How knowledge graphs can revolutionize the digital customer experience

The internet has brought all human knowledge within reach. Unfortunately, finding only the Turn right piece of information quickly and easily has become like finding the proverbial needle in a haystack. In an age where so much content is so easily available, we have to ask ourselves: how do I choose what to click on first? Is this a trusted source with reliable information? And how much time do I want to spend searching?

As an ordinary person looking for an easy answer, this flawed process adds time to your journey. As a consumer, a broken knowledge management strategy can make interacting with a brand frustrating at best — which in turn can mean a purchase being canceled, a deterioration in brand loyalty, or even outright anger that can translate into negative reviews.

The good news is, there’s a solution right in front of us: by leveraging the gold standard of search (Google) and adopting a knowledge graph-driven information management system, brands can provide customers and their support teams with the answers they need . need in the most direct way possible.

What is a knowledge graph?

The concept of knowledge graphs is intuitive to people because it is based on understanding the context of different segments of a question. For example, if I ask a friend, “Do you have a recommendation for a pediatrician in town who speaks Spanish?” they understand that a pediatrician is a kind of doctor, that “in the city” means “near” and that knowledge of the Spanish language is required.

But making these connections was difficult for machines until recently. Enter Knowledge Graphs: A way to organize and connect different categories of related data – also known as entities – so that they can be easily “understood” by different search algorithms.

Think of these entities as databases of information to themselves from which a search can draw. To give another example, if you are looking for information in a school system, individual entities can be staff, classes, extracurricular activities, buildings, and class numbers. With this framework, a knowledge graph connects disparate groups of data based on the context of the query.

If a user were to search for, “Where’s Mr. Johnston’s history lesson in the third period?” a knowledge chart will use each part of that question in different ways: “where” indicates location, “Mr. Johnston” stands for staff, “third period” and “history lesson” stands for time and schedule.

Combining all these different datasets into one query — based on the user’s natural language — allows the search engine to combine the data in just the right way to provide an exact answer. In traditional search, this query would simply pick the key terms and return a list of results, which could simply be links to articles or other resources, rather than a direct answer.

For brands, knowledge graphs are vital for connecting different types of informational content that exists across numerous platforms, including content management systems, customer relationship management platforms, and other information sources. With brands investing so much in content, it’s frustrating for anyone when a customer has to contact support because a search wasn’t advanced enough to find answers that already exist on the site.

Answers findable and knowledge findable

When knowledge graphs are used successfully, they make answers discoverable. But what exactly does that mean?

Again, we can look to Google for the answer to that question. When you ask Google a specific question, Google can give you the answer in a featured snippet, along with a structured inspector with related information. This is a feature you’ve seen time and time again; searching for “How tall was Andre the Giant?”, the results give a simple answer with his height – 7’4″ by the way – rather than a series of links to articles and websites that contain a reference to his measurements.

On a branded website, these special info boxes can draw on a knowledge graph built from information in product manuals, articles, FAQs, support documents (and more) to provide actionable answers in context for the customer. So if a customer were to search a manufacturer’s website for “how to clean a microwave,” they’ll be presented with step-by-step instructions instead of links to articles that may or may not answer the exact question.

When these answers are easy to find, users avoid contacting customer service or spend time sifting through unstructured content to arrive at an answer. It also avoids the worst-case scenario of the customer actually leaving the website to ask Google their question and potentially being redirected to a competitor or a third-party site with questionable intent.

It is important to remember that the quality of search is not measured in a silo these days. A customer is not going to compare individual brands based on their search query; instead, the best search experience is now considered the standard for everyone. When Google, Amazon, Apple and other veteran leaders make it easy to get the right answer quickly, we wonder, “Why can’t every brand make it easy too?”

When answers to questions become available, knowledge also becomes easier to find. But what is findability?

Where findability in context provides useful answers, findability means that users can more easily find information that is not directly searched for. Again, building on knowledge graphs can provide context for recommended content that understands a user’s intent and provides further relevant information to enrich their experience.

Both discoverability and discoverability are important to the customer experience, and knowledge graphs serve as the foundation for delivering that enhanced experience.

Building a better search experience for everyone

While Google has been the gold standard for applying knowledge graph structures to search for years, the technology itself isn’t just walled off to Google; it is accessible to any brand that wants to use it. Setting up a knowledge graph-based search system is an effort a brand can undertake, tailored to the products, services, and information resources the company uses. Building this better search system aggregates business knowledge by connecting disparate information systems into one actionable engine that works for both customers and support teams.

With analytics, support, and experience, leaders can review common queries to identify bottlenecks across the customer journey. A knowledge graph-based system complements these insights to form a powerful knowledge management tool. Businesses can analyze customer engagement and sentiment with search analytics, while having access to a scalable content infrastructure that can quickly address and close knowledge gaps. This level of actionable insights is invaluable in improving the overall customer experience.

Brands invest heavily in content. Knowledge graphs turn this into the most useful version of itself, enhancing resources so that answers can be found and deeper insights can be found.

Joe Jorczak is head of industry, service and support at Yext.