Building a customer service dream team

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The Dream Team.

The phrase sums up the idea of bringing a collection of the best individuals in a given field together to deliver amazing results. While this description might have been applied to many groups in the past, search the internet and the first result will probably be the 1992 United States men’s Olympic basketball team.

The Olympics were originally about amateur athletic competition. Those rules started to change in the 1980’s, permitting professional athletes from several sports to participate. Eventually, this also included United States basketball players from various National Basketball Association teams. This set the stage for bringing together an elite squad of basketball stars for the 1992 Olympic Games and what Sports Illustrated magazine famously christened The Dream Team, a crew of players who proceeded to dominate the court and bring home gold.

Imagine performing any task at a level far superior to competitors. Now, picture doing that in customer service. It’s no dream, it’s possible, with the right blend of people and technology.

Self-service channels

Rapidly responding to upward and downward trends in customer inquiry volume can be challenging–that is, if customer service is relying solely on agent-staffed channels. On top of that, reliance solely on agents entirely misses customers’ desire today for self-service.

For the last several years, Forrester has repeatedly pointed out the rise and importance of self-service. Options such as chatbots, knowledge management, online communities, and self-service process automation (e.g. registering a product, updating an address, or changing payment information) lead the pack. Investing in these key capabilities are critical to building a customer service dream team.

Skilled and well-equipped staff

Self-service alone is not enough, though. Be it a new problem, a complex issue, or a customer who prefers live interaction, skilled agents are another critical component.

Hire well and train them often, since they are the face of the company. Seek those that are well-spoken and have demonstrable empathy. Provide them with knowledge of the company and its products and services. With self-service taking most of the “easy” cases, they should be able to handle the complex as well as troubleshoot the new and unknown issues that come in.

Agents should also be provided with powerful tools to do their work. It’s not uncommon for customer questions to involve looking for answers in multiple systems–why did the financial system deliver an incorrect billing statement, what does the order processing system indicate is the order status, etc. While that information might need to reside in those other systems, it doesn’t mean the agent should be forced to switch between multiple systems to find an answer. Bring everything together into a single pane of glass so they aren’t forced to jump from system to system.

Don’t stop there. Observe how agents operate, and organize screens and fields to reflect their daily work. By minimizing clicks and scrolls, you’ll not only make them more efficient but also reduce their frustration.

And when agents solve a new, previously unseen issue, don’t let it stop there. Make it easy for them to submit these solutions as potential knowledge articles. Route those ideas using workflow to a dedicated group of writers and editors, who can revise, validate, and publish the article. In this way, the article can now assist customers searching the knowledge base or the chatbot can suggest it. Now if only technology could also ensure agents assisting live customers don’t miss a possible solution…

Machine learning

That technology does exist in the form of machine learning. By analyzing months or more of case history, machine learning can help identify potential solutions to agents as they assist customers. Be it knowledge base articles, answered questions in the community, or similar closed and solved cases, machine learning can continually make suggestions to agents that speed customer resolutions.

That’s not all. Machine learning can assist with mundane, repetitive tasks previously performed manually by staff, like case routing. Cases created online by customers can be prioritized, categorized, and assigned to the appropriate agent for addressing, also accelerating resolution time.

Data-driven insights and actions

To truly have a pulse on what’s going on in customer service, robust real-time analytics must be available. This is the only way to identify trends like high-volume topics as they are occurring. These insights lead to actionable decisions: is more agent training necessary, should a knowledge base article be fast-tracked, or does a new conversation need to be added to the chatbot (or perhaps all three)?

Beyond reacting to trends, analytics is also the gateway to next-level customer service: proactive service. It requires knowing customer particulars such as what products and services they use, how to contact them, plus other details. It involves using that data to identify customers who may potentially be affected by newly-discovered issues. And it means informing them quickly and keeping them aware of the progress towards solving the issue.

Dream and deliver

Imagine bringing together a handful of skilled athletes who have never played together and going on to dominate the Olympics, playing big to the media, and capturing the hearts and minds of citizens. The brief period the Dream Team was an on the court was an amazing period. And it’s the type of story that need not be limited to sports nor short-lived.

Amazing customer service means hiring and training the right people. Amplify their work with a powerful customer service platform that makes their job easy. That same platform should provide the self-service capabilities customers expect, drive customer resolutions faster with machine learning, and provide analytics to continually measure and improve customer service in real-time. A customer service dream team is possible!

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