Using AI to improve the contact centre experience

There is no questioning the important role that contact centres play in the success of a business today. As a direct line of contact between companies and their customers, the experience someone has with a contact centre agent can be the difference between them leaving or staying for the long-term.

CSA-Printstock/iStock

During times of economic uncertainty, as customers look to trim their spending, the quality of service that an agent delivers becomes critical. Every interaction – at every touchpoint in a customer’s journey – is about building and strengthening the relationship a customer has with a business to gain their trust and loyalty.

And this goes both ways – employees will only be able to provide a good customer experience if they feel similarly valued by their employer. Here lies the challenge – and similarly the solution – for businesses.

So how can they reprogramme to prioritise experience across the business?

Investing in your employees

Current market dynamics have forced businesses to focus on understanding what matters most to customers so they can develop long-term loyalty. But in doing this, they are failing to invest in their most important asset – their employees.

The pressure is on for contact centre agents who are on the front lines remedying customer pain points day-in and day-out, and this is leading to a rise in agent burnout and attrition.

Qualtrics research shows more than one-third (38 per cent) of contact centre agents believe they are not set up for success in their role, and nearly half (46 per cent) do not think their leadership invests enough in their team or function.

Business leaders must equip their employees with the tools and training they need to be successful in their roles.

When agents have access to the right tools, they’ll deliver more efficient, personalised and empathetic customer service. This will lead to higher employee engagement, along with improved customer loyalty by delivering on the level of CX that people have now come to expect.

Optimising the modern contact centre

At the same time, customer service is becoming more challenging. But, implementing the right solutions can make a tangible impact on agent performance, retention, and business outcomes.

The modern contact centre also has an array of technological solutions it can deploy to improve contact centre experiences for both agents and customers.

One such solution is the implementation of artificial intelligence. Whether it’s augmenting intelligent chatbots, or implementing natural language understanding (NLU) and machine learning capabilities to analyse customer interactions, AI is already transforming the contact centre and assisting agents in their day-to-day roles.

For example, AI-powered solutions can make agents more productive and efficient at their jobs by automating the mundane parts of contact centre work. It also enables businesses to streamline processes at a previously impossible scale.

In doing so, this frees up more time to focus on the tasks that require a human and empathetic touch, allowing agents to deliver more personalised experiences and achieve better customer satisfaction.

In today’s business environment, where omnichannel interactions are the norm, AI-augmented technologies can also analyse vast amounts of customer data across traditionally disparate customer interaction sources.

This provides a more holistic view of the customer and offers unique insights and solutions at speed to agents, allowing them to solve issues efficiently.

More specifically, here are three ways that businesses can consolidate customer interactions and feedback to improve contact centre experiences:

Customers don’t always articulate their problems in a way that is understandable for even the most empathetic of agents. This can be a challenge, especially when considering the number of interactions they have each day.

However, AI can track every interaction across a multitude of touchpoints – including speech and text – and recognise customer effort, emotion and intent. This provides agents with a better understanding of who they are talking to and how they are feeling in a specific moment.

It also allows them to identify who is in most need of their assistance so they can prioritise customers that are at a higher risk of ending the interaction with no resolution.

For example, one of the largest banks in the UK leverages this AI-based NLU to better identify and serve vulnerable customers, at a time when such vulnerabilities are rapidly increasing due to the cost-of-living crisis.

As the quality of customer experience delivered by an agent has a direct impact on loyalty, contact centre leaders can leverage technology such as AI and conversational analytics, to better understand agent performance and how that impacts the customer experience.

This allows them to identify the key drivers of both positive and negative interactions, so they can create a ‘quality score’ to help build an accurate picture of how well each agent is performing against this target, which areas they excel at, and which ones they need more support with.

Not only does this help deliver more consistent quality across multiple agents and touchpoints, but it also helps agents to feel supported and grow further in their careers – allowing them to deliver better, more effective customer service as a result.

A leading global publishing company automated their quality management (QM) processes by scoring all contact centre interactions, rather than relying on a manually scored small sample.

This augmented QM process not only increased agents’ trust in how they were being scored, it also enabled more meaningful agent coaching, invaluable root cause analysis, and deeper customer insights.

Historically, contact centre agents spent a lot of valuable time on tedious, manual work such as contact dispositioning, writing up post-call summaries or logging follow-up actions after every call.

These tasks were responsible for driving up call wait times and adding hours of additional expenses to the business. Not to mention they are extremely subjective and difficult to analyse for insights.

By implementing AI and machine learning capabilities, businesses can effectively free up several hours’ worth of transcription and analytical work per week and, at the same time, turn that data into meaningful insights that agents can use to improve contact centre experiences.

In particular, automated call summaries deliver instant, accurate call recaps that include all the details discussed during a call, including why a customer called, how it went, whether the issue was resolved and how much effort was needed to reach that resolution.

This allows agents to effectively understand customer emotion, effort, and sentiment throughout the call so they can easily and accurately identify next steps.

By analysing the voices of both the customer and the agent during a call, AI can also provide real-time guidance to the agent as a call progresses, optimising the CX and enabling agents to improve at the same time.

Many companies have been able to justify the spend on their conversational and speech analytics initiatives simply through the automation of post-call work. The ROI of such automation is immense but still only represents a fraction of the business value that such AI-based solutions can offer to contact centres.

AI holds the key to a positive CX

Ultimately, these AI solutions play an essential role in providing agents with useful insights which can then be actioned to deliver quicker, more efficient responses to customer concerns.

They can also help boost agent empathy – a key driver in improving contact centre experiences and meeting customer expectations – ensuring that both employees and customers are kept motivated and loyal.