heard would not feel social at all.

Should this be fixed? I’m willing to bet that Twitter will probably not put much effort in reducing noise, or adding really good personalization, precisely because such measures would compete with Promoted Tweets. Promoted Tweets make money precisely because there is increasing noise in Twitter, just like Google Ads make money because Google is not as relevant as it could be.

These trends throw into question the value of posting anything to Twitter today, at least if your goal is to reach your followers organically and get attention. That is just increasingly unlikely.

If you really want to reach people on Twitter, the best bet will be to advertise there.

But advertise to whom? If attention to Twitter is declining because people are posting more but reading less, that would reduce attention to Twitter ads as well.

Ironically it’s the noise on Twitter that creates a need for Twitter ads, but it’s that same noise that will ultimately cause people to not pay attention to Twitter anymore. And if people pay less attention to Twitter’s content, there will be less of an audience for Twitter’s ads. It’s just too much work to find the needles you care about in all that hay.

The noise problem on Twitter is a side-effect of mass adoption. But it’s also a side-effect of a growing mismatch between how Twitter was designed as a product and the size of audience, and message volumes, it now supports today. Twitter was not designed for this level of audience or activity, and it shows. Twitter was designed to be village, but it’s now a megacity.

It will be interesting to see how Twitter evolves to meet this challenge. Can they restore the balance by creating ways for consumers to filter the noise? Can they attract more attention and content consumption?

My theory is that Twitter may inevitably focus more on advertising outside of Twitter, than inside, perhaps by using a retargeting approach on sites that use Twitter OATH to register their users. Here’s how this could work:

  1. Twitter can potentially see the interests of anyone who posts content to Twitter.
  2. When any member of Twitter uses their Twitter credentials to login to any site that uses Twitter OATH as a login (including Twitter.com), Twitter can place a cookie in their browser.
  3. Then any site that uses Twitter OATH can detect that user and associate them with their interest profile from Twitter.
  4. With this knowledge any site in the Twitter network can target ads to Twitter users’ personalized interests when they get visits from those users.

This technique is already being applied by one company, LocalResponse. I wonder when Twitter will start doing it themselves. If they do this, Twitter can become an ad network that uses what people talk about inside of Twitter, to target ads to them outside of Twitter.

Ultimately this may solve the attention problem in Twitter. Don’t even bother getting people to pay attention to content inside of Twitter. Just get them to talk about their interests and then target ads to them when they pay attention to content outside of Twitter. This “retargeting” approach is working well for Facebook and it’s only a matter of time until Twitter does it. Of course I’m sure Facebook has applied for a patent on this idea by now and that will also add a wrinkle to how this plays out in the future.

 

 

 

 

 

Making Sense of Streams

This is a talk I’ve been giving on how we filter the Stream at Bottlenose.

You can view the slides below, or click here to replay the webinar with my talk.

Note: I recommend the webinar if you have time, as I go into a lot more detail than is in the slides – in particular some thoughts about the Global Brain, mapping collective consciousness, and what the future of social media is really all about.  My talk starts at 05:38:00 in the recording.

 

Bottlenose Beat Bit.ly to the First Attention Engine – But It’s Going to Get Interesting

Bottlenose (disclosure: my startup) just launched the first attention engine this week.

But it appears that Bit.ly is launching one soon as well.

It’s going to get interesting to watch this category develop. Clearly there is new interest in building a good real-time picture of what’s happening, and what’s trending, and providing search, discovery, and insights around that.

I believe Bottlenose has the most sophisticated map of attention today, and we have very deep intellectual property across 8 pending patents and a very advanced technology stack behind it as well. And we have some pretty compelling user-experiences on top of it all. So in short, we have a lead here on many levels. (Read more about that here)

But that might not even matter because I think ultimately Bit.ly will be a potential partner for Bottlenose, rather than a long-term competitor — at least if they stay true to their roots and DNA as a data provider rather than a user-experience provider. I doubt that Bit.ly will succed in making a search destination that consumers will use and I’m guessing that is not really their goal.

In testing their Realtime service, my impression is that it feels more like a Web 1.0 search engine. Static search results for advanced search style queries. I don’t see that as a consumer experience.

Bottlenose on the other hand, goes way into a consumer UX, with live photos, newspapers, topic portals, a dashboard, etc. It is also a more dynamic, always changing, realtime content consumption destination. Bottlenose feels like media, not merely search (in fact I think search, news and analytics are actually converging in the social network era).

Bottlenose has a huge emphasis on discovery, analytics, and other further actions on the content that go beyond just search.

I think in the end Bit.ly’s Realtime site will really demonstrate the power of their data, which will still mainly be consumed via their API rather than in their own destination. I’m hopeful that Bit.ly will do just that. It would be useful to everyone, including Bottlenose.

The Threat to Third-Party URL Shorteners

If I were Bit.ly, my primary fear today would be Twitter with their t.co shortener. That is a big threat to Bit.ly and will probably result in Bit.ly losing a lot of their data input over time as more Tweets have t.co links on them than Bit.ly links.

Perhaps Bit.ly is attempting to pivot their business to the user experience side in advance of such a threat potentially reducing their data set and thus the value of their API. But without their data set I don’t see where they can get the data to measure the present. So as a pivot it would not work – where would they get the data?

In other words, if people are not using as many Bit.ly links in the future, Bit.ly will see less attention. And trends point to this happening in fact — Twitter has their own shortener. So does Facebook. So does Google. Third-party shorteners will probably represent a decreasing share of messages and attention over time.

I think the core challenge for Bit.ly is to find a reason for their short URLs to be used instead of native app short URLs. Can they add more value to them somehow? Could they perhaps build in monetization opportunities for parties who use their shortener, for example? Or could they provide better analytics than Twitter or Facebook or Google will on short URL uptake (Bit.ly arguably does, today).

Bottlenose and Bit.ly Realtime: Compared and Contrasted

In any case there are a few similarities between what Bit.ly may be launching and what Bottlenose provides today.

But there are far more differences.

These products only partially intersect. Most of what Bottlenose does has no equivalent in Bit.ly Realtime. Similarly much of what Bit.ly actually does (outside of their Realtime experiment) is different from what Bottlenose does.

It is also worht mentioning that Bit.ly’s “Realtime” app is a Bit.ly “labs” project and is not their central focus, whereas at Bottlenose it is 100% of what we do. Mapping the present is our core focus.

There is also a big difference in business model. Bottelnose does map the present in high-fidelity, but has no plans currently to provide a competing shortening API, or an API about shortURLs, like Bit.ly presently does. So currently we are not competitors.

Also, where Bit.ly currently has a broader and larger data set, Bottlenose has created a more cutting-edge and compelling user-experience and has spent more time on a new kind of computing architecture as well.

The Bottlenose StreamOS engine is worth mentioning here: Bottlenose has new engine for real-time big data analytics engine that uses a massively distributed and patent pending “crowd computing” architecture.

We actually have buit what I think is the most advanced engine and architecture on the planet for mapping attention in real-time today.

The deep semantics and analytics we compute in realtime are very expensive to compute centrally. Rather than compute everything in the center we compute everywhere; everyone who uses Bottlenose helps us to map the present.

Our StreamOS engine is in fact a small (just a few megabytes) Javascript and HTML5 app (the size of a photo) that runs in the browser or device of each user. Almost all the computing and analytics that Bottlenose does happens in the browser at the edge.

We have very low centralized costs. This approach scales better, faster, and more cheaply than any centralized approach can. The crowd literally IS our computer. It’s the Holy Grail of distributed real-time indexing.

We also see a broader set of data than Bit.ly does. We don’t only see content that has a bit.ly URL on it. We see all kinds of messages moving through social media — with other shortURls, and even without URLs.

We see Bit.ly URLs, but we also see data that is outside of the Bit.ly universe. I think ultimately it’s more valuable to see all the trends across all data sources, and even content that contains no URLs at all (Bottlenose analyzes all kinds of messages for example, not just messages that contain URLs, let alone just Bit.ly URLs).

Finally, the use-cases for Bottlenose go far beyond just search, or just news reading and news discovery.

We have all kinds of  brands and enterprises actually using our Bottlenose Dashboard product, for example, for social listening, analytics and discovery. I don’t see Bit.ly going as deeply into that as us.

For these reasons I’m optimistic that Bottlenose (and everyone else) will benefit from what Bit.ly may be launching — particularly via their API, if they make their attention data available as an additional signal.

This space is going to get interesting fast.

(To learn more about what Bottlenose does, read this)

 

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence

This article is part of a series of articles about the Bottlenose Public Beta launch.

Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence (you are here)

A New Window Into the Collective Consciousness

Bottlenose offers a new window into what the world is paying attention to right now, globally and locally.

We show you a live streaming view of what the crowd is thinking, sharing and talking about. We bring you trends, as they happen. That means the photos, videos and messages that matter most. That means suggested reading, and visualizations that cut through the clutter.

The center of online attention and gravity has shifted from the Web to social networks like Twitter, Facebook and Google+. Bottlenose operates across all them, in one place, and provides an integrated view of what’s happening.

The media also attempts to provide a reflection of what’s happening in the world, but the media is slow, and it’s not always objective. Bottlenose doesn’t replace the media — at least not the role of the writer. But it might do a better job of editing or curating in some cases, because it objectively measures the crowd — we don’t decide what to feature, we don’t decide what leads. The crowd does.

Other services in the past, like Digg for example, have helped pioneer this approach. But we’ve taken it further — in Digg people had to manually vote. In Bottlenose we simply measure what people say, and what they share, on public social networks.

Bottlenose is the best tool for people who want to be in the know, and the first to know. Bottlenose brings a new awareness of what’s trending online, and in the world, and how those trends impact us all.

We’ve made the Bottlenose home page into a simple Google-like query field, and nothing more. Results pages drop you into the app itself for further exploration and filtration. Except you don’t just get a long list of results, the way you get on Google.

Instead, you get an at-a-glance start page, a full-fledged newspaper, a beautiful photo gallery, a lean-back home theater, a visual map of the surrounding terrain, a police scanner, and Sonar — an off-road vehicle so that you can drive around and see what’s trending in networks as you please. We’ve made the conversation visual.

Each of these individual experiences is an app on top of the Bottlenose StreamOS platform, and each is a unique way of looking at sets and subsets of streams. You can switch between views effortlessly, and you can save anything for persistent use.

Discovery, we’ve found from user behavior, has been the entry point and the connective tissue for the rest of the Bottlenose experience all along. Our users have been asking for a better discovery experience, just as Twitter users have been asking for the same.

The new stuff you’ll see today has been one of the most difficult pieces for us to build computer-science-wise. It is a true technical achievement by our engineering team.

In many ways it’s also what we’ve been working towards all along. We’re really close now to the vision we held for Bottlenose at the very beginning, and the product we knew we’d achieve over time.

The Theory Behind It: How to Build a Smarter Global Brain

If Twitter, Facebook, Google+ and other social networks are the conduits for what the planet is thinking, then Bottlenose is a map of what the planet is actually paying attention to right now. Our mission is to “organize the world’s attention.” And ultimately I think by doing this we can help make the world a smarter place. At at the end of the day that’s what gets me excited in life.

After many years of thinking about this, I’ve come to the conclusion that the key to higher levels of collective intelligence is not making each person smarter, and it’s not some kind of Queen Bee machine up in the sky that tells us all what to do and runs the human hive. It’s not some fancy kind of groupware either. And it’s not the total loss of individuality into a Borg-like collective either.

I think that better collective intelligence really comes down to enabling better collective consciousness. The more conscious we can be of who we are collectively, and what we think, and what we are doing, the smarter we can actually be together, of our own free will, as individuals. This is a bottom-up approach to collective consciousness.

So how might we make this happen?

For the moment, let’s not try to figure out what consciousness really is, because we don’t know, and we probably never will, but regardless, for this adventure, we don’t need to. And we don’t even need to synthesize it either.

Collective consciousness is not a new form of consciousness, rather, it’s a new way to channel the consciousness that’s already there — in us. All we need to do is find a better way to organize it… or rather, to enable it to self-organize emergently.

What does consciousness actually do anyway?

Consciousness senses the internal and external world, and maintains a model of what it finds — a model of the state of the internal and external world that also contains a very rich model of “self” within it.

This self construct has an identity, thoughts, beliefs, emotions, feelings, goals, priorities, and a focus of attention.

If you look for it, it turns out there isn’t actually anything there you can find except information — the “self” is really just a complex information construct.

This “self” is not really who we are, it’s just a construct, a thought really — and it’s not consciousness either. Whatever is aware is aware of the self, so the self is just a construct like any other object of thought.

So given that this “self” is a conceptual object, not some mystical thing that we can’t ever understand, we should be able to model it, and make something that simulates it. And in fact we can.

We can already do this for artificially intelligent computer programs and robots in a primitive way in fact.

But what’s really interesting to me is that we can also do it for large groups of people too. This is a big paradigm shift – a leap. Something revolutionary really. If we can do it.

But how could we provide something like a self for groups, or for the planet as a whole? What would it be like?

Actually, there is already a pretty good proxy for this and it’s been around for a long time. It’s the media.

The Media is a Mirror

The media senses who we are and what we’re doing and it builds a representation — a mirror – in the form of reports, photos, articles, and stats about the state of the world. The media reflects who we are back to us. Or at least it reflects who it thinks we are…

It turns out it’s not a very accurate mirror. But since we don’t have anything better, most of us believe what we see in the media and internalize it as truth.

Even if we try not to, it’s just impossible to avoid the media that bombards us from everywhere all the time. Nobody is really separate from this, we’re all kind of stewing a media soup, whether we like it or not.

And when we look at the media and we see stories – stories about the world, about people we know, people we don’t know, places we live in, and other places, and events — we can’t help but absorb them. We don’t have first hand knowledge of those things, and so we take on faith what the media shows us.

We form our own internal stories that correspond to the stories we see in the media. And then, based on all these stories, we form beliefs about the world, ourselves and other people – and then those beliefs shape our behavior.

And there’s the rub. If the media gives us an inaccurate picture of reality, or a partially accurate one, and then we internalize it, it then conditions our actions. And so our actions are based on incomplete or incorrect information. How can we make good decisions if we don’t have good information to base them on?

The media used to be about objective reporting, and there are still those in the business who continue that tradition. But real journalists — the kind who would literally give their lives for the truth — are fewer and fewer. The noble art of journalism is falling prey, like everything else, to commercial interests.

There are still lots of great journalists and editors, but there are fewer and fewer great media companies. And fewer rules and standards too. To compete in today’s media mix it seems they have to stoop to the level of the lowest common denominator and there’s always a new low to achieve when you take that path.

Because the media is driven by profit, stories that get eyeballs get prioritized, and the less sensational but often more statistically representative stories don’t get written, or don’t make it onto the front page. There is even a saying in the TV news biz that “If it bleeds, it leads.”

Look at the news — it’s just filled with horrors. But that’s not an accurate depiction of the world. For example crimes don’t happen all the time, everywhere, to everyone – they are statistically quite unlikely and rare — yet so much news is devoted to crimes for example. It’s not an accurate portrayal of what’s really happening for most people, most of the time.

I’m not saying the news shouldn’t report crime, or show scary bad things. I’m just pointing out that the news is increasingly about sensationalism, fear, doubt, uncertainty, violence, hatred, crime, and that is not the whole truth. But it sells.

The problem is not that these things are reported — I am not advocating for censorship in any way. The problem is about the media game, and the profit motives that drive it. Media companies just have to compete to survive, and that means they have to play hard ball and get dirty.

Unfortunately the result is that the media shows us stories that do not really reflect the world we live in, or who we are, or what we think, accurately – these stories increasingly reflect the extremes, not the enormous middle of the bell curve.

But since the media functions as our de facto collective consciousness, and it’s filled with these images and stories, we cannot help but absorb them and believe them, and become like them.

But what if we could provide a new form of media, a more accurate reflection of the world, of who we are and what we are doing and thinking? A more democratic process, where anyone could participate and report on what they see.

What if in this new form of media ALL the stories are there, not just some of them, and they compete for attention on a level playing field?

And what if all the stories can compete and spread on their merits, not because some professional editor, or publisher, or advertiser says they should or should not be published?

Yes this is possible.

It’s happening now.

It’s social media in fact.

But for social media to really do a better job than the mainstream media, we need a way to organize and reflect it back to people at a higher level.

That’s where curation comes in. But manual curation is just not scalable to the vast number of messages flowing through social networks. It has to be automated, yet not lose its human element.

That’s what Bottlenose is doing, essentially.

Making a Better Mirror

To provide a better form of collective consciousness, you need a measurement system that can measure and reflect what people are REALLY thinking about and paying attention to in real-time.

It has to take a big data approach – it has to be about measurement. Let the opinions come from the people, not editors.

This new media has to be as free of bias as possible. It should simply measure and reflect collective attention. It should report the sentiment that is actually there, in people’s messages and posts.

Before the Internet and social networks, this was just not possible. But today we can actually attempt it. And that is what we’re doing with Bottlenose.

But this is just a first step. We’re dipping our toe in the water here. What we’re doing with Bottlenose today is only the beginning of this process. And I think it will look primitive compared to what we may evolve in years to come. Still it’s a start.

You can call this approach mass-scale social media listening and analytics, or trend detection, or social search and discovery. But it’s also a new form of media, or rather a new form of curating the media and reflecting the world back to people.

Bottlenose measures what the crowd is thinking, reading, looking at, feeling and doing in real-time, and coalesces what’s happening across social networks into a living map of the collective consciousness that anyone can understand. It’s a living map of the global brain.

Bottlenose wants to be the closest you can get to the Now, to being in the zone, in the moment. The Now is where everything actually happens. It’s the most important time period in fact. And our civilization is increasingly now-centric, for better or for worse.

Web search feels too much like research. It’s about the past, not the present. You’re looking for something lost, or old, or already finished — fleeting.  Web search only finds Web pages, and the Web is slow… it takes time to make pages, and time for them to be found by search engines.

On the other hand, discovery in Bottlenose is about the present — it’s not research, it’s discovery. It’s not about memory, it’s about consciousness.

It’s more like media — a live, flowing view of what the world is actually paying attention to now, around any topic.

Collective intelligence is theoretically made more possible by real-time protocols like Twitter. But in practice, keeping up with existing social networks has become a chore, and not drowning is a real concern. Raw data is not consciousness. It’s noise. And that’s why we so often feel overwhelmed by social media, instead of emboldened by it.

But what if you could flip the signal-to-noise ratio? What if social media could be more like actual media … meaning it would be more digestible, curated, organized, consumable?

What if you could have an experience that is built on following your intuition, and living this large-scale world to the fullest?

What if this could make groups smarter as they get larger, instead of dumber?

Why does group IQ so often seem inversely proportional to group size? The larger groups get, the dumber and more dysfunctional they become. This has been a fundamental obstacle for humanity for millennia.

Why can’t groups (including communities, enterprises, even whole societies) get smarter as they get larger instead of dumber? Isn’t it time we evolve past this problem? Isn’t this really what the promise of the Internet and social media is all about? I think so.

And what if there was a form of media that could help you react faster, and smarter, to what is going on around you as it happens, just like in real life?

And what if it could even deliver on the compelling original vision of the cyberspace as a place you could see and travel through?

What about getting back to the visceral, the physical?

Consciousness is interpretive, dynamic, and self-reflective. Social media should be too.

This is the fundamental idea I have been working on in various ways for almost a decade. As I have written many times, the global brain is about to wake up and I want to help.

By giving the world a better self-representation of what it is paying attention to right now, we are trying to increase the clock rate and resolution of collective consciousness.

By making this reflection more accurate, richer, and faster, and then making it available to everyone, we may help catalyze the evolution of higher levels of collective intelligence.

All you really need is a better mirror. A mirror big enough for large groups of people to look into and see what they are collectively paying attention to in it, together. By providing groups with a clearer picture of their own state and activity, they can adapt to themselves more intelligently.

Everyone looks in the collective mirror and adjusts their own behavior independently — there is no top-down control — but you get emergent self-organizing intelligent collective behavior as a result. The system as a whole gets smarter. So the better the mirror, the smarter we become, individually and collectively.

If the mirror is really fast, really good, really high res, and really accurate and objective – it can give groups an extremely important, missing piece: Collective consciousness that everyone can share.

We need collective consciousness that exists outside of any one person, and outside of any one perspective or organization’s agenda, and is not merely just in the parts (the individuals) either. Instead, this new level of collective consciousness should be something that is coalesced into a new place, a new layer, where it exists independently of the parts.

It’s not merely the sum of the parts, it’s actually greater than the sum – it’s a new level, a new layer, with new information in it. It’s a new whole that transcends just the parts on their own.  That’s the big missing piece that will make this planet smarter, I think.

We need this yesterday. Why? Because in fact collectives — groups, communities, organizations, nations — are the units of change on this planet. Not individuals.

Collectives make decisions, and usually these decisions are sub-optimal. That’s dangerous. Most of the problems we’ve faced and continue to face as a species come down to large groups doing stupid things, mainly due not having accurate information about the world or themselves. This is, ultimately, an engineering problem.

We should fix this, if we can.

I believe that the Internet is an evolving planetary nervous system, and it’s here to to make us smarter. But it’s going to take time. Today it’s not very smart. But it’s evolving fast.

Higher layers of knowledge, and intelligence are emerging in this medium, like higher layers of the cerebral cortex, connecting everything together ever more intelligently.

And we want to help make it even smarter, even faster, by providing something that functions like self-consciousness to it.

Now I don’t claim that what we’re making with Bottlenose is the same as actual consciousness — real consciousness is, in my opinion a cosmic mystery like the origin of space and time. We’ll probably never understand it. I hope we never do. Because I want there to be mystery and wonder in life. I’m confident there always will be.

But I think we can enable something on a collective scale, that is at least similar, functionally, to the role of self-consciousness in the brain — something that reflects our own state back to us as a whole all the time.

After all, the brain is a massive collective of hundreds of billions of neurons and trillions of connections that themselves are not conscious or even intelligent – and yet it forms a collective self and reacts to itself intelligently.

And this feedback loop – and the quality of the reflection it is based on – is really the key to collective intelligence, in the brain, and for organizations and the planet.

Collective intelligence is an emergent phenomena, it’s not something to program or control. All you need to do to enable it and make it smarter, is give groups and communities better quality feedback about themselves. Then they get smarter on their own, simply by reacting to that feedback.

Collective intelligence and collective consciousness, are at the end of the day, a feedback loop. And we’re trying to make that feedback loop better.

Bottlenose is a new way to curate the media, a new form of media in which anyone can participate but the crowd is the editor. It’s truly social media.

This is an exciting idea to me. It’s what I think social media is for and how it could really help us.

Until now people have had only the mainstream, top-down, profit-driven media to look to. But by simply measuring everything that flows through social networks in real time, and reflecting a high-level view of that back to everyone, it’s possible to evolve a better form of media.

It’s time for a bottom-up, collectively written and curated form of media that more accurately and inclusively reflects us to ourselves.

Concluding Thoughts

I think Bottlenose has the potential to become the giant cultural mirror we need.

Instead of editors and media empires sourcing and deciding what leads, the crowd is the editor, the crowd is the camera crew, and the crowd decides what’s important. Bottlenose simply measures the crowd and reflects it back to itself.

When you look into this real-time cultural mirror that is Bottlenose, you can see what the community around any topic is actually paying attention to right now. And I believe that as we improve it, and if it becomes widely used, it could facilitate smarter collective intelligence on a broader scale.

The world now operates at a ferocious pace and search engines are not keeping up. We’re proud to be launching a truly present-tense experience. Social messages are the best indicators today of what’s actually important, on the Web, and in the world.

We hope to show you an endlessly interesting, live train of global thought. The first evolution of the Stream has run its course and now it’s time to start making sense of it on a higher level. It’s time to start making it smart.

With the new Bottlenose, you can see, and be a part of, the world’s collective mind in a new and smarter way. That is ultimately why Bottlenose is worth participating in.

Keep Reading

Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence (you are here)

 

Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

Recently, one of Twitter’s top search engineers tweeted that Twitter was set to “change search forever.” This proclamation sparked a hearty round of speculation and excitement about what was coming down the pipe for Twitter search.

The actual announcement featured the introduction of autocomplete and the ability to search within the subset of people on Twitter that you follow — both long-anticipated features.

However, while certainly a technical accomplishment (Twitter operates a huge scale and building these features must have been very difficult), this was an iterative improvement to search…an evolution, not a revolution.

Today I’m proud to announce something that I think could actually be revolutionary.

 

And here’s the video….

 

My CTO/Co-founder, Dominiek ter Heide, and I have been working for 2 years on an engine for making sense of social media. It’s called Bottlenose, and we started with a smart social dashboard.

Now we’re launching the second stage of our mission “to organize the world’s attention” — a new layer of Bottlenose that provides a live discovery portal for the social web.

This new service measures the collective consciousness in real-time and shows you what the crowd is actually paying attention to now, about any topic, person, brand, place, event… anything.

If the crowd is thinking about it, we see it. It’s a new way to see what’s important in the world, right now.

This discovery engine, combined with our existing dashboard, provides a comprehensive solution for discovering what’s happening, and then keeping up with it over time.

Together, these two tools not only help you stay current, they provide compelling and deep insights about real-time trends, influencers, and emerging conversations.

All of this goes into public beta today.

An Amazing Team

I am very proud of what we are launching today, in many ways — while still just a step on a longer journey — it is the culmination of an idea I’ve been working on, thinking about, dreaming of… for decades… and I’d love you to give it a spin.

And I’m proud of my amazing technical team — they are the most talented technical team I’ve ever worked with in my more than 20 years in this field.

I have never seen such a small team deliver so much, so well. And Bottlenose is them – it is their creation and their brilliance that has made this possible. I am really so thankful to be working with this crew.

Welcome to the Bottlenose Public Beta

So what is Bottlenose anyway?

It is a real-time view of what’s actually important across all the major social networks — the first of its kind — what you might call a “now engine.”

This new service is not about information retrieval. It’s about information awareness. It’s not search, it’s discovery.

We don’t index the past, we map the present. That’s why I think it’s better to call it a discovery engine than a search engine. Search implies research towards a specific desired answer, whereas discovery implies exploration and curiosity.

We measure what the crowd is paying attention to now, and we build a living, constantly learning and evolving, map of the present.

Twitter has always encouraged innovation around their data, and that innovation is really what has fueled their rapid growth and adoption. We’ve taken them at their word and innovated.

We think that what we have built adds tremendous value to the ecosystem and to Twitter.

But while Twitter data is certainly very important and high volume, Bottlenose is not just about Twitter… we integrate the other leading social networks too: Facebook, LinkedIn, Google+, YouTube, Flickr, and even networks whose data comes through them like Pinterest and Instagram. And we also see RSS too.

We provide a very broad view of what’s happening across the social web — a view that is not available anywhere else.

Bottlenose is what you’d build if you got the chance to start over and work on the problem from scratch — a new and comprehensive vision for how to make sense of what’s happening across and within social networks.

We think it could be for the social web what Google was for the Web. Ok that’s a bold statement – and perhaps it’s wishful thinking – but we’re at least off to a good start here and we’re pushing the envelope farther than it has ever been pushed. Try it!

Oh and one more thing, why the name? We chose it because dolphins are smart, they’re social, they hunt in pods, they have sonar. We chose the name as an homage to their bright and optimistic social intelligence. We felt it was a good metaphor for how we want to help people surf the Stream.

Thanks for reading this post, and thanks for your support. If you have a few moments to spare today, we’d love it if you gave Bottlenose a try. And remember, it’s still a beta.

Note: It’s Still a Beta!

Before I get too deep into the tech and all the possibilities and potential I see in Bottlenose, I first want to make it very clear that this is a BETA.

We’re still testing, tuning, adding stuff, fixing bugs, and most of all learning from our users.

There will be bugs and things to improve. We know. We’re listening. We’re on it. And we really appreciate your help and feedback as we continue to work on this.

Want to Know More?

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence

 

 

I Get 13,000 Messages/Day via Different Streams – Here’s the Analysis

Continuing with the theme I’ve been writing about lately, focused on the growth of the next phase of the Web, what I call “The Stream,” I’ve started to analyze the messages I get on a typical day.

First of all, through all the different channels I use, I now receive approximately 13,000 messages a day. I don’t think I am an extreme case. In fact, anyone who uses Twitter, Facebook, LinkedIn, Google+, email, RSS, and a few Web apps, is probably in the same boat.

Of these, email is no longer the largest stream, but it’s still the most important. However, of 112 email messages received on that day, 46 (41%) were “notifications” from Web apps and Web sites, and these were a lot less important than the remaining messages that were actual communication of one form or another.

The largest streams are Twitter, Facebook and LinkedIn. These streams are comprised of public messages posted by people and sources I follow. In these streams, based on a cursory analysis, it appears messages spool in at a rate that varies on the low side from 1 message every 2 minutes on average (LinkedIn) up to 2 messages per minute on average (Twitter for my personal account where I follow 525 people).

The volume of messages pouring through my social streams is impossible to keep up with. It’s becoming a personal firehose. So, like most people, I have no choice but to ignore 99.99% of them.

However, there are some needles in the haystack that I really would like to find. To solve for that, I use Bottlenose as my dashboard – it surfaces the social messages I really need to pay attention to. It helps me extract more value from my social streams. Fewer calories, more protein.  (Disclosure: I’m the CEO of Bottlenose).

I really need something to make sense of all the messages I’m getting. You probably do too. And in with the exponential growth of message volume across all streams, everyone’s going to need this within a year or two. Not just professionals and power-users, but even everyday consumers. In fact, many regular people are already overwhelmed.

Today, while we’re in beta testing, Bottlenose only pulls in Twitter and Facebook. However, where we’re heading is to include ALL the types of streams you see in this diagram above, making Bottlenose a truly “universal dashboard” for the era of the Stream.

I would be very curious to hear what your messaging looks like and if you’re seeing similar levels of overload.

Here’s the raw data for a typical day:

Stream Messages/day Percentage Notes
LinkedIn Direct Messages 1 0.01%
LinkedIn Connect Requests 2 0.01%
Facebook Private Messages 3 0.02%
Facebook Events Notifications 6 0.04%
Facebook Notifications 7 0.05%
Novaspivack Twitter Mentions 10 0.07%
Facebook suggested Events 15 0.11%
GitHub Notifications 42 0.31%
Yammer Messages 44 0.32%
Facebook Groups 58 0.42%
Email Messages 112 0.82%
Bottlenose Twitter Mentions 200 1.46%
RSS News Articles 350 2.55%
Google+ 720 5.24% .5/min*
LinkedIn Updates 720 5.24% .5/min*
Facebook News Feed 1440 10.49% 1/min*
Twitter @bottlenoseapp 2800 20.39% 2/min**
Twitter @novaspivack 7200 52.44% (5/min)**
Total  13,730
Notes
 *  Estimated average based on counting; does not include comments on messages
 ** Estimated average based on counting

Keeping Up With the Stream — New Problems and Solutions

This is Part III of a series of articles on the new era of the Stream, a new phase of the Web.

In Part I, The Message is the Medium, I explored the shift in focus on the Web from documents to messages.

In Part II, Drowning in the Stream, we dove deep into some of the key challenges the Stream brings with it.

Here in Part III, we will discuss new challenges and solutions for keeping up with streams as they become increasingly noisy and fast-moving.

 

Getting Attention in Streams

Today if you post a message to Twitter, you have a very small chance of that message getting attention. What’s the solution?

You can do social SEO and try to come up with better, more attention-grabbing, search engine attracting, headlines. You can try to schedule your posts to appear at optimal times of day. You can even try posting the same thing many times a day to increase the chances of it being seen.

This last tactic is called “Repeat Posting” and it’s soon going to be clogging up all our streams with duplicate messages. Why am I so sure this is going to happen? Because we are in an arms race for attention. In a room where everyone is talking, everyone starts talking louder, and soon everyone is shouting.

Today when  you post a message to Twitter, the chances of getting anyone’s attention are low and they are getting lower.  If you have a lot of followers, the chances are a little better that at least some of them may be looking at their stream at precisely the time you post. But still, even with a lot of followers, the odds are that most of your followers probably won’t be online at that precise moment you post something, and so they’ll miss it.

Scheduled Posting

But it turns out there are optimal times of day to post, when more of your followers are likely to be looking at their streams. A new category of apps, typified by Buffer, has emerged to help you schedule your Tweets to post at such optimal times.

Using apps like Buffer, you can get more attention to your Tweets, but this is only a temporary solution. Because the exponential growth of the Stream means that soon even posting a message at an optimal time will not be enough to get it in front of everyone who should see it.

Repeat Posting

To really get noticed, above the noise, you need your message to be available at more than one optimal time, for example many times a day, or even every hour.

To achieve this, instead of posting a message once at the optimal time per day, we may soon see utilities that automatically post the same message many times a day – maybe every hour – perhaps with slightly different wording of headlines, to increase the chances that people will see them. I call this “repeat posting” or “message rotation.”

Repeat posting tools may get so sophisticated that they will A/B test different headlines and wordings and times of day to see what gets the best clickthroughs and then optimize for those. These apps may even intelligently rotate a set of messages over several days, repeating them optimally until they squeeze out every drop of potential attention and traffic, much like ad servers and ad networks rotate ads today.

But here’s the thing — as soon as anyone starts manually or automatically using repeat posting tactics, it will create an arms race – others will notice it, and compete for attention by doing the same thing. Soon everyone will have to post repeatedly to simply get noticed above the noise of all the other repeat posts.

This is exactly what happens when you are speaking in a crowded room. In a room full of people who are talking at once, some people start talking louder. Soon everyone is shouting and losing their voice at the same time.

This problem of everyone shouting at once is what is soon going to happen on Twitter and Facebook and other social networks. It’s already happening in some cases – more people are posting the same message more than once a day to get it noticed.

It’s inevitable that repeat posting behavior will increase, and when everyone starts doing it, our channels will become totally clogged with redundancy and noise. They will become unusable.

What’s the solution to this problem?

What to Do About Repeat Posting

One thing that is not the solution is to somehow create rules against repeat posting. That won’t work.

Another solution that won’t work is to attempt to detect and de-dupe repeats that occur. It’s hard to do this, and easy to create repeat posts that have different text and different links, to evade detection.

Another solution might be to recognize that repeat posting is inevitable, but to make the process smarter: Whenever a repeat posting happens, delete the previous repeat post. So at any given time the message only appears once in the stream. At least this prevents people from seeing the same thing many times at once in a stream. But it still doesn’t solve the problem of people seeing messages come by that they’ve seen already.

A better solution is to create a new consumption experience for keeping up with streams, where relevant messages are actually surfaced to users, instead of simply falling below the fold and getting buried forever. This would help to ensure that people would see the messages that were intended for them, and that they really wanted to see.

If this worked well enough, there would be less reason to do scheduled posting, let alone repeat posting. You could post a message once, and there would be much better chance of it being seen by your audience.

At Bottlenose, we’re working on exactly this issue in a number of ways. First of all, the app computes rich semantic metadata for messages in streams automatically, which makes it possible to filter them in many ways.

Bottlenose also computes the relevance of every message to every user, which enables ranking and sorting by relevancy, and the app provides smart automated assistants that can help to find and suggest relevant messages to users.

We’re only at the beginning of this and these features are still in early beta, but already we’re seeing significant productivity gains.

Fast-Moving Streams

As message volume increases exponentially in streams, our streams are going to not just going to be noisier, they are going to move faster. When we look at any stream there will be more updates per minute – more new messages scrolling in – and this will further reduce the chances of any message getting noticed.

Streams will begin to update so often they will literally move all the time. But how do you read, let alone keep up with, something that’s always moving?

Today, if you follow a Twitter stream for a breaking news story, such as a natural disaster like the Tsunami in Japan, or the death of Steve Jobs, you can see messages scrolling in, in real-time every second.

In fact, when Steve Jobs died, Twitter hit a record peak of around 50,000 Tweets per minute. If you were following that topic on Twitter at that time, the number of new messages pouring was impossible to keep up with.

Twitter has put together a nice infographic showing the highest Tweets Per Second events of 2011.

During such breaking news events, if you are looking at a stream for the topic, there is not even time to read a message before it has scrolled below the fold and been replaced by a bunch of more recent messages. The stream moves too fast to even read it.

But this doesn’t just happen during breaking news events. If you simply follow a lot of people and news sources, you will see that you start getting a lot of new messages every few minutes.

In fact, the more people and news sources, saved searches, and lists that you follow, the higher the chances are that at any given moment there are going to be many new messages for you.

Even if you just follow a few hundred people, the chances are pretty high that you are getting a number of new messages in Twitter and Facebook every minute. That’s way more messages than you get in email.

And even if you don’t follow a lot of people and news sources – even if you diligently prune your network, unfollow people, and screen out streams you don’t want, the mere exponential growth of message volume in coming years is soon going to catch up with you. Your streams are going to start moving faster.

But are there any ways to make it easier to keep up with these “whitewater streams?”

Scrolling is Not the Answer

One option is to just make people scroll. Since the 1990’s UX designers have been debating the issue of scrolling. Scrolling works, but it doesn’t work well when the scrolling is endless, or nearly endless. The longer the page, the lower percentage of users will scroll all the way down.

This becomes especially problematic if users are asked to scroll in long pages – for example infinite streams of messages going back from the present to the past (like Twitter, above). The more messages in the stream, the less attention those messages that are lower in the stream, below the fold, will get.

But that’s just the beginning of the problem. When a stream is not only long, but it’s also moving and changing all the time, it becomes much less productive to scroll. As you scroll down new stuff is coming in above you, so then you have to scroll up again, and then down again. It’s very confusing.

In long streams that are also changing constantly it is likely that engagement statistics will be very different than for scrolling down static pages. I think it’s likely engagement will be much lower, the farther down such dynamic streams one goes.

Pausing the Scroll is Not the Answer

Some apps handle this problem of streams moving out from under you by pausing auto-scrolling as you read – they simply notify you that there are new messages above whatever you are looking at. You can then click to expand the stream above and see the new messages. Effectively they make dynamic streams behave as if they are not dynamic, until you are ready to see the updates.

This at least enables you to read without the stream moving out from under you. It’s less disorienting that way. But in fast moving streams where there are constantly new updates coming in, you have to click on the “new posts above” notification frequently, and it gets tedious.

For example, here is Twitter, on a search for Instagram, a while after the news of their acquisition by Facebook. After waiting only a few seconds, there are 20 new tweets already. If you click the bar that says “20 new Tweets” they expand. But by the time you’ve done that and started reading them, there are 20 more.

 

Simply clicking to read “20 new tweets” again and again is tedious. And furthermore, it doesn’t really help users cope with the overwhelming number of messages and change in busy streams.

The problem here is that streams are starting to move faster than we can read, even faster than we can click. How do you keep up with this kind of change?

Tickers and Slideshows Are Helpful

Another possible solution to the problem of keeping up with moving streams is to make the streams become like news tickers, constantly updating and crawling by as new stuff comes in. Instead of trying to hide the movement of the stream, make it into a feature.

Some friends and I have tested this idea out in an iPad app we built for this purpose called StreamGlider. You can download StreamGlider and try it out for yourself.

StreamGlider shows streams in several different ways — including a ticker mode and a slideshow mode where streams advance on their own as new messages arrive.

 

The Power of Visualization

Another approach to keeping up with fast moving streams is to use visualization, like we’re doing in Bottlenose, with our Sonar feature. By visualizing what is going on in a stream you can provide a user with instant understanding of what is in the stream and what is important and potentially interesting to them, without requiring them to scroll, skim or read everything first.

Sonar reads all the messages in any stream, applies natural language and semantic analysis to them, detects and measures emerging topics, and then visualizes them in realtime as the stream changes.

It shows you what is going on in the stream – in that pile of messages you don’t have time to scroll through and read. As more messages come in, Sonar updates in realtime to show you what’s new.

You can click on any trend in Sonar that interests you, to quickly zoom into just the messages that relate.

The beauty of this approach is that it avoids scrolling until you absolutely want to. Instead of scrolling, or even skimming the messages in a stream, you just look at Sonar and see if there are any trends you care about. If there are, you click to zoom in and see only those messages. It’s extremely effective and productive.

Sonar is just one of many visualizations that could help with keeping up with change in huge streams. But it’s also only one piece of the solution. Another key piece of the solution is finding things in streams.

Finding Things in Streams

Above, we discussed problems and solutions related to keeping up with streams that are full of noise and constantly changing. Now let’s discuss another set of problems and solutions related to finding things in streams.

Filtering the Stream

For a visualization like Sonar to be effective, you need the ability to filter the stream for the sources and messages you want, so there isn’t too much noise in the visualization. The ability to filter the stream for just those subsets of messages you actually care about is going to be absolutely essential in coming years.

Streams are going to become increasingly filled with noise. But another way to think about noisy streams is that they are really just lots of less-noisy streams multiplexed together.

What we need is a way to intelligently and automatically de-multiplex them back into their component sub-streams.

For example, take the stream of all the messages you receive from Twitter and Facebook combined. That’s probably a pretty noisy stream. It’s hard to read, hard to keep up with, and quickly becomes a drag.

In Bottlenose you can automatically de-multiplex your streams into a bunch of sub-streams that are easier to manage. You can then read these, or view them via Sonar, to see what’s going on at a glance.

For example, you can instantly create sub-streams – which are really just filters on your  stream of everything. You might make one for just messages by people you like, another for just messages by influencers, another for just news articles related to your interests, another for just messages that are trending, another of just photos and videos posted by your friends, etc.

The ability to filter streams – to mix them and then unmix them – is going to be an essential tool for working with streams.

Searching the Stream

In the first article in this series we saw how online attention and traffic is shifting from search to social. Social streams are quickly becoming key drivers for how content on the Web is found. But how are things found in social streams? It turns out existing search engines, like Google, are not well-suited for searching in streams.

Existing algorithms for Web search do not work well for Streams. For example, consider Google’s PageRank algorithm.

In order to rank the relevancy of Web pages, PageRank needs a very rich link structure. It needs a Web of pages with lots of links between the documents. The link structure is used to determine which pages are the best for various topics. Effectively links are like votes – when pages about a topic link to other pages about that topic, they are effectively voting for or endorsing those pages.

While PageRank may be ideal for figuring out what Web pages are best, it doesn’t help much for searching messages, because messages may have no links at all, or may be only very sparsely linked together. There isn’t enough data in individual messages to figure out much about them.

So how do you know if a given message is important? How do you figure out what messages in a stream actually matter?

When searching the stream, instead of finding everything, we need to NOT find the stuff we don’t want. We need to filter out the noise. And that requires new approaches to search. We’ve already discussed filtering above and the ability to filter streams is a per-requisite for searching them intelligently. Beyond that, you need to be able to measure what is going on within streams, in order to detect emerging trends and influence.

The approach we’re taking in Bottlenose to solve this is a set of algorithms we call “StreamRank.” In StreamRank we analyze the series of messages in a stream to figure out what topics, people, links and messages are trending over time.

We also analyze the reputations or influence of message authors, and the amount of response (such as retweets or replies or likes) that messages receive.

In addition, we also measure the relevance of messages and their authors to the user, based on what we know of the user’s interest graph and social graph.

This knowledge enables us to rank messages in a number of ways: by date, by popularity, by relevance, by influence, and by activity.

Another issue that comes up when searching the Stream is that many messages in streams are quite strange looking – they don’t look like properly formed sentences or paragraphs. They don’t look like English, for example. They contain all sorts of abbreviations, hashtags, @replies, and short URLs, and they often lack punctuation and are scrunched to fit in 140 character Twitter messages.

Search algorithms that use any kind of linguistics, disambiguation, natural language processing, or semantics, don’t work well out of the box on these messy messages.

To apply such techniques you need to rewrite them so that they work on short, messy, strange looking messages. This is also something we’ve built in Bottlenose — we’ve built a new natural language processing and topic detection engine in Javascript that is designed specifically to handle these types of streams and messages.

These are some of the new challenges and solutions we’re applying in Bottlenose to make working with streams more productive. They are components of what we call our “StreamOS,” a new high-level Javascript and HTML5 operating system for applications that need to do smart things with streams. We’ll be writing a lot more about this in future articles.

 

Drowning in the Stream — New Challenges for a New Web

This is Part II of a three-part series of articles on how the Stream is changing the Web.

In Part I of this series, The Message is the Medium, I wrote about some of the shifts that are taking place as the center of online attention shifts from documents to messages.

Here in Part II, we will explore some of the deeper problems that this shift is bringing about.

New Challenges in the Era of the Stream

Today the Stream has truly arrived. The Stream is becoming primary and the Web is becoming secondary. And with this shift, we face tremendous new challenges, particularly around overload. I wrote about some of these problems for Mashable in an article called, “Sharepocalypse Now.”

The Sharepocalypse is here. It’s just too easy to share, there is too much stuff being shared, there are more people sharing, and more redundant ways to share the same things. The result is that we are overloaded with messages coming at us from all sides.

For example, I receive around 13,000 messages/day via various channels, and I’m probably a pretty typical case. You can see a more detailed analysis here.

As the barrier to messaging has become lower and people have started sending more messages than ever before, messaging behavior has changed. What used to be considered spam is now considered to be quite acceptable.

Noise is Increasing

In the 1990’s emailing out a photo of the interesting taco you are having for lunch to everyone you know would have been considered highly spammy behavior. But today we call that “foodspotting” and we happily send out pictures of our latest culinary adventure on multiple different social networks at once.

Spam is the New Normal

It’s not just foodspotting – the same thing is happening with check-ins, and with the new behavior of “pinning” things (the new social bookmarking) that is taking place in Pinterest. Activities that used to be considered noise have somehow started to be thought of as signal. But in fact, for most people, they are still really noise.

The reason this is happening is that the barrier to sharing is much lower than it once was. Email messages took some thought to compose – they were at least a few paragraphs long. But today you can share things that are 140 characters or less, or just a photo without even any comments. It’s instant and requires no investment or thought.

Likewise, in the days of email you had to at least think, “is it appropriate to send this or will it be viewed as spam?” Today people don’t even have that thought anymore. Send everything to everyone all the time. Spam is the new normal.

Sharing is a good thing, but like any good thing, too much of it becomes a problem.

The solution is not to get people to think before sharing, or to share less, or to unfollow people, or to join social networks where you can only follow a few people (like Path or Pair), it’s to find a smarter way to deal with the overload that is being created.

Notifications Overload

Sharing is not the only problem we’re facing. There are many other activities that generate messages as well. For example, we’re getting increasing numbers of notifications messages from apps. These notifications are not the result of a person sharing something, they are the result of an app wanting to get our attention.

We’re getting many types of notifications, for example:

  • When people follow us
  • When we’re tagged in photos
  • When people want to be friends with us
  • When there are news articles that match our interests
  • When friends check-in to various places
  • When people are near us
  • When our flights are delayed
  • When our credit scores change
  • When things we ordered are shipped
  • When there are new features in apps we use
  • When issue tickets are filed or changed
  • When files are shared with us
  • When people mention or reply to us
  • When we have meeting invites, acceptances, cancellations, or meetings are about to start
  • When we have unread messages waiting for us in a social network

The last bullet bears an extra mention. I have noticed that LinkedIn for example, sends me these notifications about notifications. Yes, we are even getting notifications about notifications!

When you get messages telling you that you have messages, that’s when you really know the problem is getting out of hand.

Fragmented Attention

Another major problem that the Stream is bringing about is the fragmentation of attention.

Today email is not enough. If it wasn’t enough work that we each have several email inboxes to manage, we are now also getting increasing volumes of messages outside of email in entirely different inboxes for specialized apps. We have too many inboxes.

It used to be that to keep up with your messages all you needed was an email client.

Then the pendulum swung to the Web and it started to become a challenge to keep up with all the Web sites we needed to track every day.

So RSS was invented and for a brief period it seemed that the RSS reader would be adopted widely and solve the problem of keeping up with the Web.

But then social networks came out and they circumvented RSS, forcing users to keep up in social-network specific apps and inboxes.

So a new class of “social dashboard” apps (like Tweetdeck) were created to keep up with social networks, but they didn’t include email or RSS, or all the other Web apps and silos.

This trend towards fragmentation has continued – an increasing array of social apps and web apps can really only be adequately monitored in those same apps. You can’t really effectively keep up with them in email, in RSS, or via social networks. You have to login to those apps to get high-fidelity information about what is going on.

We’re juggling many different inboxes. These include email, SMS, voicemail, Twitter, Facebook, LinkedIn, Pinterest, Tumblr, Google+, YouTube, Yammer, Dropbox, Chatter, Google Reader, Flipboard, Pulse, Zite, as well as inboxes in specialized tools like Github, Uservoice, Salesforce, and many other apps and services.

Alan Lepofsky, at Constellation Research, created a somewhat sarcastic graph to illustrate this problem, in his article, “Are We Really Better Off Without Email?” The graph is qualitative – it’s not based on direct numbers – but in my opinion it is probably very close to the truth.

What this graph shows is that email usage peaked around 2005/2006, after which several new forms of messaging began to get traction. As these new apps grew, they displaced email for some kinds of messaging activities, but more importantly, they fragmented our messaging and thus our attention.

The takeaway from this graph is that we will all soon be wishing for the good old days of email overload. Email overload was nothing compared to what we’re facing now.

The Message Volume Explosion

As well as increasing noise and the fragmentation of the inbox, we’re also seeing huge increases in message volume.

Message volume per day, in all messaging channels, is growing. In some of these channels, such as social messaging, it is growing exponentially. For example, look at this graph of Twitter’s growth in message volume per day since 2009, from the Bottlenose blog:

Twitter now transmits 340 Million messages per day, which is more than double the number of messages per day in March of 2011.

If this trend continues then in a year there will be between 500 million and 800 million messages per day flowing through Twitter.

And that’s just Twitter – Facebook, Pinterest, LinkedIn, Google+, Tumblr, and many other streams are also growing. And email messages are also increasing as well, thanks to all the notifications that are being sent to email by various apps.

Message volume is growing across all channels. This is going to have several repercussions for all of us.

Engagement is Threatened

First of all, the signal-to-noise ratio of social media, and other messaging channels, is going to become increasingly bad as volume increases. There’s going to be less signal and more noise. It is going to get harder to find the needles in the haystack that we want, because there is going to be so much more hay.

Today, on services like Twitter and Facebook, signal-to-noise is barely tolerable already. But as this situation gets worse in the next two years, we are going to become increasingly frustrated. And when this happens we are going to stop engaging.

When signal-to-noise in a channel gets too out of hand, it becomes unproductive and inefficient to use that channel. In the case of social media, we are right on cusp of this happening. And when this happens, people will simply stop engaging. And when engagement falls the entire premise of social media will start to fail.

This is already starting to happen. One recent article by George Colony, CEO of analyst firm, Forrester Research, cites a recent study that found that 56% of time spent on social media is wasted.

When you start hearing numbers like this, it means that consumers are not getting the signal they need most of the time, and this will inevitably result in a decrease in satisfaction and engagement.

What’s Next?

We have seen some of the issues that are coming about, or may soon come about, as the Stream continues to grow. But what’s going to happen next? How is the Stream, and our tools for interacting with it, going to adapt?

Click here to read Part III of this series, Keeping Up With the Stream, where we’ll explore various approaches do solving these problems.

The Message is the Medium – Attention is Shifting from the Web to the Stream

Shift Happens

A major shift has taken place on the Web. Web pages and Web search are no longer the center of online activity and attention. Instead, the new center of attention is messaging and streams. We have moved from the era of the Web to the era of the Stream. This changes everything.

Back in 2009, I wrote an article called “Welcome to the Stream – Next Phase of the Web” which discussed the early signs of this shift. Around the same time, Erick Schonfeld, at TechCrunch, also used the term in his article, “Jump Into the Stream.” Many others undoubtedly were thinking the same thing: The Stream would be the next evolution of the Web.

What we predicted has come to pass, and now we’re in this new landscape of the Stream, facing new challenges and opportunities that we’re only beginning to understand.

In this series of articles I’m going to explore some of the implications of this shift to the Stream, and where I think this trend is going. Along the way we’re going to dive deep into some major sea changes, emerging problems, and new solutions.

From Documents to Messages

The shift to the Stream is the latest step in a cycle that seems to repeat. Online attention appears to swing like a pendulum from documents to messages and back every few decades.

Before the advent of the Web, the pendulum was swinging towards messaging. The center of online attention was messaging via email, chat and threaded discussions. People spent most of their online time doing things with messages. Secondarily, they spent time in documents, for example doing word-processing.

Then the Web was born and the pendulum swung rapidly from messages to documents. All of a sudden Web pages – documents – became more important than messages. During this period the Web browser became more important than the email client.

But with the growth of social media, the pendulum is swinging back from documents to messaging again.

Today, the focus of our online attention is increasingly focused towards messages, not Web pages. We are getting more messages, and more types of messages, from more apps and relationships, than ever before.

We’re not only getting social messages, we’re getting notifications messages. And they are coming to us from more places – especially from social networks, content providers, and social apps of all kinds.

More importantly, messages are now our starting points for the Web — we are discovering things on the Web from messages. When we visit Web pages, it’s more often a result of us finding some link via a message that was sent to us, or shared with us. The messages are where we begin, they are primary, and Web pages are secondary.

From Search to Social

Another sign of the shift from the Web to the Stream is that consumers are spending more time in social sites like Facebook, Pinterest and Twitter than on search engines or content sites.

In December of 2011, Comscore reported that social networking ranked as the most popular content category in online engagement, accounting for 19% of all consumer time spent online.

These trends have led some such as VC, Fred Wilson, to ask, “how long until social drives more traffic than search?”  Fred’s observation was that his own blog was getting more traffic from social media sites than from Google.

Ben Elowitz, the CEO of Wetpaint, followed up on this by pointing out that according to several sources of metrics, the shift to social supplanting search as the primary traffic driver on the Web was well underway.

According to Ben’s analysis, the top 50 sites were getting almost as much traffic from Facebook as from Google by December of 2011. Seven of these top 50 sites were already getting 12% more visits from Facebook than from Google, up from 5 of these top sites just a month earlier.

The shift from search to social is just one of many signs that the era of the Stream has arrived and we are now in a different landscape than before.

The Web has changed, the focus is now on messages, not documents. This leads to many new challenges and opportunities. It’s almost as if we are in a new Web, starting from scratch – it’s 1994 all over again.

Click here to continue on to Part II of this series, Drowning in the Stream, where we’ll dig more deeply into some of the unique challenges of the Stream.

Live Matrix Acquired by OVGuide

I’m really pleased to announce that a startup I helped co-found, Live Matrix, has been acquired by OVGuide, a leading video portal.

TechCrunch covered the deal here.

The new combined company is a unique powerhouse in the online video space – covering the entire life cycle of online videos from when they are upcoming, to when they go live, to when they are on-demand.

Sanjay Reddy, my co-founder, and friend, has done an amazing job bringing our vision to life. The deal with OVGuide is a big step forward in the evolution of this project. I look forward to great things from the combined company. Congratulations to the team, and my thanks to our loyal and helpful angel investors. It’s been a very interesting project to be a part of.