---
title: AI Review Analytics — NPS & Feedback Mining | Zentavor
description: Every review and NPS comment, read and ranked into the drivers your teams can act on.
canonical: https://zentavor.com/solution-review-analytics.html
---

# AI Review Analytics — NPS & Feedback Mining | Zentavor

> Every review and NPS comment, read and ranked into the drivers your teams can act on.

Solutions / AI Review Analytics

Every review, read and ranked

Turn millions of reviews and NPS comments into ranked drivers and sentiment your product and ops teams can act on
NLPLangChainVector DBPySparkClickHouseAirflow
9:41
Voice of CustomerNPS & review analyticsOn-premise
Retail / CX · in production
+17%
Drivers ranked
accuracy vs CSI baseline · 12M+ / yr
top driver · checkout & payments
Ranked drivers
Checkout & payments31%↓ NPS
Delivery speed18%↑ NPS
auto-tagged12M+ / year
12M+reviews per year
−65%analyst time
+17%accuracy vs baseline
What we build

## From raw feedback to ranked drivers

Every review, survey comment and message streams in, gets tagged for topic and sentiment, and rolls up into the drivers that actually move your score. So teams read the answer, not the backlog
Sources
App StoreGoogle PlayTrustpilotZendeskIn-app survey
Last 30 days
12M+
reviews / year read
+17%
accuracy vs CSI baseline
−65%
analyst time on reviews
net sentiment · 30-day trend
Incoming feedback12,408,290 read
Ranked NPS driversshare of movement
auto-tagged · topic + sentiment5 of 5 drivers
The challenge

## Feedback nobody reads

Manual analysis

Reviews and comments arrive faster than teams can read.
Manual tagging is slow, inconsistent, unscalable.
The headline NPS number hides the drivers behind it.
Insight never reaches product/ops teams who can act.

With Zentavor

12M+ reviews / year read automatically.
Consistent AI tagging across languages and channels.
Ranked NPS drivers with +17% accuracy versus a CSI baseline.
Ranked drivers delivered straight to product and ops.
Proof

## Results in production

Retail / CXAnalytics
−65%
analyst time on reviews
Manual coding replaced by AI tagging. Analysts moved from reading to acting.
In productionNDA
Retail / CXAnalytics
12M+
reviews / year read
Reviews, NPS comments and UGC ingested and tagged automatically across channels and languages.
In productionNDA
Retail / CXAnalytics
+17%
driver accuracy vs CSI baseline
Ranked NPS drivers beat a classic CSI baseline, showing which themes move the score.
In productionNDA
Selected case studies available under NDA. Contact us for examples in your industry.
FAQ

## Your frequently asked questions

**
Which feedback sources can you ingest?**

Reviews, NPS/CSAT survey comments, support tickets and social UGC across channels like Telegram and Instagram, text in any of your operating languages.

**
How accurate is the tagging compared with manual coding?**

On benchmarked datasets the models reach +17% driver accuracy versus a classic CSI baseline, and tagging stays consistent across reviewers, languages and time.

**
How is this different from a top-line NPS score?**

A score tells you what happened; we rank the drivers behind it, so you see which themes are pushing the number up or down and where to act first.

**
Can you handle multiple languages?**

Yes. Topic and sentiment tagging run across languages, so multi-market feedback rolls up into one consistent set of drivers.

**
How do results reach the teams who act on them?**

Ranked drivers and sentiment flow into the dashboards and tools your product and ops teams already use, cutting analyst time on reviews by around 65%.

**
Does our feedback data leave our environment?**

No. The pipeline runs inside your perimeter, on-prem or private cloud, so customer feedback never leaves your environment.
Let's talk

## Hear what your customers are saying

Point us at your reviews and surveys. We'll scope analysis and the dashboards your teams need
Request a demo