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Customers prefer self-service: Here’s how to do it right

Customers prefer self-service: Here’s how to do it right

Self-service customer support systems are popular for good reason: customers want to solve their problems quickly and get back to their lives. You might think speaking with a live representative would be preferable, but those who prefer live calls are in the minority.

The quality of live customer support has taken a dive, and customers don’t want to deal with it. The human element of support has all but disappeared. Agents who are required to read from their script have replaced authentic conversations between people.

Self-service support has its challenges, but customers are willing to accept its limitations if it means not talking to a robot.

Accommodating the aversion to live calls isn’t the only reason to focus on self-service. Even if you’ve got the best live call center in the galaxy, you’ll need a self-service support portal; newer generations are demanding it. Millennials, for example, feel better when they resolve a problem without having to contact customer service.

Your self-service portal begins with intelligent insights

Supporting your customers means solving their problems and making them happy. This can include troubleshooting, replacing broken or defective parts, and giving them something extra when their expectations weren’t met.

When customers access a self-service portal, you can guarantee they’re looking for a specific piece of information or answer. The best way to help them find what they’re looking for is to increase the relevance of your search results.

To do that, you need to enable customers to search across your whole ecosystem of resources: your knowledge base, FAQs, YouTube videos, and documents stored on a file-sharing site. You also need a system that automatically fine-tunes results for each query. Coveo, a leader in intelligent search, refers to this as “intelligent insights.”

Superior self-service portals are powered by machine learning

You may have noticed some self-service portals asking, “Was this answer helpful?” This is machine learning in action. Your answer doesn’t go to the webmaster, it goes to the AI program where its relevancy is analyzed and incorporated into future results.

Your willingness to answer truthfully helps the computer “learn” how relevant that search result was to your search. If most people don’t find it useful, the machine will either eliminate it from the results or push it toward the bottom. Results that are reported as useful will be pushed to the top.

Coveo explains the role your community plays in fine-tuning intelligent search results, “You need to leverage the intelligence of your community members by using machine-learning technology to analyze precisely what each visitor is trying to resolve on your site, how they look for answers, and what content ultimately helps them succeed. With this data, a well-designed machine-learning solution can continually and automatically fine-tune the relevance of your community search experiences.”

Why traditional live customer service is dying

When implemented correctly, live customer support is your biggest asset. However, when your system is structured to minimize the amount of time your agents spend with customers, it will be your downfall.

After working their way through a tangled mess of automated selections, being transferred to multiple agents, and put on hold, it’s no wonder people suffer from “tech support rage.” The problem is caused by the cost-per-contact model, which limits the amount of time agents can spend with each customer.

Most agents don’t want to provide bad support, but they have no choice. In fact, a 2015 customer management survey revealed that 74% of companies have protocols that prevent their agents from providing satisfactory experiences.

This makes self-service the obvious choice for people who want a hassle-free solution. As a business, creating an AI powered self-service portal to support those customers will provide them with an easy way to get the value they expect.

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