Machine learning in support operations.
WHAT THE TASKS ARE
Categorising by topic Assessing urgency Routing to the right team Suggesting responses Detecting sentiment and escalation risk
WHAT DATA EXISTS ALREADY
Historical tickets with their eventual category and resolution.
WHY THAT IS A STRONG STARTING POINT
Labels exist without annotation effort.
WHAT TO BE CAREFUL WITH
Categories applied inconsistently by agents Categories that changed over time A dominant catch-all category
WHAT TO DO FIRST
Examine the label quality, before modelling.
WHY
Inconsistent labels cap achievable performance, and the model learns the inconsistency.
WHAT TO START WITH
A simple text classifier on historical tickets.
WHAT TO MEASURE
Routing accuracy Time saved Reassignment rate after routing
WHY REASSIGNMENT RATE
It is the direct measure of misrouting.
WHAT TO AUTOMATE FULLY
Only categories where accuracy is high and the cost of error is low.
WHAT TO LEAVE AS A SUGGESTION
Everything else.
WHAT SUGGESTED RESPONSES REQUIRE
Review before sending, for anything not trivial.
WHAT TO MONITOR
Whether suggestions are accepted or rewritten.
