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!
