As search becomes less traditional, it is becoming increasingly important to our world. Yet, when most users think of search, they still imagine the old Google style of a search bar with ten blue links, from an era where platforms used search queries to look for keywords in web pages to select the most relevant destinations.
While traditional search engines were very capable at servicing what we call navigational search, there were always too many pieces of information competing for our attention. Platforms had to search across many pieces of information based on what they know about the user, to prioritise what was shown and when. Consequentially, our definition of a search engine has evolved to keep up.
Modern search engines however are different. They are better at responding to different kinds of searches beyond navigational, particularly informational and transactional searches. An informational search doesn’t respond with a website, but with a fact like “What time does my local Asda close?” or “What does ‘smells like teen spirit’ mean?” Transactional searches don’t respond with websites or information, but actions. “Book a cab” or “Order a pizza” are both perfectly valid requests to make to a search engine. It might respond with links to websites, or into apps, or even maybe with buy buttons.
Now instead of searching for keywords in web pages to present destinations, search engines scan for information and action terms across a multitude of structured data to present a far broader and higher quality set of results. This structured data can come from a wider variety of places and could include everything from data scraped from websites and information from emails, to live weather feeds and sports scores. What all of this means is that more than ever, brands can structure the information they share online in such a way as to help a search engine use it to respond to as wide a variety of user requests as possible.
Google Now is a great example of keywordless search. Instead of waiting for a user’s explicit request for destinations, information or actions, the platform scans a user’s data (location, time of day, search history, browsing history, etc) to search for matching information from across the structured data it has available.
This new model extends far beyond the companies that users would normally associate with search. Facebook, Twitter, and indeed most if not all of the major platforms today each utilise location based data in some form to target their content to users based on their search history. Content across these platforms increasingly includes links to websites, relevant content and information from outside sources. With so many items competing for a user’s attention, these platforms have to evaluate context to prioritise what to show and when. Just like a search engine.
In fact, it is arguable that all programmatically bought digital media today fits our modern definition of a search engine. To match a user’s context against the structured data available, to present the best possible result at the right time, with the information that adds the most value. What brands need to focus on is how these ad platforms use data to define the criteria for targeting when and where an ad should show. The stronger the data and the higher the quality of the content, then the easier it is to get the right information in front of the right user at the right time.
Marketers and brands alike need to ensure that they are thinking about media the right way when planning any digital campaign. When a user sees an ad, it is because a search has been done. It is therefore incumbent upon us as marketers to make sure we are providing search engines with the best possible data to work from, so that they know when and where to display a brand’s ads. With so many messages competing for consumer attention, brands can only get traction with the right people in the right context.
