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 Such as how fast it can become outdated, as products update continually, and the knowledge generation (e.g. product feature design) moves further and further away from those selling and supporting to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: