The digital loyalty loop: how AI can transform customer experience

AI can quickly answer simple customer queries while helping teams tackle more complex ones, improving customer loyalty but retaining a human touch

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Customer service teams are a critical part of any organisation – often quite literally the human face of the brand. Indeed, the difference between a happy customer and a disgruntled one is usually how quickly and effectively their queries are answered. If they end up stuck on hold or waiting for an agent to respond on live chat, for example, they may decide to take their business elsewhere. 

Considering that most customer questions have a simple answer, this is an unfortunate – and entirely avoidable – loss for the company. Indeed, that simple answer is probably part of the company’s knowledge base. But today’s busy consumers rarely have the time or inclination to trawl through FAQ sections or other brand documents. They are, however, increasingly happy to interact with an AI chatbot, which can draw upon company information to provide instant answers to their queries. According to research by Servicenow, 66% of UK consumers would use chatbots for an element of customer service.

Companies leveraging AI can provide 24/7 support to customers across the globe and answer inquiries instantly

This is changing customer expectations around the speed and quality of customer service. “Companies leveraging AI can provide 24/7 support to customers across the globe and answer inquiries instantly with chatbots that connect to personalised knowledge sources – all while freeing up agents to focus on complex questions,” says Dan O’Connell, CEO of the customer service platform Front.

AI’s current capabilities are focused on digital deflection, and it’s an effective tool for providing customers with quick answers in a self-service manner. But more complex inquiries will still require some good old-fashioned human intelligence to solve. “The ability of AI to solve complex workflows is challenging due to the lack of reasoning, the need for integrated systems and the understanding of training data needed to automate complex workflows,” O’Connell explains.

This means that businesses can’t solely rely on AI to handle their full customer support queue. “Those limitations lead to escalations – in the form of emails, SMS, direct messages on social media or calls – all of which require the human touch of your support team.”

Creating unique customer conversations

Despite their limitations, modern AI chatbots can be smart enough to mimic brand identity, values and voice, ensuring a consistent customer experience across multiple channels and touchpoints. They can even tailor chats by drawing upon a customer’s CRM data, language, location or other conversation data, creating a unique and personalised experience. 

Front, for instance, uses “dynamic variables” which branch chatbot flows based on chat visitor attributes, and personalise chatbot message content. Along with the default options, Front customers can set up customised dynamic variables to pull from website or contact data stored in Front.

“For example, if the chatbot senses the visitor’s browser language is French, you can route them to French-speaking agents,” says O’Connell. “Or if your chat visitor URL ends in ‘/pricing’, then you can direct visitors to a sales-oriented chatbot path. Similarly, if the chat visitor URL ends in ‘/troubleshooting’, then you can direct visitors to a support-oriented chatbot path.”

When a hand-off occurs due to a complex query, teams have instant access to all the details and context already gathered from the AI’s interaction with the customer, helping to minimise repetition and ensure a speedy resolution. Automatically tagging messages by topic or sentiment can also give agents quick context on the customer’s query, ensuring it reaches the right person as fast as possible.

“So much of the work that service teams do, such as escalations and handoffs, are moments of collaboration, and moments that – if done well – also address your customers’ expectations around speed and quality of service,” says O’Connell. “Establishing seamless handoffs for issue escalation will deliver a consistent experience for customers, regardless of the team member they interact with.

“Proactive service measures, like auto-assigning queries to available team members when someone is out, utilising snooze functions for when the teammate returns, and leveraging AI summarisation to quickly bring team members up to speed can also help to improve a common pain point in the customer journey.”

In the future, AI will even be able to route inquiries based on customer satisfaction. “For example, if AI knows that John Smith has the highest customer satisfaction rating for handling a particular issue, and is available, related questions can be sent their way – dynamically matching topic to expertise,” says O’Connell. “This is the future of routing: using AI to categorise the insights and data in your customer conversations, and matching the customer inquiry to the right customer expert.”

AI can provide accurate, automated summaries of a customer’s past conversations and relationship with the organisation to date, for example, so agents don’t have to trawl through endless back-and-forth threads and can reclaim their time. If a question is particularly tricky, AI can show how a similar question was successfully resolved for another customer. AI can even draw on the organisation’s knowledge base to serve up draft replies to customer emails and messages.

All of these capabilities help to free up agents’ time so they can focus on more complex issues, and deliver exceptional service with a high degree of empathy. “This combination of AI insights and human empathy means that brands leverage both to grow revenue and drive repeat business, increase productivity with automation, and get insights on customer experience and how to improve the business.”

Enabling collaborative working

To fully unlock AI’s potential, customer conversations must be accessible from one platform rather than siloed away in separate tools. This not only enables the collaborative ways of working that go hand-in-hand with exceptional customer service; it’s essential for accessing insights and analytics that span the entire customer experience.

The best way to utilise AI’s potential is to ensure that all customer conversations are managed in one platform that enables collaborative working

“The best way to utilise AI’s potential is to ensure that all customer conversations are managed in one platform that enables collaborative working,” says O’Connell. “That way all the AI insights and analytics are in one place and you can be sure it accurately represents the full customer experience.”

The integration of AI into customer service is ultimately part of a broader shift toward more customer-centric business models. “Leading businesses are transitioning from a model where the frontline team is solely responsible for customer experience to one where the entire company collaborates based on insights gained directly from customers,” O’Connell explains.

Front keeps these customers at the centre of every interaction by aggregating all conversations in a single view. “Teams work together, sharing context and understanding, so they can address customer challenges swiftly and at scale,” says O’Connell. “And every conversation sparks insights that create clarity and focus, and helps teams better meet and anticipate customer needs.”

Analysing the customer experience

By identifying patterns in historical data, AI can also help to identify emerging trends and predict future outcomes, such as customer churn risk. “All of the answers to your most critical business decisions lie within the conversations your support and sales orgs have with customers,” says O’Connell.

Through detailed analysis of resolution rates and unresolved questions, for instance, businesses can quickly identify areas ripe for improvement. Monitoring response times and resolution speed at scale can also help to improve performance over time. In addition, AI can prompt any necessary updates to knowledge base articles.

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This last point is important, as the customer experiences powered by AI will only be as good as the data sources it learns from. “A help centre has long been a support team’s number-one source of truth, but its importance is only growing in the age of AI,” says O’Connell. “For AI to provide your customers accurate answers, knowledge management needs to be a top priority.”

Despite the obvious benefits that AI and collaboration can bring to customer service, the most important thing is that customers can always access clear and accurate support with minimal effort, whenever they need it. 

“It’s that reliability, the end result, that builds trust,” says O’Connell. “Customer service should not be viewed through the lens of individual agents draining their queues. Customer service is all about collaboration, and doing so on behalf of the customer.”

So what does the ideal customer experience ultimately look like in the age of AI? “Whether customers seek a self-serve experience or require assistance with a complex issue, systems should be able to automatically resolve simple, repetitive queries and seamlessly route or transfer the more complex ones,” says O’Connell.

Ultimately he feels that: “The most effective way to address complex customer issues is through your human agents, with AI serving to assist these agents in making better and faster decisions on behalf of the customers.”

So while AI is undoubtedly changing customer service expectations and the way agents respond to queries, there’s one thing it won’t change: “Businesses that obsess over their customers and put the customer experience above all else are the ones that will win.”

For more information, visit front.com