Getting on the front foot with customers with AI conversation monitoring – EdgeTier CEO and Co-Founder, Shane Lynn, discusses
The contact centre is no stranger to AI. The sector was fast to embrace chatbots and self-serve solutions to help automate the simpler queries so that human agents can focus on the more complex situations facing customers.
However, there are still many more manual processes underpinning the management of the contact centre. When it comes to understanding trends for customer enquiries, almost all contact centres still rely on manual labelling of content, or agents raising the alarm when they happen to notice a trend is arising.
This approach might work in a small contact centre, where the customer service manager could walk between agents and ask what they were hearing. However now, many customer service functions operate on a global scale with thousands of agents, many of whom work from home, speaking in multiple languages, working across different channels (i.e. email, phone, WhatsApp), and dealing with customers across multiple channels.
When dealing with a fast-moving, frantic environment, it is impossible to manually monitor thousands of simultaneous customer interactions to spot the small number that signpost how in 20 minutes you could have an explosive issue on your hands.
The cost of being on the back foot
The inability to identify these trends early has a real cost to modern businesses.
Firstly, contact volumes can be reduced when issues are identified early and proactive communications to potentially impacted customers are made. Take, for example, a travel company that starts to receive a small number of calls relating to a hotel fire. Further to supporting those directly impacted, customers with bookings at the hotel can be contacted to communicate any changes to their package. The proactive outreach not only reduces the number of inbound enquiries, but also boosts customer satisfaction (CSAT) scores.
Secondly, small issues can often get lost in a contact centre due to the nature of reporting. The tagging process of customer enquiries is typically reviewed periodically – weekly or monthly depending on the organisation – with any uncommon spikes in certain tags investigated further. This retrospective analysis means issues can go weeks before being recognised and resolved. But it also means that small issues – for example, a button not working on the website – can be missed entirely if they don’t amass the volume of conversations that sparks investigation.
Finally, CSAT can be improved as agents can be advised on how to respond to customers calling about an early-identified issue to provide a better level of service during the call. Returning to our example about the hotel fire, if that issue is identified earlier and the correct information is communicated to all agents, any customers reaching out regarding that issue can be provided up-to-date, consistent information without the agent having to put them on hold to investigate. Boosting customer satisfaction in this way ultimately leads to better customer retention and acquisition in competitive B2C sectors.
The power of listening at scale
None of these benefits can be achieved with manual methods of tracking customer conversations. The speed, granularity, and accuracy cannot scale to thousands of simultaneous interactions in multiple languages.
Using AI to monitor and detect even subtle issues in real-time that could otherwise have gone unrecognised and unaddressed empowers businesses to improve customer satisfaction, reduce contact volumes – and in turn, costs – and elevate the role of the contact centre as a source of insights to the business. Armed with real-time knowledge of customer attitudes, concerns and issues, the relevant business stakeholders can be engaged to resolve it, whether that’s marketing, IT, compliance teams, or the board.
It’s time to recognise the potential of AI in the contact centre goes beyond automating outbound communications, but can help scale your ability to listen to and understand your customers to provide a superior service.
EdgeTier CEO and Co-Founder, Shane Lynn
The EdgeTier team realised that the true power of AI / machine learning in customer care is not in pushing customers away, but in building software that works for customer care organisations, allowing them to deliver higher-quality and more personalised care that their customers will love, while having unprecedented accessibility to data for all of their decisions.
EdgeTier began as a boutique machine learning consultancy company wholly focussed on the customer care industry. We analysed tens of millions of real-life customer queries while working with customer care teams across a diverse range of industries such as travel, insurance, banking, utilities, financial services and energy.
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