Turn Customer Complaints Into Revenue You Can Count
Your customers are telling you exactly why they leave — across the App Store, Google Play, Trustpilot, Reddit, and your call center. VoC Radar reads all of it in one place, tells you which issue is costing you the most money, and puts a number on it before the spike shows up in your quarterly numbers.
Built with feedback & CX teams across banking, retail, and telco
The gap this closes
Most feedback lives in silos. A 1-star review on the App Store, an angry Reddit thread, and 40 calls about the same login bug never meet. So teams react late — usually after a screenshot is doing the rounds on social.
VoC Radar runs like a control tower. It pulls every channel into one stream, clusters the noise into named topics, scores the sentiment, and links a complaint spike back to the event that caused it — the v5.2 release, the payment outage, the new fee. You stop guessing which fire to fight. Detection target: under one hour from spike to alert, with sub-second dashboard queries even on high-volume feedback.
What makes it different
One stream from every channel
Google Reviews, Play Store, App Store, Reddit, Trustpilot, and call-center logs land in a single schema. Each source writes to its own isolated table, so a flood of Reddit traffic never slows the App Store pipeline or locks the database.
A revenue number on every problem
Every topic carries a dollar figure for revenue at risk, from a formula you control (default churn_rate * avg_customer_value, 2% baseline). Change it in the admin screen and every dashboard recalculates on the spot. The math runs through a safe expression parser — never raw code execution.
Root cause, not just symptoms
The platform lines up sentiment drops against your operational timeline. When negative volume jumps, you see what shipped that week sitting right next to it — turning "people are unhappy" into "the checkout rewrite cost us this much, starting Tuesday."
Action that actually goes somewhere
When a rule trips, VoC Radar routes the alert to where work happens: dual email, SMS, and Jira automation. Deduplication means one anomaly fires one ticket, not forty. A human approves the recommended action before anything moves.
What is experience-driven churn costing you?
Enter four numbers about your business. See how much revenue you lose to experience-driven churn each year — and how much VoC Radar could help you recover. The math is identical to the in-product revenue engine.
Adjustable assumption — set it to what fits your business.
Get the detailed report
A per-channel breakdown across all six sources and your 90-day recovery projection, emailed as a one-page PDF.
By submitting, you agree to receive your report and occasional product updates from VoC Radar. Unsubscribe anytime. [Confirm wording with legal before launch.]
Your report is on its way
Check your inbox in a few minutes. Want a walkthrough with your own numbers?
Book a 20-min demoThe rules that decide when you hear about it
VoC Radar watches the stream with a rule engine that runs on its own, separate from ingestion. These are the defaults — admins tune them per tenant.
| Rule | Default trigger | What it catches |
|---|---|---|
| Critical sentiment | Topic sentiment drops below 40 (0–100) | A category turning toxic |
| Volume spike | Volume > 3× moving average, ≥ 10 records | A sudden surge of complaints |
| Sentiment drop | A 20-point fall between consecutive periods | A fast slide before it bottoms out |
| Revenue at risk | Breach of the admin-defined SLA threshold | Money leaking faster than your limit |
How teams use it
Manual
An analyst reviews the feed, corrects a misclassified entry, bulk-imports a legacy spreadsheet through the CSV wizard, and sets the rules by hand.
AI-assisted
The analyst leans on topic clustering and root-cause synthesis to find the real driver behind a spike, then approves the recommended fix.
Autonomous
The watcher daemon catches the anomaly, the synthesis engine drafts the resolution, and a Jira ticket lands for sign-off. Your team wakes up to a triaged problem, not a surprise.
Everyone sees the slice they should. Super Admins govern the whole system, Admins own tenant branding and formulas, CXOs get the strategic dashboards, and Analysts drill into the raw feed and ingestion logs. Access follows the role — every screen and every API call.
Your 90-day targets
What a typical rollout aims for in the first quarter. These are goals to hold the platform against, not averages we promise.
Issue detection under one hour, down from days.
A 30% cut in complaint volume on the topics you act on.
NPS climbing toward +30.
Every executive review opening with a revenue figure, not a hunch.
Common questions
How is our data kept separate from other tenants?
Every record is scoped to a tenant, and access is enforced on every screen and every API call by role. Passwords are hashed with bcrypt, formulas run in a sandboxed parser, and per-tenant rate limits stop one customer's load affecting another.
What does it integrate with?
Today: Google Reviews, Play Store, App Store, Reddit, Trustpilot, and call-center logs in, plus email, SMS, and Jira out. CSV import covers any offline source.
How long does onboarding take?
Most tenants are live in under two weeks — connect your sources, set your revenue formula, tune your thresholds. [Confirm with the delivery team before publishing.]
Book a walkthrough
Bring your three loudest customer complaints. We'll show you the dollar figure behind each one and the alert that would have caught it early.
For your technical team: configuration and defaults
Nothing here is hardcoded. Admins own the levers, and each tenant runs its own settings.
| Setting | Default | Notes |
|---|---|---|
| Revenue formula | churn_rate * avg_customer_value | Edit in admin studio; recalculates live |
| Churn baseline | 0.02 (2%) | Override per tenant |
| Cache freshness | 30s / 5min / 30min | Keeps dashboards sub-second |
| List page size | 50 default, 200 max | Server-side pagination on every feed |
| Rate limits | 500/15min IP, 120/min tenant | Stops one tenant starving another |
| KPI window | Rolling 30-day | NPS & complaint-volume delta server-side |
KPIs come straight from live data. NPS uses ((positive - negative) / total) * 100 over the recent 30-day window. Complaint Volume Change measures the percentage shift in negative sentiment against the prior 30 days. Cards turn green when things improve and red when they slip.