Knowledge management is at the heart of the contact centre’s role, working to bridge the gap between customers’ enquiries and the facts which will resolve their problems.

The bigger the contact centre’s remit, the bigger the knowledge needed. With extensive product ranges, specialised niche customers, and endless market requirements — not to mention complex partner relationships like B2B2C — not only is there more data to stay on top of, there are other issues too. Such as how fast it can become outdated, as products update continually, and the knowledge generation (e.g. product feature design) moves further and further away from those selling and supporting to the customer.

Maintaining an up-to-date knowledge base is the first challenge, and Steve Nattress, Product Director at Enghouse Interactive points out that for many contact centres as recently as a decade ago this consisted of printed product files for each call handler. When something needed updating, a manual ‘push notification’ consisted of physically replacing page 572 in every file, at the start of the shift… something we have got safely beyond now, when we can use machine learning and artificial intelligence to support knowledge management across all aspects of customer support.

Connecting the enterprise and the customer

“Whether it’s collection, distribution, or delivery of knowledge, AI can support it,” he explained. It’s not only automated delivery of information through self-service bots and similar applications. Often the AI will discern the point at which escalation to a human agent is the most sensitive and appropriate next move, Nattress explained. “The technology also finds and surfaces the right knowledge to the right person to deal with it, supporting them with the information when they need it to help the customer in the best way, applying the intelligence in the way it’s needed most.”

Indeed, the AI can often help the agent figure out what the customer needs in the first place, by making internal knowledge accessible through the terms and language the customer themselves is using — thereby overcoming the kinds of misunderstandings that lead to dissatisfaction, thanks to enhanced semantic reasoning.

“There are subtle things from parsing common misspellings, to different local phrasing, like Hoover being used as a generic word for a vacuum cleaner in the UK. Or gas versus petrol. It works by being a bit fuzzier around the terms people are using, to cut through to what they really mean.”

Changing training and supervision

This makes a huge difference in the direct delivery of customer service, with one of Enghouse’s customers — selling a vast inventory of white goods’ insurance products — cutting their training and onboarding pathway from eight weeks down to a fortnight for new agents:

“They set them a challenge, day one, go and find the answer to this question — and the trainee gets immediate feedback and reward, and embedded in the culture, that you go and ask the knowledge base. Because the answer you need is going to be in there,” Nattress described.

Once those new hires are actively delivering customer service excellence alongside their colleagues, the consistency and quality of the service are enhanced too. “It’s had a knock-on impact on their call handling time, over 20% reduction, about 30 seconds reduction per call.” Whether the agents are working in a colocated or distributed way, the AI supports each of them in an individual and timely way — overcoming the distance and lack of shared experience, with direct access to information and support.

All the customer knows is that the person they speak to has immediate access to the information needed to understand and solve their problem as quickly as possible, and find the best way to make them happy… So, that’s a win-win, for the business and their end-user.

Maya Middlemiss

Guide to AI-Driven Knowledge Management