UX Sniff

Captures and replays user sessions with AI-driven analysis that automatically identifies friction points, drop-off moments, and rage clicks. The system ingests raw session data (mouse movements, clicks, scrolls, form interactions) and applies machine learning models to flag anomalous or problematic user behaviors without manual tagging, surfacing insights like 'user clicked submit button 5 times' or 'abandoned form after 30 seconds at email field'.

Generates visual heatmaps showing click, scroll, and hover density across page elements using aggregated user interaction data. The system tracks pixel-level interaction coordinates, normalizes them across viewport sizes and device types, and renders density visualizations where color intensity represents interaction frequency. Supports multiple heatmap types (click, scroll, move) and can segment by user cohort, traffic source, or device type to reveal how different audiences interact with the same page.

Tracks page load performance metrics (time to first byte, first contentful paint, largest contentful paint, cumulative layout shift) and interaction latency (time from user action to visible response) to identify performance-related UX issues. The system correlates performance metrics with user engagement and conversion outcomes to identify if slow pages have higher bounce rates or lower conversion rates. Generates performance reports showing performance variance by device, browser, and geographic region, and alerts when performance degrades below thresholds.

Tracks user progression through defined conversion funnels (e.g., landing page → signup → payment) and automatically identifies where users drop off using event-based tracking. The system correlates drop-off events with user attributes (device, traffic source, geography, session duration) and AI-driven behavioral signals to attribute abandonment to specific friction points. Generates reports showing drop-off rates per funnel step, cohort-level conversion variance, and predictive indicators of abandonment (e.g., 'users who hesitate >3 seconds on password field have 60% higher abandonment').

Analyzes aggregated session, heatmap, and funnel data using machine learning models to identify patterns and generate actionable UX optimization recommendations. The system ingests behavioral data (session replays, interaction heatmaps, conversion funnels, user attributes) and applies pattern-matching algorithms to detect common friction patterns (e.g., 'users consistently hover over button X without clicking', 'form field Y has 40% abandonment rate'). Generates prioritized recommendations with estimated impact (e.g., 'moving CTA above fold could increase conversions by 15%') and links recommendations to supporting evidence (specific sessions, heatmap clusters, funnel drop-off data).

Provides a JavaScript API and UI-based event configuration system for tracking custom user events beyond standard page views and clicks. Developers can define custom events (e.g., 'video_played', 'feature_used', 'error_encountered') with arbitrary properties (event_name, user_id, timestamp, custom_data), then query and segment by those events in dashboards. The system stores events in a time-series database, supports real-time event streaming for live dashboards, and allows retroactive event filtering and segmentation without re-instrumentation.

Enables creation of user cohorts based on behavioral attributes (device type, traffic source, geography, session duration, custom events) and compares conversion rates, funnel drop-off, and engagement metrics across cohorts. The system supports both pre-defined cohorts (e.g., 'mobile users', 'organic traffic') and custom cohort definitions using boolean logic (e.g., 'users from US who spent >2 minutes on page AND clicked CTA'). Generates side-by-side comparison reports showing variance in key metrics, statistical significance tests, and cohort-specific heatmaps and session replays.

Implements privacy-first data collection with configurable PII masking, consent management, and GDPR/CCPA compliance features. The system allows configuration of sensitive data patterns (passwords, credit card numbers, email addresses) to be automatically masked in session replays and event logs. Supports consent-based tracking (opt-in/opt-out), cookie management, and data retention policies. Provides audit logs showing what data was collected, masked, and deleted per user.

Provides integrations with popular analytics, CRM, and marketing automation platforms (e.g., Google Analytics, Segment, Zapier, Slack) to export UX Sniff data and trigger actions based on UX events. The system supports bidirectional data sync (importing user segments from external platforms, exporting UX events to external systems) and webhook-based event streaming for real-time integrations. Enables use cases like 'send Slack notification when funnel drop-off rate exceeds threshold' or 'export high-engagement user cohorts to CRM for targeted outreach'.

Automatically segments user sessions and heatmaps by device type (mobile, tablet, desktop), browser (Chrome, Safari, Firefox, Edge), and OS (iOS, Android, Windows, macOS) to identify platform-specific UX issues. The system normalizes interaction coordinates across different viewport sizes and device pixel ratios, then generates device-specific heatmaps and funnel analyses. Enables comparison of conversion rates, engagement metrics, and session replay quality across platforms to identify where users experience friction on specific devices or browsers.

Segments all analytics (sessions, heatmaps, funnels, conversion rates) by geographic location (country, region, city) and traffic source (organic, paid, direct, referral, social) to identify regional and channel-specific UX patterns. The system uses IP geolocation and UTM parameter parsing to assign users to geographic and traffic source cohorts, then generates region-specific and channel-specific heatmaps, funnel analyses, and session replays. Enables identification of regional UX issues (e.g., 'users in Japan have 40% higher drop-off on checkout') or channel-specific problems (e.g., 'paid traffic from Google Ads has lower engagement than organic').