From intent to purpose…
Good friend Jeremiah Owyang recently wondered whether or not the real-time Web was fast enough to keep pace with our insatiable appetites for information and connectivity. As such, Jeremiah introduced the emergence of what he refers to as the “Intention Web.”
With event planning features, like Facebook events, upcoming.org, we’re starting to see people make explicitly public remarks on what they want to do, when, and with who. Welcome plancast.com a startup by Mark Hendrickson formerly of Techcrunch who created this simple website that allows people to broadcast what they plan to do next using Twitter or Facebook.
Owyang summarized the true opportunity for the Intention Web as follows…
Bottom Line: Intention Web will provide consumers with contextualized experiences. People will work together to share their information about what they plan to do, and improve how they work or organize. Expect Social CRM systems (Salesforce, SAP), Brand Monitoring vendors (Radian6, Visible Technologies), and Search Engines (Bing and Google) to quickly try to make predictive models on what could happen, and what are the chances. Businesses that have a physical location like retail, events, or packaged goods can use this data to anticipate consumer demand. They may offer contextualized marketing, or increase or decrease inventory or store hours to accommodate. Don’t be surprised in the future and you walk into a store with your preferred items, meal, or drink already nicely packaged for you.
His reference to Plancast is indeed representative of an emerging medium to publish future activities and intentions. And as such, they trigger a social effect that introduces new opportunities and incites potential activity among those within an immediate social graph as well as those defining the friends of friends network. While Plancast is a new service, Facebook events, Upcoming, as well as travel services such as TripIt and Dopplr, among many others across multiple verticals, have long represented an emerging category for the publishing and sharing of planned activities and goals. These activities serve as social objects and as such, they reveal information that can transform activities into relevant content and experiences that are presented to us in the near future.
The Predictive Web
Social Media becomes less about a move-and-react strategy and sets the stage for engendering meaningful interactions as well as building more tuned business infrastructures to support anticipated activity based on the intelligence and insight extracted from online behavior.
As 2010 begins a new decade, we also usher in a new genre of context and personalization in the evolution of an intelligent and semantic Web – a Web that Tim O’Reilly, refers to as Web Squared. Among the hottest trends taking place in and around The Golden Triangle of social, mobile and digital innovation is the emergence of geo-local networking (such as Loopt, FourSquare and Gowalla), augmented reality, and social filtering.
In the Future of the Social Web, I discussed the materialization of technologies and applications that would introduce a new era of social context in 2010. The reality is that these capabilities have existed for quite sometime, however, the iteration of new products and underlying algorithms have matured to a point where we can consider solutions for mainstream applications.
After several years of harnessing the power of participatory media, the wisdom sourced from crowds proves that crowdsourced insight is not an exact science. As Andrew Keen author of the best-selling book The Cult of the Amateur once observed, “Sometimes the Wisdom of the Crowds is not so wise after all.”
On the other hand, the idea of collective intelligence is extremely promising as it registers and converts activity and interests based on how we as individuals interact with content and objects within a site or community and as patterns and paths emerge, the algorithm adapts to create more efficient passages.
Jack Jia and Dr. Scott Brave of Baynote are fusing crowdsourced intelligence with social sciences. As such, they built a sophisticated platform that transcends mob rule into swarm intelligence – a form of artificial intelligence based on the collective behavior of decentralized, self-organized systems. Ants, for instance, wander randomly until food is found and returned. In the process, ants leave pheromone trails which eventually lead other ants to following and eventually optimizing the path. The greater the concentration of ants who pick up the trail in shorter periods of time, the denser the pheromone trail becomes. On the contrary, if other ants do not pick up on the path, pheromone evaporation occurs and over time, the path is lost.
Baynote uses swarm intelligence to employ a type of ant colony optimization that enables an online system to automatically learn from organic behavior to personalize and enhance the online experience – reducing click paths and surfacing relevant content to connect people to relevance expeditiously. If you were searching for a particular product on Zappos.com, for example, you may have to sort through product after product until you finally matched the result with your choice of keywords. If Zappos.com integrated Baynote, as users replicated the activity, the system would automatically identify the pattern and reorganize the content based on keywords and clicks to match products to people faster and more efficiently.
In many ways, we click aimlessly today, and as we search for information, people, and social objects, we do so until we stumble upon something that captivates our attention. We then react, save, share with others, but without movement on a mass scale, the direct path to the content or the experience is erased. To help, meme trackers (or trend watching) technologies such as Tweetmeme, TechMeme, Blogged.com, among others, help create direct paths to the data online that enchants us en masse within fixed periods of time.
Money doesn’t grow on trees, but it does grow on Tweets
In a recent issue of MITSloan Management Review, the writers observed,
There’s a new tool that can help companies predict sales for the coming weeks, or decide whether to increase inventories or put items on sale in certain stores. It’s Twitter.
The post captures something that I believe represents the defining spirit for excelling in what is shaping up to be an online Darwinian survival of the fittest.
In a new era of socially conscious and responsible businesses, we will manually observe and maneuver the adaptation and streamlining of products and services as well as the customer experience based on online social activity. A majority of these events will follow the path proven by the likes of Baynote to herald a coveted, predictive Web that surfaces personalized focus and value.
If we review a basic model of a tag or word cloud, not unlike the ecosystem that tracks and pools “trending topics,” we can visualize the most commonly used keywords related to most online activity. As Huaxia Rui, Andrew Whinston, and Elizabeth Winklery wrote in the MITSloan Management Review,
We believe executives can make accurate predictions about sales trends by analyzing tweets that mention their products or services, and we have created a model based on Twitter’s keyword-search function to help them do that.
I call this trendcasting, the ability to spot themes and pinpoint opportunities to deliver a solution to a need that either exists, is emerging, or is on the horizon based on the concentration of social conduct.
But as we see in technology similar to Baynote, we can surface trends without the need for manual search, and I believe this technology exists today and will soon become prominent. Imagine improving the experience within social networks such as Twitter and Facebook and in turn, within every stop along a sales cycle, to ensure that in each instance, we’re presented with content that matters to us at the right moment, in the appropriate context.
Improving the Signal to Noise Ratio
While improving the signal to noise ratio is a never-ending quest, in social media, noise is amplified exponentially. In an era of the predictive Web, platforms will emerge that present people and content based on who we are, what interests us, and how we navigate the Web.
During the proliferation of RSS feeds and the feelings of being overwhelmed by those who over-subscribed to their favorite sources, services such as AideRSS (now PostRank) and also mSpoke created platforms that organized feeds based on our preferences, implied explicitly (something we say or do) and implicitly (how we interact with what’s presented.) The goal was to organize feeds to prioritize the content each system formulated would best match what we should read.
Recently, My6Sense (note, I’m an advisor) recently launched a new iPhone app that channels social streams into a river of relevance. The app takes social and RSS feeds and analyzes content based a sophisticated algorithm (dubbed digital intuition) to serve tweets, updates, and posts without requiring manual input of preferences. Essentially, it predicts what you would find most interesting as determined by how you react to your content normally. Through a “top messages” function, you are presented with qualified content. It improves the more you use it and quickly, you’ll realize its potential for expediting the future of the predictive web.
What if the technology powering My6Sense was built into your attention dashboard (Tweetdeck, Seesmic, or Tweetie)? What if digital intuition powered CNN or Techmeme, presenting only the stories, comments, and reactions across social networks that aligned to your interests?
From Estimation to Prediction
Suddenly the predictive Web comes into focus. The innovation that materializes into products and platforms creates an ecosystem that wires the individual human algorithm to the technology that will work on our behalf to mine and present data, content, products, people and companies that match what interests us based on who we are – not solely derived from what we have in common. Just because we viewed a common item, purchased a product or service based on our click patterns or share contacts within networks, doesn’t imply, nor does it guarantee, that we share interests, ideas, and ambitions. Therefore, the ability to predict is only as accurate as the technology that focuses on who we are defined by all we do.
Make no mistake, the web will evolve from social chaos to genuine social “ME”dia to personalize experiences and solutions. Attention is a precious commodity and it is not to be taken for granted. Our attention only thins with every new and interesting object that traverses across our horizon. As such, technology will help save us from our insatiable appetite for information and eventually predict what it is that interests or benefits us before we may realize the need or desire.
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