CSAT showed smoke, but Resident couldn't find the fire
Understanding where the journey breaks down impacts retention, future sales, referrals—everything that matters. Rippit finally gives us that visibility.
Michelle Zimmerman
Senior Quality Data Analyst, Resident Home
About Resident
Resident is an ecommerce home furnishings company selling mattresses and bedroom products. They support complex delivery, trial, and return cycles where satisfaction and retention are tightly linked.
E-Commerce
~500 Employees
CHALLENGE
CSAT showed 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.”
Michelle Zimmerman
Senior Quality Data Analyst, Resident Home
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 Rippit, Resident replaced inaccurate manual tagging with AI 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 AskRippit to pull the real drivers, supporting VoC, and representative examples—giving them validated root-cause insight in minutes.
“Once we tied AI dispositions to our operational data in Rippit, we finally had trustworthy insight into what was actually driving negative sentiment.”
Michelle Zimmerman
Senior Quality Data Analyst, Resident Home
Impact
Insight informs revenue, risk, and operational decisions
3
Policy changes that improved customer retention
“Once we tied customer sentiment to policy decisions, it stopped being noise and became a clear path to change.”
Michelle Zimmerman
Senior Quality Data Analyst, Resident Home
Customer value preservation
AI intent signals surfaced the exact point where the return journey broke down, enabling targeted policy updates that improved CSAT and protected long-term customer value.
Churn prevention
With AI Classifiers, highly negative interactions were immediately identifiable, allowing Resident to intervene earlier and redesign workflows that previously led to cancellations and avoidable churn.
Cost & efficiency improvements
AI insights showed intents requiring lead approval had double the handle time. Expanding agent autonomy cut AHT in half, reduced cost, and improved forecasting accuracy for WFM.
Accelerated decision velocity
With AI-classified intents tied to CSAT, sentiment, AHT, and cancellations, leaders can pinpoint issues in minutes instead of relying on retroactive surveys.
Resident is expanding AI Classifiers to evaluate additional points across the customer journey. They are also exploring an AI-powered Experience Score to complement CSAT and give a fuller, more complete view of customer sentiment.
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