Think Search Is Solved? Think Again
Search is one of the oldest technologies around. Ever since the dawn of the World Wide Web, a search engine has been the portal through which we obtain information. The search for a better search engine index kick started the Hadoop craze, and it continues to drive Google to push the limits of technology. But don’t for a second think that search has been solved.
“Who said it’s solved?” barked Coveo’s Director of AI Ciro Greco in a recent interview with Datanami. “Search is far from being solved. It’s the hardest thing we do. It’s the hardest thing everybody does.”
Coveo is one of a handful of companies building the next generation of search engines, although even calling it that seems to be a disservice to someone. Forrester calls the field “Cognitive Search,” thanks to the abundance of machine learning, natural language understanding, and deep learning embedded in these products. Gartner, in turn, calls them “Insight Engines,” because they deliver more context than mere search engines.
“In contrast to search engines that provide links to original source materials such as documents and videos,” Gartner analysts write in a September 2019 Magic Quadrant report, “insight engines can also provide contextual information about the fact or entity in question.”
Today’s enterprise search engines use the index as the starting point for surfacing insights. But beyond just returning information based on the degree to which an entered term matches a predefined keyword stored in an index, modern search engines bring other data and technology to the party, including text analytics and machine learning technology that try to predict what the user is trying to find.
For Greco, the nature of search itself opens up such a rich field for exploration that it could never possibly be perfectly solved. For example, if people could always be relied upon to enter the perfect search term, there wouldn’t be much of a need for more elaborate search technology. But, of course, we’re all human, and so we can’t be relied upon to do that.
“If you go in a website because you have a problem with a product and you want to use a search engine to find the right information in your knowledge base, what are you going to do?” he asks. “Do you know exactly what you’re looking for? No, because you have a problem right. Do you know exactly where that thing is stored and how it is expressed in the documentation that this company has? Obviously not.
“So what you’re trying to do when you do search is actually you’re trying to the best of your knowledge to guess the intent of the human being through different layers that separate you from this person,” he continues. “It is as hard as trying to understand the map of New York City from the people that go in and out from JFK, if that is the little information that you have.”
Search itself will never be solved because of that humanness and that human connection. Some vendors may be better at it than others, and their search systems work better than others. But that does not mean that it’s simply a matter of dropping in a better search engine, because almost every single search engine is contextual and customized to solve specific problems for certain groups of users in individual use cases.
“Search is a fascinating topic,” Greco says. “I think it gets a bad rap because Goolle is good it, and so people think that search is easy. But I think the general rule in life is if Google can do it, that does not mean that you can.”
Coveo, which is based in Quebec City, Canada, has emerged as one of the leaders in the modern incarnation of search, and has been highly ranked in recent editions of the Forrester Wave and Gartner Magic Quadrant.
Forrester, for instance, hails Coveo’s cognitive search capabilities and the availability of pre-built applications for a range of use cases, including customer self-service, customer communities, and in-product intelligence. It applauded its integration with Salesforce, ServiceNow, and Microsoft Dynamics 365 applications, as well as its abundance of data connectors, ability to ingest data, and tuning of the search.
Gartner, for its part, hailed Coveo’s “rich set of productized integrations” and its “well-rounded ecosystem.” Its in-house investments in AI and machine learning pay off with the product’s workflow, the analyst group says, and its reference customers ranked it highly. (It has 600 customers.)
For Coveo, the motivation for building a better search engine really revolves around helping its customers power its customers’ journeys. Being able to bring the data that can impact that customer journey is just as critical as the decisioning that is done upon that data, according to Greco.
“What we’re investing in mostly is data tracking and processing in a way that can basically get data from one place and from another, knowing that you’re going to optimize for different things in the specific touch points,” he says. “In one case you want to optimize for conversion, in one case you want to optimize for [something else]. But then, always keeping it in mind that, down the line, you want these two things to be reconciled in a data platform that can tell you something meaningful from a long distance.”
Improving search is a priority at many companies, particularly when they’re weighing their own search prowess against the likes of Google, Amazon, or Netflix. The big difference, of course, is those tech giants have an abundance of very fine-grained data that allows them to track individual preferences at a very detailed level. The average company does not have access to that kind of data, and so they must do something different with their search engines.
“When we’re chatting with a customer or starting an initiative, the challenge often starts with ‘I have crappy search. I have a search problem and I want to improve the search experience,’” Greco says. “But it goes beyond that because when you thank about Coveo it’s at the intersection of three technologies – search, recommendation, and personalization.”
Search isn’t solved because the challenge has become greater than it previously was, and customer expectations have evolved today. The trick today is helping customers get more out of the limited data that they do have. That way, with a little bit of human intelligence and the right application of machine learning technology, customers can improve their clients’ search results, and get better recommendations and a more highly personalized customer experience.