4 technologies that are rapidly changing online search habits

The traditional search via desktop browser has been on the decline since advent of the iPhone a decade ago. Currently, a typical mobile user might search via Google Maps, or the app versions of vertical services like Yelp, or through social networks of every stripe.

The next generation of search will rely on far more complex and intelligent methods. This will change not only how consumers find, say, their way to your product, but also the way brands can connect with their presences online.  Here’s a look at some of the ways that search technology is already evolving.

Virtual assistants

Google Now, Apple’s Siri and, more recently, Amazon’s Alexa, are OS-level virtual assistant (VA) services that have been widely embraced by consumers who relish the convenience of voice control.

On one level, they’re simply portals to traditional search engines, except the interaction is more of a dialogue and search results are highly personalized, sometimes even just direct answers to questions or predictive search results based on a person’s real-time behavior, thanks to cloud- and big data-connected AI. As a result, they’re able to access everything from your email inbox and shopping habits to location and demographic information.

What’s more, these assistants are moving beyond the smartphone into everything from TVs and cars to desktop computers and dedicated “assistant” devices like the Amazon Echo and Google Home.

Thanks to advances in AI, these get better every day, and will continue to do so.  For instance, a recent demo of a new VA, Viv, by the makers of Siri, showed off  the ability to understand far more human-like conversation and interact with third-party services, so that you can place sophisticated requests as easily as you would with a concierge.

Chatbots

Chatbots have been around for years, but they’re increasingly powered by the same kind of AI and big data access that makes everything from Siri to Watson so smart.

Already at use in messenger platforms such as Kik and WeChat, and just starting in Facebook Messenger, today’s chatbots are poised to become a standard point-of-service (POS) feature in the near future for every business and content producer, allowing users to conveniently and efficiently discover, select and purchase products, look up information, or receive in-depth customer service in highly automated and personalized ways.

Already, KLM and Nexmo have paired up to offer current flight information to travelers in both WeChat and Messenger. With APIs coming out from Facebook, Nexmo and others, one can imagine everything from a search for all the news on, say, the presidential election to a search for movie times from Fandango to directions to the airport — all conducted via a chat dialog with a bot.

Micro-location

We are in the age of wearables, and even the simplest of these devices – a FitBit, for example — is capable of gathering a rich array of data, much of it intensely personal.

That includes the obvious cross-section of health data stats—what we do, at what intensity, and for how long—but also, crucially, our location. As this data becomes better integrated, it will be used to create a more refined personal context for search results or suggestions and prompts via virtual assistants.

Similarly, beacon technology, which has yet to have a break out moment, will eventually be a commonplace source of data used to refine search results. Your habits, your preferences, your locale—all of those are extremely revealing and create a context that will be leverage to create more precise search.

Looking for the nearest place that has your favorite running shoe with the correct shoe size and support in stock? AI-powered micro-location search can help you cut to the chase by finding the nearest store, enabling you to purchase the shoe, and simply pick it up when you get there — all with just a few taps or voice commands.

Visual recognition

From Facebook to Instagram to Twitter, photos and video have become a large part of the content we consume, and what we look at, like, share and snap is valuable and revealing data. Yet the vast majority of those images lack any identifying text or hashtags, and so are extremely tricky to find.

There are a number of companies devising image recognition software that are already harnessing this rich source of data and make it available for search. On a consumer level, Google Photos, for example, can help you find pictures in your own collection that you haven’t labeled just by entering names, scenes, objects, locations, and more — the program automatically labels the contents of these images via image recognition, with no “tagging” required by the shooter.

On a B2B level, contextual ads are served to relevant images found via image recognition, essentially monetizing images, just as AdWords monetized search words. Marketers are also now able to employ computer vision algorithms that can analyze photos and pick out faces, animals, objects, logos, and more, in a relative instant. This lets brands have access to better and previously unavailable insight about how consumers use their products.

We’re on the cusp of major change with search, when a flood of data, synthesized from a variety of novel sources, will be leveraged to be create valuable, fast, and precise search results for users, whether they’re consumers or brands.

As with search so far, the key for forward-thinking marketers and brands will be to not only delve deeply into these new technologies and platforms as research and marketing intelligence tools, but also to master newfangled forms of search engine optimization.