Identify your highest cost-to-serve ticket types
For when you're cutting support costs but don't know which ticket types to automate first.
The Challenge
Volume does not equal cost
Your highest-volume tickets might be quick to resolve. Your most expensive tickets might be mid-volume but require senior agents and multiple touches.
Automation guesswork
Without knowing which themes are repetitive and low-complexity, you invest in chatbot content that addresses the wrong problems.
No deflection baseline
You can't measure deflection improvement if you don't know what's deflectable in the first place. You need a map of ticket types by cost and complexity.
How teams use Enterpret today
Situation
A CX leader was tasked with reducing support costs by 15% but didn't know where to start cutting.
Action - asked Wisdom, Enterpret's AI assistant
What are the most frequent support themes that are also the simplest to resolve? Rank by volume and show average resolution time.
Impact
34% of tickets were 'how do I configure X?' questions. Building targeted help articles for the top 3 themes reduced ticket volume by 18%.
Situation
A support ops manager was deciding which topics to add to the AI chatbot and needed data, not opinions.
Action - prompted Claude with Enterpret MCP connector
Use the Enterpret Wisdom connector to show which support themes have the most repetitive resolutions — meaning agents give essentially the same answer each time. Rank by volume.
Impact
Identified 5 high-volume, low-variance themes ideal for automation. The chatbot handled 40% of those tickets within a month.
Situation
Volume-based reporting showed “order modification” tickets as only 5% of total volume — but support leadership suspected they consumed disproportionate agent time.
Action -
Action — configured an Enterpret Escalation Agent for high-touch ticket clusters
Escalation agent flags ticket themes where customers require 3+ agent touches or escalations — posts weekly to #support-ops with the highest cost-to-serve themes.
Impact
Identified that “order modification after checkout” consumed 22% of agent time despite being 5% of volume. Created a specialized team to handle the pattern.
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