What is UXsniff?

UXsniff automatically detects user experience changes on a website by analyzing heatmaps and session recordings, then compares pre‑ and post‑change behavior to identify which interactions shifted. Its Change Radar continuously monitors for layout, copy, and component updates, flagging modifications that correlate with changes in key metrics.

Retro A/B, or Time‑Travel A/B, allows teams to perform a historical comparison without a planned experiment, using real user data to pinpoint the effect of a release. Impact Reports summarize the behavioral impact of each detected change, highlighting increases or decreases in conversion steps and linking them to specific UI elements.

The tool’s rage‑click detection surfaces unusual click patterns and dead‑clicks, providing evidence for friction points. A lightweight tracking script integrates quickly and operates non‑blocking, giving product and engineering teams rapid insight into what changed and why metrics move.

UXsniff pricing Paid

Poodle $19

Hound $49

K-9 $89

Wolf pack $299

Verify on the official pricing page.

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UXsniff user reviews

Based on 6 reviews, 50.0% of users recommend UXsniff, rated highly for value for money.

recommend

don't

6 reviews

Liked for

Worth the price 3 of 3

Quality results 2 of 3

Easy to use 2 of 3

All key features 2 of 3

Good integrations 2 of 3

Disliked for

Inconsistent results 2 of 3

Hard to use 2 of 3

Lacks integrations 2 of 3

Missing features 1 of 3

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UXsniff's key features

Automatic UX change detection
Before‑after behavior comparison
Impact reports linking changes
Heatmaps and session recordings
Rage click detection
Lightweight script installation
AI abnormal click analysis

UXsniff use cases

Quickly validate that a new checkout layout boosts conversion by automatically comparing heatmaps before and after rollout, highlighting changes that correlate with higher add-to-cart rates.
Detect unexpected user frustration after a feature update by flagging rage-click clusters and generating an impact report that links copy changes to bounce rates.
Leverage historical A/B comparison to prioritize UI tweaks—compare session recordings across multiple releases and identify which component adjustments consistently improve engagement metrics over time.