Customer service is no longer limited to just answering phone calls or emails; social media, online chat, SMS text messaging, and in-app communications all require monitoring to ensure no customer complaints or issues goes unanswered or unresolved. Fortunately, new automation and artificial intelligence tools can help customer support teams stay on top of call volume in a multi-channel service environment.There are two levels of automated customer service monitoring available today: conventional automation, and artificial intelligence solutions. While many vendors position these categories as mutually exclusive (because they only offer one or the other), robust customer service channel monitoring should include a mix of basic and AI-enhanced automation solutions.
All customer service monitoring automation -- AI or otherwise -- has one goal: to sort signal from noise and identify actual customer service requests among the normal flow of spam, subtweets, and at-mentions.
The best way to use conventional monitoring automation is behind an explicit support request channel. For example, if you have published instructions to "text HELP to 444555" or "email customer support at firstname.lastname@example.org", conventional automation could make the initial response to the customer -- "Thank you for your support request" -- and then begin a very straightforward flow of information gathering and sorting to auto-create a support ticket and triage basic information for a human rep to act on.
Conventional automation can also perform keyword monitoring of your various online channels and flag any appearances of terms like help, service, complaint, broken, or other words or phrases that may indicate an issue with your solutions. If a message qualifies, the automation can escalate it to a review queue where a service rep can determine if the complaint is both legitimate and actionable.
Where artificial intelligence can go beyond conventional automation is in identifying non-explicit or incomplete requests for help within your service channels. Keyword matching is crude, but machine learning can parse sophisticated context, so your monitoring solution can distinguish between, for example, downtime (relaxation) and downtime (a service outage), DC (the electrical current) and DC (the US capitol), or your bae and BAE.
For large organizations with lengthy product and service offerings, many of which have shorthand common names (Word instead of Microsoft Office 365 Word Online), AI may be the only way to reach a useful automated monitoring level. AI can even go the extra step and incorporate sentiment analysis to distinguish idle mentions from actual complaints or requests for help. This allows automation to build the most accurate and actionable service queue, so human support staff doesn't waste time chasing down clumsy keyword matches or closing meaningless auto-generated support tickets.
Automation can make your service channel monitoring more efficient. AI can help your channel monitoring scale.