Whether your AHT criteria are based on strict SLAs or that last conference call with corporate, meeting these expectations is business critical. The moment a requestor hits send, you’re on someone else’s clock.
Automated ticketing processes powered by machine learning techniques ensure tickets are assigned priority based on analyzing historical patterns vs. “best guess.” They intuitively improve support processes in exactly the right places for quicker, more satisfactory resolutions—ultimately contributing to the health of the entire organization.
If you’re finding average handle time is too long by hours or days, ticket triage automation may be your best bet. Automated solutions can scan new requests as they come in—looking for type of concern, urgency, historical data, etc. and routing them automatically to the most appropriate support agent and/or learning document like an FAQ.
These kinds of fire drills waste resources, compromise customer satisfaction and paint your support team as sloppy and disorganized. Negative opinion finds its way quickly to social media sites and message boards prompting unwanted attention and additional calls, while Corporate puts the pressure on. You’ll never win this way.
All this can be avoided by putting an automated ticket triage process in place. How? Automated solutions embedded with machine learning technology improve your team’s effectiveness from the very first contact. Machine learning can recognize subtle patterns in the context of a ticket, like how a subject line is worded and inferences made by vocabulary used in the body of the request. Time-intensive, manual routing practices that deliver less-than-perfect results are replaced by automated, predictive and intelligent routing rules made by finding patterns in past customer interactions, channel preference, market segmentation, lifecycle status, etc.
It happens all the time. When unique or more troublesome tickets come in, emails are sent to several different people/departments successively in search of the best resolution, and then confusion ensues. Emails go back and forth, someone is copied unnecessarily—someone vital wasn’t copied at all. Manual filtering of tickets simply doesn’t work fast enough, is disruptive and often causes more problems than it solves.
Automating your ticket triage process with machine learning allows teams to measure and improve First Call Resolution (FCR) metrics to trigger routing rules or apply response macros to a ticket as it comes in. Without an automated process, agents might be found calling across a cubicle for an answer or digging through their inbox for the fix mentioned in the morning meeting. Automating the ticket triage process places both the ticket and the best resolution in front of agents, allowing them to respond in the most accurate, efficient and friendly way.
31% of people these days are quitting their jobs within the first 6 months according to this FastCompany infographic. Of these employees, 43% are entry level—most just out of college or entering the workforce for the first time. Most likely you staff your support team with this same demographic. If that’s so, automated ticket routing can improve employee satisfaction levels--keeping your team together, reducing training costs and enabling the consistency required for support metrics to rise.
Introducing new employees to a support environment with automated ticketing processes reduces onboarding time because staff can rely on automated scripts and different knowledge documents to respond confidently to customer questions instead of having to rely on gaining incidental tribal knowledge--one call at a time. And, when one of these 31% decides to walk away, their choice affects only them—not your team, not your organization, not your waiting customers.
If assigning tickets were a pure numbers game, the ratio of tickets to agents would be simple. Take the number of tickets and divide by the number of available agents. But how do you balance things out when the first five tickets that come through are easy fixes while ticket number six is a monster?
Automating your ticket triage process with machine learning takes into consideration any number of variables from historical tickets to ensure workloads are spread evenly and accurately and can continually learn and adapt to current customer support needs. It removes the subjectivity and bias inherent in processing tickets by hand and enables companies to conserve valuable Support manpower that could be put to better use elsewhere.
Learn more about how AnswerIQ is invested in helping businesses increase agent productivity by automating ticket triage processes.