Editorial
The Gist
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Smarter digital containment. Advanced AI chatbots now handle complex inquiries and help contact centers saving costs and improve customer service experiences.
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Insight-driven deflection. AI-powered conversation intelligence identifies issues proactively, reduces call volume and enhances customer satisfaction.
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Effortless CX focus. AI in customer service reduces friction and makes it easier for brands to deliver seamless, loyalty-building experiences.
Despite the hype and massive investments in generative AI, most businesses aren’t yet realizing value from their AI projects. Forrester found that while 80% of businesses recognize AI’s strategic value and expect it to increase in the next 12 months, less than a quarter say they can realize AI’s value today. Gartner reports similar findings with 49% of those surveyed saying that demonstrating AI value is their biggest obstacle.
Experts agree that one of the main reasons businesses aren’t seeing a return on their AI investments is because they’ve rushed into adopting the technology without first defining their use cases. It’s a little like a homeowner buying an expensive power tool without having the expertise to use it effectively.
In the customer experience (CX) space, we’re seeing more caution in adopting generative AI, especially when it comes to customer facing interactions. This is because brands are protective of their customer experiences, and they don’t want to jeopardize their hard-earned CX equity.
However, when the right use cases are identified upfront, CX leaders have the greatest potential to see strong returns on their generative AI investments.
With the correct match between technology and customer experience design, CX leaders can realize more savings than with nearly any other contact center investment. The key is pinpointing exactly where AI in customer service can save money, increase productivity and improve the customer experience.
AI Is Changing ROI Calculations in the Contact Center
Traditionally, CX leaders have thought about ROI by asking three questions:
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What does it cost to serve a customer today?
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How much less could it cost to serve a customer tomorrow?
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What is the investment price to get from point A to point B?
In practice, CX leaders then sought to automate high-volume, low-complexity customer interactions while routing the remaining low-volume, high-complexity interactions to live agents. If they got the balance right, CX leaders would see incremental reductions in labor costs.
That's old math. With generative AI, we can move beyond traditional use cases and allow technology to solve much more complicated problems and even drastically reduce the need for customers to reach out to your brand.
Related Article: Is This the Year AI Dominates the Call Center?
2 Use Cases for AI in Customer Service That Generate Fast and High ROI
Increasing Digital Channel Containment
AI in customer service, like chatbots, has become very good at handling straightforward or simple questions like, “What’s my bank balance?” or “What are your holiday hours?” Now with AI’s natural language understanding (NLU) capabilities, they’re becoming able to handle even very complex questions.
Recently, I put this type of next generation chatbot to the test. I had received an email from a financial institution where I have a retirement account. In the email, the institution stated they would be moving funds from certain accounts into clients’ banking accounts. Immediately alarm bells went off because moving retirement funds into my bank account would trigger a taxable event. Before I called the institution to make sure this wouldn’t happen, I decided to give their chatbot a try.
I presented my problem to the chatbot in exactly the way I would state it if I were speaking with a human. I didn’t try to guess at the right keywords or phrase my questions in a bot-friendly way. And guess what? The bot understood my needs perfectly and gave a thoughtful and comprehensive response based on well-curated knowledge documents.
How is this possible?
With generative AI and NLU, CX architects no longer need to create extremely detailed decision trees reliant on keywords and pre-drafted chatbot responses. Instead, AI can recognize intent and pull in elements of the company’s knowledge base to create personalized responses that directly answer the question asked.
Containing more customer interactions within digital channels is a clear cost saver — but only if the customer has an effortless experience within those channels. Let’s not forget that customer satisfaction levels are currently at an all-time low despite digital interactions being at an all-time high.
My own experience with my financial institution’s chatbot was the very definition of an effortless experience. Now if I had attempted that same interaction even two years ago, the outcome would have been very different. I have no doubt that the chatbot would have failed to understand my question and routed me to a human representative. Until very recently, the technology just wasn’t there.
Technology is advancing quickly, but, more importantly, so is CX strategy. That’s really the crux. Without a solid CX strategy, even the most robust technology will fail to deliver the effortless experiences customers demand.
Improving Insight-Driven Deflection
The cheapest customer call is the one that never happens because:
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Nothing has gone wrong.
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You’ve already identified a problem and have fixed it or reached out to your customers to inform them of the issue.
You clearly can’t eliminate all outreach — nor would you want to entirely, since giving your customers a tangible connection to your brand is such an important value driver. However, you can reduce the number of interactions by identifying issues and mitigating them before they become bigger problems. You can do this through conversation intelligence.
Conversation intelligence is the process of collecting customer feedback and conversation data from all customer support channels and integrating it with first- and third-party data to drive deeper customer insights using an algorithmic approach along with generative AI. With conversation intelligence, you can create an always-on predictive analytics engine that helps identify key product or service issues before they can damage brand reputation.
Let’s look at an example of conversation intelligence in action. An electronics company’s contact center sees an unusual uptick in calls. With conversation intelligence, the contact center manager understands very quickly that the majority of these calls are related to a specific product that’s suddenly not working as expected.
With this information, the contact center manager can escalate the issue to product development where the team can immediately start working on a fix. The company can also alert customers who have bought the product and give them proactive information about how they solve or mitigate the problem before they call.
By identifying this issue faster and taking steps to fix the problem, the electronics company will drastically reduce the numbers of frustrated customers calling the contact center. Even better, through proactive outreach the electronics company will show their customers that they value their time and loyalty.
Related Article: The Customer Service Recovery Paradox: Turn Angry Customers Into Your Company's Best Friends
Effortless Customer Experience Must Be Your North Star
I like the above two example use cases for their clear ROI, but more importantly I like them because they show how AI in customer service reduces friction in the customer journey. Eliminating friction — in other words, creating an effortless customer experience -– must be every brand’s goal.
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Advanced digital channel containment and conversation intelligence are two effective ways to apply AI in customer service for value realization without increasing customer effort, but there are many, many more use cases. As brands explore where AI can deliver value, I’d urge them to identify the CX problems to be solved before buying or developing the AI solution. And remember: Always keep effortless CX as their North Star.
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