Improving Self-Service Error Rate with Generative AI

By Machielle Thomas

Customers today both expect and prefer self-service options — like a help center or knowledge base, FAQs, automated phone system, or AI-driven chatbot — when it comes to customer service.

Data from Zendesk shows, “69% of customers want to resolve as many issues as possible on their own, and 63% of customers always or almost always start with a search on a company’s online resources when they have an issue.”

Self-service resources are often more efficient and scalable for companies, too. But that leaves customer service teams in search of a way to monitor and measure the efficacy of those resources and ensure a seamless customer experience.

Enter the Self-Service Error Rate.

What is the Self-Service Error Rate and What Does It Measure?

The crucial caveat is this: customers prefer self-service options… when they work. Above all else, customers prefer to have their issues resolved as quickly and easily as possible. 

The Self-Service Error Rate is one metric companies use to help measure whether or not their self-service resources are delivering on that. Expressed as the percentage of total visitors to self-service resources who face at least one error, it measures how frequently customers run into errors like technical glitches, confusing or incorrect information, or user interface issues when using self-service resources.

It’s good to continuously monitor this number, but you’ll want to pay particularly close attention anytime you implement new self-service tools, update existing resources, or see an uptick in customers complaining about errors.

It’s tough to benchmark Self-Service Error Rate since it can vary widely across industries, customer bases, and self-service tools. Striving to simply reduce that number — whatever it may be for your business — is always a good place to start.

Why is Self-Service Error Rate Important?

Self-Service Error Rate is an important metric for the whole company, from frontline customer service reps to leadership — and across other departments, like IT and UX, too.

  • For customer service reps, understanding the Self-Service Error Rate can shed light on common customer pain points and add vital context to tickets and escalations.
  • At the team level, a high Self-Service Error Rate may indicate a need for improved help center content, for example, or a more intelligent AI-driven chatbot. Customer service teams can work together to improve self-service resources, reducing demand for live support and freeing agents up to focus on more complex customer issues.
  • At the organization level, self-service error rate spells the difference between a seamless self-service experience — yielding higher customer satisfaction, retention, and a more efficient customer service operation — and an error-prone process that turns customers off, increases the burden on live support agents, and leads to higher churn.

A Real World Self-Service Error Rate Case Study

Home Depot, a leading home improvement retailer, saw a bump in customers complaining about errors in their self-service system. Customers struggled with unclear project details, ineffective FAQ search, and glitches in the checkout process.

The company revamped the website, fixed the search functionality, and put in place a real-time error reporting solution to stay ahead of similar issues in the future.

As a result, Home Depot saw a significant reduction in Self-Service Error Rate, leading to higher customer satisfaction and a reduced number of support tickets making it to the live team.

Improve Self-Service Error Rate with Generative AI and Automation

Knowing your company’s Self-Service Error Rate is just the beginning. Identifying and implementing fixes to improve that number and deliver a better customer experience overall is the crucial next step.

At Forethought, our suite of generative AI and automation products is designed to help you do that.

  • Continuously Detect Errors in Real-Time: AI can detect, categorize, and triage errors, highlighting your customers’ most common problems.
  • Use AI to Automate Troubleshooting: AI can guide users through troubleshooting steps, reducing user errors and minimizing escalations to live agents.
  • Leverage AI-Powered Predictive Analytics: AI can predict potential errors based on real-time user behavior, allowing for proactive actions to prevent errors.
  • Optimize the Self-Service User Experience with AI: AI and automation tools can analyze user interaction data to optimize the design and flow of self-service tools, reducing user-induced errors.

Our new Autoflows solution for Solve is one way you can drastically lower self-service errors, while supporting your team to improve self-service resources and freeing up live agents to provide more complex support.

Check out the demo here to learn more about Autoflows!

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Interested in generative AI for customer support? Check out this guide to learn about the 3 key pillars you need to get started.

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