- Uncover business-critical issues hidden in support conversations
- Replace disconnected metrics with insights that drive action
- Equip teams across product, ops, and support with data that matters
- Connect the dots between customer experience and company outcomes
Industry
E-commerce
Use Case
Company Size
~500 Employees
Increase in CSAT
30%
Decrease in agent ramp time
120%
Increase in monthly coaching sessions
300%
CHALLENGE
CSAT Showed the Smoke But They Couldn’t Find the Fire
“Returns were driving a huge share of our DSATs, but we had no way to see which part of the return lifecycle was causing the friction.”
Resident relied on CSAT to flag friction, but with <20% response rates, it surfaced problems without showing where the experience was breaking down. Returns drove a disproportionate share of DSATs, yet the root cause inside the multi-step process was unclear.
Manual QA covered too little volume, and agent-applied dispositions often misclassified intent, solution, and outcome. The data wasn’t reliable enough to segment journey issues or validate assumptions.
As a result, Resident couldn’t quantify the size of key problems, pinpoint where friction originated in returns or transit, or tie dissatisfaction to cancellations, repeat contacts, or retention risk.
SOLUTION
Complete Customer Journey Visibility Through AI-Powered Intent Signals
Using MaestroQA, Resident replaced inaccurate manual tagging with LLM Classifiers that automatically categorize 100% of conversations by journey intent: returns, cancellations, transit issues, logistics, and more.
By merging these AI intent signals with operational metrics like handle time, DSAT, repeat contacts, and cancellations, Resident created a clear map of where friction occurs across the customer journey.
When a problem surfaces, the team drills into that slice of data, using AskAI to pull the real drivers, supporting VoC, and representative examples—giving them validated root-cause insight in minutes.
“Once we tied LLM dispositions to our operational data in Maestro, we finally had trustworthy insight into what was actually driving negative sentiment.”
