Knowing that wearable technology such as Google Glass provides “answers without having to ask,” how can marketers position content to answer questions that are not being asked?
Wearable Search engines provide results based on search intent, but that was not always the case. Search engines evolved to a point where they could understand intent, and today they are evolving to a point where they can predict intent. Unprompted search occurs when search engines predict intent and push search results completely unprompted. Unprompted search is limited to push friendly devices such as wearable technology, at least at this point.
To understand how we arrived at unprompted search, let’s take a simple historical look at search as it relates to intent. Then, let’s look at ways in which marketers can build content for technology wearers who want answers but are not asking questions.
In the beginning, there was Discovery without Search. Web portals and directories such as Netscape and DMOZ provided links to great content that had already been discovered by humans associated with those portals and directories, but did not provide a direct connection to content outside this limited web segment. To understand this phase, think of both the potential and limitations of a book recommendation.
Then there was Search without Intent. Early search engines such as Lycos crawled the web and returned pages that contained the search queries that were entered. This liberating feat allowed searchers to explore the web outside of directories, but could not address instances of search phrases appearing on a variety of pages for multiple subject matters. Think of someone recommending the Jungle Book because you asked about the Bears.
And then there was Search with Intent. Sophisticated search engines such as Google employed time, location, history, and other data points to pinpoint search intent so that only relevant results were displayed, but poor unfortunate souls still actually had to search for something to get results. Think of someone telling you the score of a game in progress because you asked about the Bears.
Unprompted Search: Intent without Search
Now we are at the advent of Intent without Search. Search engines can flag situations where intent can be predicted based on common confluences of data points, and can then push those results to technology wearers. The search engine recognizes an intent based on data points coming together, predicts the search that would have occurred based on that intent, and then delivers the result of that predicted search query.
The example Google provides is that based on time and a wearer’s location within an airport, Google Glass can predict the wearer wants flight information and then can push flight statuses to the wearer completely unprompted. Here’s an example I would like to see: Based on the Chicago Bears’ defensive performance data, time and wearer location near the NFL draft podium, Google Glass could predict that wearer Phil Emery wants to draft a safety and could push names of the best still available in the draft to him, also completely unprompted.
Unprompted search likely will not eclipse prompted search, but it will become more prevalent as wearable technology moves to mainstream audiences. Keep in mind that wearable technology includes drivable technology, so its likelihood of going mainstream does not hinge solely on the fate of Google Glass. Also, note that while unprompted search is currently limited to wearable technology, this does not mean it cannot expand to any push friendly devise and user.
Now that search engines can predict intent and push content without a search query, marketers must learn how to get search engines to push their content for unprompted search results.
Marketers Must Ask Better Questions for Unprompted Search
Unprompted search is crawl based. Search engines rely on crawlable data (web pages, time, location, etc.) to predict need for search. No search means no keyword. In order to generate content for unprompted search, marketers must stop thinking about keywords and start thinking about confluences of crawlable data.
Marketers must answer these questions when building content for wearable/drivable technology users.
- What is a predictable search need for my targeted audience?
- Let’s pretend our targeted audience is drivable technology owners. A predictable need for this audience would be gas.
- What are the data points that come together when your targeted audience has a predictable need for search?
- The confluence of data points would be time of day, car location, and amount of gas in the tank.
- What content will satisfy need when those data points come together?
- The content would be location, open hours, and prices for nearby gas stations.
- What format will best present this content?
- The format would be an app that identifies the need for gas, and then displays nearby gas stations, prices, and directions to the selected result
Marketers should not wait for wearable technology to go mainstream in order to start asking these questions. Many reasons exist to rid a marketing campaign of keyword reliance, and search engines could choose to push unprompted search results to other audiences at any time. At best, these questions fuel content for the unprompted search opportunity ahead. At worst, these questions fuel better content for any targeted users.
Stay Tuned for Structured Data for Unprompted Search
My next post will discuss structured data as it relates to unprompted search. Structured data is your content’s opportunity to help search engines understand how that content should interact with users. Search engines will rely heavily on structured data to communicate the predicted need and the confluence of data points for content that is likely to push for unprompted search.