Find Why — Not Just What
Most feedback tools tell you what customers are saying. Pivony tells you why — which segment, which channel, which operational factor — so your team acts on causes, not symptoms.
Standard Feedback Analysis Tells You What. Root Cause Analysis Tells You Why.
Standard reporting
- ✕ "20% of customers mention delivery"
- ✕ "NPS dropped 6 points this quarter"
- ✕ "Checkout complaints are up"
- ✕ Generates a management discussion
Root cause analysis with Pivony
- ✓ VIP customers in Region X using Carrier B, high-value orders — SLA failure since Week 8
- ✓ NPS drop in 18-34 segment on mobile checkout — payment SDK timeout at peak hours
- ✓ Generates a ticket, a recovery campaign, a carrier call
How Pivony Runs Root Cause Analysis
01
Connect every feedback source
Tickets, NPS surveys, call centre transcripts, app reviews, structured forms — all flowing into one platform, normalised and ready for analysis.
02
AI discovers root causes
NLP models cluster feedback semantically, then overlay your segment and operational data. Not 'delivery is a problem' — but which carrier, which region, which customer tier, since when.
03
Key Driver Analysis surfaces what matters most
Statistical analysis identifies which factors most drive satisfaction — by performance and importance. So you fix what actually moves the needle, not just what's complained about most.
04
Agentic AI acts — without waiting for a human
When a root cause crosses a threshold, Pivony creates tickets, routes them to the right team, triggers VIP recovery workflows, and generates executive briefings automatically.
Every Capability Built for Root Cause Discovery
Not a generic analytics dashboard. A system designed specifically to surface why satisfaction moves — down to micro-segment level.
Customer Journey Monitoring
Track feedback at every touchpoint — onboarding, purchase, delivery, support. Pinpoint exactly where the journey breaks down and for which segment.
Performance Monitoring & Anomaly Alerts
Your KPIs are watched 24/7. When a metric shifts abnormally in any segment, you receive an instant alert — not a weekly report discovered too late.
Micro-segmentation
Blend feedback with shipping, sales channel, CRM, and geographic data. Discover why VIP customers in one region churn while others stay loyal.
Key Driver Analysis
Statistical engine finds what drives satisfaction most — performance score, importance weight, negativity share. Know what to fix first, not just what gets complained about most.
Agentic AI Actions
Root causes trigger real actions automatically: tickets open, teams are alerted, VIP recovery workflows launch — no manual review of every item required.
Highlights & Executive Summaries
Auto-generated every analysis period: what's going great, what needs work — grounded in real customer comments. Leadership-ready in 10 seconds.
What to Look for in a Root Cause Analysis Platform
Use this checklist when evaluating any platform for root cause analysis in customer feedback. Pivony scores 8 of 8.
Case Study · ETS Tur
Root Cause Analysis at Scale: Thousands of Hotels, Real-Time Guest Intelligence
ETS Tur — Turkey's leading tour operator — uses Pivony to run continuous root cause analysis across thousands of hotel properties. Call centre transcripts, NPS surveys, and booking platform reviews are ingested simultaneously. AI surfaces root causes by hotel segment. Agentic workflows create tickets, route actions, and publish reviews without human review of each item.
The result: real-time root cause intelligence across the entire portfolio, with a significant reduction in manual analyst hours — reallocated toward strategic guest experience improvement.
Read the full case studyCommon Questions
How is root cause analysis different from standard feedback reporting?＋
Standard reporting tells you what customers are saying — topic counts, sentiment scores, averages. Root cause analysis tells you why satisfaction is moving in a specific direction within a specific segment. It answers the operational question: which carrier, region, or process is driving this, and for whom?
Do I need to provide a coding scheme or pre-define categories?＋
No. Pivony's NLP models discover themes from your data without requiring a pre-defined coding scheme. Emerging issues surface automatically — including ones your team has not yet anticipated.
How long does it take to get a first root cause analysis running?＋
Typically 48 hours from initial connection of your first data source. You do not need months of implementation or a dedicated data engineering team.
Can Pivony connect feedback to operational data (shipping, CRM, segments)?＋
Yes. Blending feedback with operational context — carrier, sales channel, customer tier, geographic region — is a core capability. This blending is what makes true root cause identification possible rather than just theme discovery.
Is root cause analysis useful for churn prevention?＋
Directly. Churn prediction models identify who is at risk — but only root cause analysis tells you why. Understanding the specific experience failure driving at-risk behaviour lets you build targeted interventions rather than generic discounts or check-in calls.
Ready to find the real root causes in your customer feedback?
Request a demo with your own data. We'll show you the root causes your current process is missing.