By Nate Sanders
The first tweet calling out the problem went unheeded by the social media team.
Simultaneously, an email highlighting the same problem was overlooked as it was just one of the hundreds of support tickets that came in that hour.
The first customer complaint call mentioning the issue came 15 minutes later, was marked low priority, and given equal attention as any of the other 75 calls that rep would take that day. The rep did not realize they were at ground zero of a bigger issue.
Then another tweet.
And a Facebook post.
An hour later a negative review went up on Amazon.
This is an everyday occurrence for brands. They hear from customers across multiple touchpoints. Customers who leave reviews and report problems are often left wondering, “does anyone read this?”
We can now confidently say “yes.” The promise of customer experience AI to close the customer feedback loop is finally here.
AI tools like GPT-3 are capable of reading and understanding what an entire customer base is saying and becoming their best advocate.
Recently, a smart home product had a Wi-Fi connectivity problem. Typically, this problem would be identified by software bug reporting, but this was not an issue that the software engineers knew to capture in their system. Customer service reps might not recognize a pattern for days, or sometimes weeks. The AI recognized the issue much quicker. Bug reporting is instant. Through the intelligence of their AI engine, the product team discovered an anomaly via customer reports in real-time.
A problem that otherwise could have taken weeks to unearth was solved in hours.
The rapid response created a positive impression. Considering 68 percent of customers say they are willing to pay more for products and services that offer good customer service experiences, brands cannot afford to ignore what their consumers are saying.
And yet, every day, customers talk to brands, and it goes seemingly unnoticed. Leaving customers frustrated and disengaged while companies are unwittingly leaving themselves deaf to the true voice of the customer.
The avalanche burying the voice of the customer
The promise of businesses adopting the internet and social media was such that companies would finally complete the feedback loop internally, and the customer would finally be heard, and more importantly, understood.
Every year, organizations get a flood of signals—medium companies generate hundreds of thousands, large companies with tens—and sometimes hundreds of millions—of interactions. Those signals could be via support tickets, survey responses, reviews, call recordings, social media, and hundreds of other inputs, all of which come in as unstructured text.
With the recent wave of AI innovation, this information can finally now be measured and contextualized in an actionable way.
AI is a customer’s new best friend.
Unfortunately, with AI being so new to the mainstream, and the technology so misunderstood, far too many customers perceive AI to mean a colder, more mechanical, less personalized approach to customer service. The opposite is true. AI can help customers receive an individualized experience.
A few weeks ago, I was asked by a product executive to explain to customers that even though the company was going to integrate AI into its customer experience system, the company is still listening.
I replied: “You have never been listening – not to everyone.”
The company in question had five million customer conversations a year. They did not have the capacity to listen to everything that was being said, nor the capacity to understand it all.
Too often, those businesses cannot see the forest through the trees. There are simply too many diversified inputs; connecting the dots takes time. It’s a herculean task to differentiate actual problems from the noise.
Through AI, not only can businesses listen to everything the customer says, but now they can measure and quantify the information. They can get a fuller, more complete picture of their customer. Product executives can make decisions 6-7x faster. With the machines doing the interpretation and understanding, the customer’s voice is finally clearly heard. It is better for the customer in every single way.
CX’s new frontier
In 2020, 68 percent of customer experience executives said their companies were using AI, with a majority of those crediting the technology for improved customer interactions. By the end of 2022, that number was up to 75 percent and climbing.
But more does not necessarily equal better.
Smart customer-focused companies want to make data-driven decisions, but far too often the data they are basing their strategy on is incomplete, or worse, incorrect.
Most AI systems that analyze customer data require intense manual model training, which is time-consuming, delays results, and often leads to misinterpreted data. Insights are boiled down to merely surface-level keywords, without sentiment, context, or specific conditions.
The contextual features are vital because not every customer uses the same terminology to describe their problem. Under a manual tagging structure, one problem could have been described in dozens of separate ways, each of those unintentionally put into an overly generalized silo, even though they were separate and distinct issues, thus limiting a company’s ability to see the bigger picture.
That gap between what is said and what is heard leads to a disconnect between business and customer.
For a health and fitness company, old tagging systems mean getting topics like “treadmill” or “power.” But with the latest solutions, the product team can receive topics that are incredibly actionable, such as, “My treadmill Wi-Fi disconnects during my run.”
According to a 2022 Calabrio report covered by CMSWire, “79% of managers think they are meeting or exceeding customer expectations for response times, but just 45% of consumers agree. Additionally, 84% of managers believe they are meeting or exceeding customers’ needs to feel heard and understood by the brand, but only 45% of consumers agree.”
AI can be trained to understand nuance, not just sentiment. Machines can spot topics spiking or declining, see new opportunities, and stay on top of customer engagement minute-by-minute, positioning companies to resolve customer issues before they have a chance to fester. It also allows product departments to strategize based on predictive and long-term trend data to get out ahead of customer demands and desires.
A better bottom line
By improving their customer experience with AI, companies improve their bottom line. They can reallocate or cut employee hours spent dedicated to tagging and manual customer service tasks. Weekly and monthly meetings that display untimely data are outdated. Companies increase their overall efficiency and empower their product teams to make faster decisions, reducing future customer complaints.
Most importantly, it increases customer satisfaction. Using the latest AI engine, the smart home product team knew there was a problem before customer service did. Their AI customer service engine was quick to recognize and highlight the issue and their engineering team immediately asked their customer service to let customers know they were aware of the problem and were working on it. A brief time later the customer service teams were informed they could tell customers there was a fix.
A happy customer is a repeat customer. And one that will have long-term brand loyalty.
Aren’t you glad you can finally listen to them?
About the author
Nate Sanders is the CEO of Artifact.