For years, brands have assessed the return on investment of customer experience automation by asking three questions:
What does it cost to serve a customer today?
What could it cost to serve a customer tomorrow?
What is the investment price to get from A to B?
With these questions guiding the way, we’ve seen a few prevailing investment philosophies emerge over the last decade. Foremost among them, many brands have chosen to target high-volume, low-complexity use cases with automation — think rewards account password lookup or order tracking. Meanwhile, low-volume, high-complexity queries have largely remained the domain of live agents. The thinking here is that high-volume, low-complexity use cases provide the necessary scale to deliver meaningful ROI more quickly. Not to mention, they’re less risky from a customer satisfaction perspective.
In the artificial intelligence era of customer experience, the traditional formula for automation investment suddenly looks a little different. The conversational skills of modern generative AI separate it from many traditional forms of customer experience automation. Thanks to these improvements, AI can now handle higher complexity tasks without significantly increasing implementation costs or sacrificing customer satisfaction.
For starters, this might look like using a chatbot or agent assist tool to handle a wider variety of customer queries — not just order lookup, but questions about the product itself. First movers, like Target, Walmart, Amazon.com, and eBay, are already starting to realize the potential of generative AI to expand how their chatbots and employees interact with customers.
However, thinking about generative AI as simply another tool to help reduce inbound customer support costs is a missed opportunity.
To illustrate what I mean, let’s look at a hypothetical example.
Let’s say your retail contact center has crunched the numbers and discovered each customer interaction costs your business about $5. By automating the first 30 seconds of preliminary customer identification and problem identification with an IVR solution, you get that down to $4. Then, AI-enabled conversation prompts and agent-assist features help speed up each interaction, getting your cost per conversation all the way down to $3.
Saving $2 per conversation is a big deal — there’s no minimizing that. But I can’t help but see the $3 you’re still paying every time a customer reaches out.
Organizations are just beginning to point AI at this challenge, but the early returns are impressive. Termed "conversation intelligence," the idea is that past customer interactions can help predict future ones. By better understanding which topics and problems are trending, brands hope to find new ways to solve them proactively.
Here’s how AI makes conversation intelligence strategies possible:
First, the natural language skills of modern AI models enable organizations to process, transcribe and summarize all conversations taking place across the entire customer experience, regardless of channel or medium.
Second, advanced AI models can be pointed at this conversation data to search and analyze them for trends and insights.
And finally, rapidly improving access to data visualization via AI-enabled dashboards helps close this loop, bringing real-time insights to the people who can act on them.
You can imagine how trending topics hidden deep within terabytes of conversation data could help inform new strategies to get out in front of some inbound customer interactions — like known product issues or down websites.
For example, let’s say fictional electronics brand ABC is seeing an uptick in calls about a product that has started to overheat in hot summer weather. Now ABC can fix this product issue and create an outbound email campaign that notifies customers about how they can get an improved replacement sent out to them for no extra cost. By taking this approach, ABC keeps more of these soon-to-be-disgruntled customers off the phone lines. Better yet, it shows ABC’s customers it values their time and loyalty.
In a hypercompetitive retail marketplace where competing products and services are more similar than they are different, thinking outside the box to control contact center costs and deliver proactive customer experiences is one way for brands to regain their competitive edge.
Tom Lewis is global leader, CX transformation, at TTEC Digital, a global leader in customer experience orchestration, combining technology and empathy at the point of conversation.
{Categories} _Category: Takes{/Categories}
{URL}https://network.napco.com/total-retail/article/think-outside-the-box-to-maximize-genai-investments-in-the-contact-center/{/URL}
{Author}Tom Lewis{/Author}
{Image}https://www.mytotalretail.com/wp-content/uploads/sites/14/2018/06/automated-customer-service.jpg{/Image}
{Keywords}Artificial Intelligence (AI),ChatBots,Contact Centers,Customer Service,Customer Service Reps,AI,Artificial intelligence,chatbots,Customer service,Customer support,GenAI,generative AI,live agents{/Keywords}
{Source}POV{/Source}
{Thumb}{/Thumb}