Tap the Anticipatory Web Now
Web 2.0 has often been described as the “participatory Web,” where startups thrived by creating experiences that changed how people interact with, use and, most importantly, share with the Web. In this read-write Web, companies derived value from making the most useful tools and engaging user experience.
While these principles will continue to be important, we are now seeing a shift to the next battleground: Anticipating user needs by building on and understanding data.
Many thought leaders, such as Tim Berners-Lee, have referred to Web 3.0 as the “semantic Web,” which understands the meaning of things.
In reality, we are already beyond Web-enabled services that merely understand concepts. Just think of Amazon, Netflix, Facebook, and countless advertising platforms that serve and recommend content based on your actions across the rest of the Web.
Today, we have graduated to a new challenge of building an anticipatory Web. The “meaning of things” is really just the by-product of data, and specifically massive amounts of user data that can be analyzed and made actionable. Whether you call it Big Data or little data, the important distinction in this new era is that content is “anticipatory” — it’s a very personalized experience that attempts to give you what you need before you search for it.
What’s driving the push into anticipatory technology?
Data is more available and accessible
This is a no-brainer if you have followed technology for the past decade. The cloud enables cheap storage and computation, and these costs are declining at a Moore’s law pace.
Data is more portable and open
Technologies like OAuth and open APIs have facilitated more open and secure data exchange, and in general, services have become less throttled. This isn’t universally true (e.g., LinkedIn) but most companies that have embraced the platform are seeing network effects in terms of engagement.
Consumers are willing to trade privacy for better experiences
Companies are giving consumers more reason to share more about themselves and their social graph in exchange for a better experience. NPR recently quantified the net dollar savings based on each data type. For example, letting services know all of your driving history, and having access to your vehicle data in real time, can save you gas money and better predict when you have a maintenance issue. Gen Y is concerned about privacy, but, so far, millennials see things differently. What will end up as the norm remains to be seen.
Consumers are willing to train the data
In the early days of Web advertising, companies had major doubts that a consumer would manually vote whether or not an ad is relevant. Now it happens every day on Facebook or Hulu. Users seem be accepting the importance of advertising to sustain business, and are more willing to put in the time to at least improve their overall experience. Pandora and Netflix have become some of the biggest tech stars by marrying data with end-user training, a unique advantage over their Web 2.0 competitors.
The mobile ecosystem has evolved
Smartphones are pervasive, and with 64-bit CPUs and an ever-growing array of sensors, the software experience is for the world to define. LTE and Wi-Fi pervasiveness have basically made bandwidth a non-issue. Dell once considered mobile to be their laptop division, but the world has changed as the smartphone has become the personal computer.
Wearables amplify always-on data
The ever-connected lifestyle means more data and touchpoints are available than ever before. Google Glass, smart cars and smartwatches such as Basis and Pebble create new signals and ways to interact with personal data. The anticipatory Web can now be delivered on screens for every context and use case. In particular, it will be interesting to see how smarter notifications are managed across devices, screen types, times and locations.
For some companies, the anticipatory Web started more than a decade ago, and they are now realizing a competitive advantage of “compound interest.” Data builds on data, just like the compound interest in your bank account, making it more difficult for laggards to catch up.
This compound interest around data does amazing things at scale. For example, Google Now uses a combination of multiple signals and massive amounts of existing search data that has been collected and analyzed for years. Even Google Plus, though widely panned, focused the company on bringing its disparate user data together to create the “ultimate” user data store and offer more connected, relevant experiences.
In the last few months, we have seen some of the world’s biggest tech companies push hard into the anticipatory Web: Apple with its purchase of Cue, Yahoo funding Carnegie Mellon University’s research into a smarter personal assistant, Microsoft’s ongoing development into assistant “Cortana,” and Google’s continuing push into the Internet of Things and acquisition of Nest. Another great example is how Netflix predicted the success of “House of Cards” by anticipating what viewers wanted. This is a phenomenon that is not relegated to tech; it’s happening across the board in the financial, health, retail and entertainment spaces, and beyond.
That said, there is still plenty of room for disrupting the titans of the industry. Companies like Google and LinkedIn are fundamentally constrained by business goals such as the desire to connect and integrate only their own siloed data. For example, Google Now obviously prefers Google+ to Facebook or LinkedIn, and Google Places over Yelp or Foursquare. There is less incentive for these companies to go outside their own ecosystems.
The anticipatory Web is happening now. Although it’s presently defined as a category of its own, due to the small number of companies tackling the challenge, it is soon to be another layer that spans the ecosystem. As the battle for the best anticipatory technologies continues to heat up, data will become increasingly paramount, and the companies that can best collect and build on data will lead their respective industries. It’s time to evaluate how you can use data to deliver a more anticipatory experience, or you may find yourself too far behind to catch up.
Raj Singh is CEO and co-founder of Tempo AI, creators of the Tempo Smart Calendar. Prior to Tempo, he served as founding VP of business development for Skyfire, the mobile browser that sold to Opera for $155 million, and has also worked in product management, engineering, strategy and consulting for many innovative companies. Reach him @mobileraj.