How Knowledge Graphs Can Revolutionize the Digital Customer Experience

The internet has put all of human knowledge at your fingertips. Unfortunately, just finding the right quickly and easily information has become like finding the proverbial needle in the haystack. At a time when so much content is so readily available, we are forced to ask ourselves: how do we choose what to click on first? Is it a reliable source with reliable information? And how much time do I want to spend searching?

As an ordinary person looking for a basic 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 an abandoned purchase, degraded brand loyalty, or even outright anger. which can result in negative reviews.

The good news is that a solution is right under our noses: by taking inspiration from the gold standard of search (Google) and establishing an information management system based on knowledge graphs, brands can provide customers and their support teams with the answers they need in the most direct way possible.

What is a knowledge graph?

The concept of knowledge graphs is intuitive for humans 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 type of doctor, that “in town” means “nearby” and that fluency in the Spanish language is required.

But making those connections has been difficult for machines until relatively recently. Capturing knowledge graphs: a way to organize and connect different categories of related data – called entities – so that they can be easily “understood” by various search algorithms.

Think of these entities as databases of information in themselves that a search query can retrieve. To give another example, if you were looking for information in a school system, separate entities might include 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 search query.

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

Connecting all of these different sets of data into a single query – based on the natural language of the user – allows the search engine to combine the data in the right way to provide an exact answer. In traditional search, this query would simply select key terms and provide a list of results, which may simply be links to articles or other sources of information, rather than a direct answer.

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

Make answers findable and knowledge discoverable

When knowledge graphs are successfully deployed, they help find answers. but what does that mean exactly?

Again, we can look to Google for the answer to this question. When you provide Google with a specific question, it has the ability to give you the answer in a featured snippet with a structured info box of related information. It’s a feature you’ve seen time and time again; searching for “How tall was André the Giant?” results present a simple answer with his height – 7’4″ by the way – rather than a series of links to articles and websites containing a reference to its dimensions.

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

When these answers are easy to find, users avoid contacting customer support or spending valuable time sifting through unstructured content to arrive at an answer. It also avoids the worst case where the customer actually leaves the website to ask Google their question and possibly be directed to a competitor or third party site with questionable intentions.

It is important to remember that the quality of research today is not measured in silos. A customer is not going to compare individual brands based on their search; instead, the best search experience is now considered the norm for everyone. When Google, Amazon, Apple, and other experienced leaders make it easy to get the right answer fast, we wonder, “Why can’t all brands make it easier too?”

When answers to questions are made available, it also allows knowledge to become more accessible. But what is discoverability?

While findability provides usable answers in context, discoverability means that users can more easily find information that is not immediately sought after. Again, creating knowledge graphs can provide context for recommended content that understands a user’s intent and offers other relevant information to enrich their experience.

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

Create 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 isolated just to Google; it is accessible to any brand wishing to use it. Setting up a knowledge graph-based search system is an endeavor a brand can undertake, tailored to the products, services and information resources used by the company. Building this better search system consolidates enterprise knowledge by connecting disparate information systems into a usable engine that works for both customers and support teams.

With analytics, support, and experience managers, managers can examine common search queries to identify pain points throughout the customer journey. A knowledge graph-based system complements this information to form a powerful knowledge management tool. Businesses can analyze customer engagement and sentiment through search analytics, while gaining access to a scalable content infrastructure that can quickly fill and close knowledge gaps. This level of actionable information is invaluable in improving the overall customer experience.

Brands invest heavily in content. Knowledge graphs make it the most actionable version of itself, enhancing resources so that answers are findable and deeper insights are discovered.

Joe Jorczak is Head of Industry, Service and Support at Yext.