In 2018, companies invested in chatbots and self-service technologies to help their helpdesks manage the volume of customer requests they were experiencing – according to research by Forrester, 46 per cent of enterprises were investing in AI for sales and marketing, followed by 40 per cent for their customer service teams. However, these investments were typically aimed at filled specific gaps in the customer interaction, rather than being part of wider strategies around customer engagement, or CX.
Yet your customer service team holds the key to not just making AI and automation campaigns work better today; they can also help make AI work more effectively into the future. This is through knowledge management.
So, what is knowledge management?
Knowledge management covers all the ways in which a company manages its data around customer interactions, around products, and around ways of working. From official help and support documents that are sent to customers, through to the self-help articles stored within what are called Knowledge Bases, this pool of content is useful to help customers and to help them help themselves too. Knowledge management refers to all the processes involved in creating, using and sharing that data within the business. Finally, it covers how this organisational knowledge is maintained and developed over time, so that anyone across the business – from sales, operations and marketing – can make use of it whenever they need to.
For businesses with large customer support or IT service teams, knowledge management programmes should already be in place. However, they tend to be run primarily for the benefit of those teams, rather than used more widely across the whole business. They also tend not to have been developed with automated services like chatbots in mind. However, with more self-service and chatbot implementations taking place, this source of customer-focused data can be used to support those AI projects and make them more valuable.
At the same time, customer support teams gather information on the issues and problems that customers report. This set of data can be really helpful for planning purposes, from spotting where customers are having individual issues with products through to spotting problems that affect large numbers of customers at the same time. This data can help teams find out where new content is needed to aid customers in solving their problems themselves.
Extending knowledge management outwards will mean some changes in internal mindsets, in processes around developing content, and how agents work with their knowledge management tools. These changes can help your initiatives around automation deliver more value for everyone.
Automating knowledge processes and feedback loops
The issues around knowledge management tend to revolve around information not getting shared well, or not adequately managed over time. If one agent comes up with a brilliant solution to a problem, but it is not shared with other people on the same time, then it will be difficult to scale up. Similarly, if that knowledge remains in agents’ brains and not shared with new starters, it’s more difficult to keep service levels high over time when staff leave the team.
Making it easier to share knowledge – via new articles and videos, or via more in-depth content pieces – is a big challenge for teams. However, getting good data on the problems that affect people can make it easier to help more customers, more efficiently. This means that marketers and customer service teams need to collaborate more around the kinds of content that people are looking for, how to get that content to them, and how to understand where customers are in their journey around a product.
At the same time, getting this content to more customers earlier can improve efficiency, regardless of how you make use of new automation services and chatbots. For companies that need to have more human interaction as part of their brand, automating recommendations on content to use or guiding interactions can help agents be more efficient in their services. For those that make use of chatbots, agents can make content available for those chatbots to use based on the new problems that are coming up as important or affecting multiple customers.
This process relies on the combination of automation and AI with understanding how customers ask around their problems and what they really mean. By linking up traditional knowledge management skills with new automation processes, you can make the most of AI and the human touch together. More importantly, you can use the knowledge that you already have to keep improving. Rather than looking at AI or chatbots solely to fill gaps, your marketing and customer experience can be improved through combining the best of both worlds.
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