The pressure on financial services organisations to provide an excellent contact centre experience is coming from all sides, and something has to change to keep standards high and customers happy.

What was a rising tide of customer calls to the contact centre became a deluge during the pandemic, as worried customers reached out for reassurance and help in confusing times. Increased reliance on virtual contact is something banks and building societies will have to factor into their operations from now on.

Customers have adjusted to limited branch availability and many have gotten over any initial reluctance to do business remotely. Now the challenge is to deal with a sustained increase in call volumes.

There’s a common argument to ‘get more agents’, but the economics are against this. We know that the typical costs of running contact centres are around 60% for staffing, 30% for facilities, and 10% for technology.

Currently, bringing in more staff would mean more training, which also adds to the bill. We also have to remember that the contact centre is getting more complex queries that would previously have gone to face-to-face contact in branch, and just adding more agents won’t necessarily do anything to improve resolution rates.

A better approach is to build the business case around ‘digital-first’. In particular, using artificial intelligence and machine learning - to resolve the more straightforward ‘self-service’ types of contact, and to support the agents you’ve already got to manage the more complex interactions.

But where do you start when putting together a business case for bringing in an overlay of digital tools to your contact centre? 

Switching to a digital-first approach can immediately reduce pressure on your human agents by giving your customers other options for problem-solving, such as messaging with self-service virtual agents. By offering self-service for simple customer journeys, you reserve your agents’ valuable time for more complex issues and save your customers from having to wait to speak to an agent. And, if the virtual assistant can’t solve the query, you can transfer the call seamlessly to a live agent, so the customer experience is protected from start to finish.

One credit card company recently cut its call volumes by 30%, just by introducing a virtual assistance and chat solution.

Often, knowledge in an organisation sits in silos, and the agent has to jump from one system to another to get answers for the customer. This stretches out call times, increases stress on the agent to get results, and can leave the customer irritated at the delay.

Instead, think about how you can use machine learning to bring together knowledge and build up an understanding of issues a customer is likely to call about so you can more accurately predict their needs - and suggest answers to agents. For example, when a customer calls on the day they receive overdraft charges, it's usually because the charges are more than they were expecting. What if the agent knew the customer had received that letter and had the facts at their fingertips?

A high street bank that was running out of contact centre capacity, but needed to support growth without increasing its staff, introduced a knowledge management tool with striking results. Its average call handling time fell by 67% and its rate of resolving issues on the customer’s first call rose from 70% to 95%. 

As calls to the contact centre get more complex, your agents need more expertise to solve queries – if you stick to the traditional approach where service quality depends on agents building up knowledge over time. It’s time consuming to train agents to the degree of specialism needed to satisfy customers and, with high levels of agent churn, keeping your workforce fully trained becomes a significant headache.

Adding a machine-learning-driven guidance solution to your contact centre can help you cut training times and costs. Instead of learning a whole series of complex processes and product details, all agents need to do is get to grips with one system that predicts and provides what they’ll need. Now all agents can have expert knowledge.

One high street bank was able to cut its agent training time from 10 weeks to four and found that the number of specialist areas an agent could handle went up as well. 

The future of our financial services clients’ contact centres depends on the smart application of innovative AI technology.

That’s why we’re working with eGain, a specialist in award-winning customer engagement automation platforms. Together, we help financial contact centres solve key challenges around customer and agent experience that affect the bottom line.