Better Automated Customer Experiences Hinge On Human-AI Synergy During Pivotal Moments Of Trust 

Raghu Ravinutala is CEO of leading CX automation platform Yellow.ai, writing about customer experience (CX), AI, conversational AI and more.

Automated customer support, whether you’re calling in or interacting with a chatbot online, can be frustrating, to say the least. For brands, this is a serious issue that has recently been magnified as more consumers have flocked to digital channels during the pandemic. A good customer experience requires much more than just the latest enabling technology. It requires brands to maximize opportunities around every “moment of trust”—those pivotal instances when faith in a company can either be further strengthened or instantly broken.

A business can spend years building a customer’s loyalty only to lose it with one bad interaction. These moments of trust occur when a customer is making a purchasing decision, getting technical support or receiving marketing messages. Brands recognize there is a problem, which is why they spend over $1.3 trillion on 265 billion customer service calls each year.

According to Salesforce, 92% of consumers say that a positive customer experience makes them more likely to come back for repeat business, while 78% reported that a phenomenal customer support experience would result in their giving a brand a second chance if they were initially unhappy with a product purchased or service experienced beforehand.

In short, quality support is invaluable.

The Difference Between People And Technology In The Help Center

One of the biggest problems with automated support has been that much of the technology available has been extremely linear, built on siloed systems and lacking thoughtful integration of human agents. Conversely, there has long been a persistent myth that humans are always superior to machines; this has pushed businesses to continue throwing money at hiring human agents when the data shows that customers are clearly not happy—and neither are the human agents. Deloitte reported nearly a decade ago that the bigger a center was, the less efficient it became at keeping agents, with call centers with over 500 agents averaging more than 50% turnover annually.

The truth is that machines can assimilate large volumes of data from hundreds of systems in an instant—a task that would take days for a human to achieve. These are systems where all the data about your customers and their interactions reside, making machines better at processing a 360-degree view of a customer’s ongoing relationship with your company and connecting the dots for the best response. Humans, meanwhile, are great at delivering empathy. For example, when a customer is looking for Covid protocols and assurance, a real person can connect in a way that the most advanced AI can’t sincerely replicate. And even if they can, humans may not quite be ready for it.

But we’re also living at a time when we expect services to respond immediately and on demand. If you want a car, you pull up Uber or Lyft on your phone and immediately have a driver located and on the way. If you want to order in for dinner, you pull up DoorDash and get that same immediate response. What the leading brands born in the current tech era have in common is that their CX is designed to interact with backend systems and serve customers with end-to-end fulfillment. This is what today’s consumers now expect.

Delivering On Today’s Consumer Expectations

So, how can we deliver on today’s expectations? The answer is leveraging what I call “dynamic AI agents.” Any bot can automate basic interactions against an FAQ. This isn’t very intelligent AI as it often pushes customers to hit the same wall over and over again until all they want to do is get a real person on the line to help with a simple request. This can be intensely frustrating for consumers and, as a major loyalty-killer, incredibly expensive for companies. People want dynamic support that can connect the dots across decentralized systems for better responses. They want it to gauge their frustration and connect them to a human agent before they lose their minds. They want bots that respond more like humans.

Today’s brands function across multiple geographies and serve customers with different personas and needs across channels. Over the past decade, brands have moved from engaging customers across two to three channels to over 50. Meanwhile, consumers expect brands to be more proactive and responsive on every channel, delivering a bespoke experience that is truly omnichannel, and even multilingual. And this is exactly what dynamic AI agents are doing.

For organizations interested in moving forward with dynamic AI agents, the first step is to have a strong understanding of your customers.

This includes grasping their journeys through the marketing funnel and building their in-depth profiles, noting how they prefer to interact and what they likely will want from engaging with your brand. This will help narrow down your business’ unique needs for the implementation and provide a checklist to guide your selection of the best vendor.

Once decided, it will be critical that you drive the deployment across all functionalities and modalities—customer support, marketing and sales are all connected to the user experience. Continuing to silo data and channels can be a major roadblock that greatly limits an organization’s capability to deliver the cohesive, personalized and immediate experiences that today’s consumers expect. Breaking these silos will also provide more effective data insights, which you will want to constantly apply to further improve the deployment. Once satisfied with your initial implementation, it is then time to scale the use cases, channels and language capabilities to serve all of your customers.

The technology that brands need is already here. To achieve the level of service being deployed by some of the biggest names in the world, the trick’s really to first start with the experience you want for your customers, identify those critical moments of trust, and then work your way back to the solutions that can maximize the value of your data, drive better interactions across your channels, and utilize your human agents where they shine the most—being human.

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