What’s preventing you from realizing the dream of unified customer service?

Customers and agents struggle with your contact center

Your customers are frustrated trying to get answers out of you. Ask them and they’ll tell you. Whether it’s by voice, live chat, email, or through bots, they’ll tell you they get stuck trying to reach you one way and then try another and another as their frustration grows.

When I was managing contact centers, I found that a third of our customers tried to reach us on multiple channels before finally getting their issue resolved. That third of customers doesn’t walk away happy, and a lot of their stories end up on social media.

If you ask the customer service agents, they’ll tell you that they’re frustrated too. They don’t have what they need to help customers the first time. Their main obstacles are (1) that they don’t have enough information about the customer’s intent, or (2) they don’t have enough training on how to resolve the issue.

Nobody likes talking to angry customers. Agents are just trying to do their job, but the technology is holding them back when it should be solving their problems.

AI-powered unified view across channels is the solution

When a customer has an inquiry about a product, they’ll try order tracking or some other self-care option first. If that doesn’t work, they’ll move to the next channel: dial the support number, engage in a live chat, write an email, and so on. When they engage with a human, the first question usually prompts the customer to repeat what they said on the previous channel. Now the frustration starts to build.

As the customer goes from channel to channel, this scenario keeps repeating because each channel is part of a different solution. Customer frustration rises. Agent frustration rises. And business frustration rises because each touch from each channel costs the company money. The only one who’s happy in this situation is the contact center vendor cashing in on all these repeated inquiries for the same intent.

The answer: put all channels on the same solution so that data is centralized. Then, no matter how many channels the customer uses to make the same inquiry, the context is carried and viewable by agents in an actionable way. This is where AI steps in — to perform cross-channel analysis against the data in your backend systems so agents have the context they need to give customers what they need.

This is why AI that is specific to one channel solution can’t solve the underlying context problem. Only when everything lives on the same platform can your AI see the full landscape and surface the best information for agents and bots.

Conversational AI and agent assistance work hand in hand

Chatbots can solve your scalability problem, but you need to understand the relationship between bots and agents so you can empower both to work in combination with each other.

When you first deploy bots, the percentage of customer inquiries they can handle will be low. Most cases will still go to agents.

But good conversational AI gets smarter every day. It learns which responses and reactions result in high CSAT scores, and it gives insights to management about which customer intents are missing from your strategy.

As your bots mature, the percentage of small- to medium-complexity inquiries they can handle increases. In parallel, the complexity of the cases your agents handle also increases.

Soon, your agents only handle the highest complexity cases. So while you might need fewer agents, these agents need to be superpowered. This is where AI steps in again.

When you have full context in a centralized location managed by smart AI, you can automatically learn which responses from agents produce the best CSAT scores and then suggest those responses to agents handling similar cases when the conversation calls for it.

You can digitize your processes and then surface the right steps to guide agents through common scenarios, so an agent’s success doesn’t rely so heavily on memory and training. The use cases for AI-powered agent assist go on and on.

If you do this right, then Average Handle Time (AHT) becomes the wrong KPI to hold agents accountable for. Since they only handle the most complex cases where the customer wants personal attention, it doesn’t matter how long it takes to make the customer happy. Just make the customer happy. As a business, it’s most important to empower agents to have the right tools and information to provide the best experience.

The future of Customer Care

The future of Customer Care is proactive, where issues are solved before they become catastrophes. I see the technology becoming 100% proactive, 0% reactive. Service agents receive cases from internal systems telling them something is wrong, not from customers. This is the nirvana state.

If your delivery is delayed, you know it as well as the customer waiting for it. So why not reach out first? “We’re sorry your order is late. We’re having some difficulties on our end. Your order will arrive tomorrow. Here’s 15% off your next purchase.”

If your customer’s 4G service isn’t providing the right speed, you and your suffering customer know at the same time. Don’t wait for the anger to flood your live chat queue. Reach out first. Text them a notification: “We are aware of your problem. Our technicians are working to restore your service.” And then send updates until the problem is fixed.

You can be ahead of the flood. You have all the data required to make it happen. There’s no reason that 0% inbound inquiries can’t become a reality.

See how the vision is progressing so far.