GermainUX provides preconfigured KPIs, pivots and measures for monitoring real user experience across web and native desktop applications.
These analytics help teams understand what users experienced, where they encountered friction, how application behavior affected them, and which issues have the greatest impact on adoption, conversion or productivity.
Preconfigured analytics can be used as provided or customized for your applications, workflows and business objectives.
Watch the User Experience KPI overview.
🔎 What User Experience Analytics Help You Understand
GermainUX UX analytics can answer questions such as:
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Question |
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1 |
Where do users become frustrated, confused or blocked? |
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2 |
Which interactions are slow, ineffective or followed by errors? |
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3 |
Which searches return no results? |
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4 |
Where do users abandon pages, journeys or workflows? |
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5 |
Which browsers, devices, applications or user groups are most affected? |
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6 |
What happened before and after an error or poor experience? |
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7 |
Which problems affect the greatest number of users? |
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8 |
What improvements does AI recommend based on real sessions? |
⚙️ KPIs for Web User Experience
🤖 AI-Driven Session Insights
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KPI |
Data model |
Description |
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AI Recommendation |
A configurable scheduled analysis that summarizes a selected group of recorded sessions and recommends UX improvements. Teams can configure the schedule, prompt and session-selection criteria. |
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Session Analysis |
AI-generated summary of what occurred during a user session. |
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Session Friction |
AI-detected friction found within a user session. |
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Session Recommendation |
AI-generated recommendation derived from one or more user sessions. |
❗ Frustration and Ineffective Interactions
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KPI |
Data model |
Description |
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Dead Click |
A click after which the page content does not change and no expected navigation or scrolling occurs. |
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Error Click |
A click associated with a subsequent JavaScript console error. |
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Rage Click |
Multiple rapid, consecutive clicks on the same element, often indicating frustration or an unresponsive interface. |
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No Search Results |
A user search that returns no results. |
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Phrase of Interest Appearance |
The appearance of configured text on a page, such as an error, warning, confirmation or other message of interest. |
🖐️ User Interactions
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KPI |
Data model |
Description |
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Mouse Click |
An individual mouse click or touch interaction. |
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User Click |
A user-interaction interval that includes the cumulative duration of synchronous and asynchronous activity between two consecutive clicks. |
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User Change |
A user changing the value of an input, selection or other monitored field. |
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User Key Press |
A monitored keyboard interaction. |
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User Feedback |
Feedback submitted by a user through GermainUX or an integrated feedback mechanism. |
💓 Browser Health and Activity
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KPI |
Data model |
Description |
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Browser Crash |
UX and technology event |
A browser tab or process crash, including critical failures such as an “Aw, Snap!” page. |
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Browser Freeze / JavaScript Long Task |
A long-running JavaScript task that blocks the browser's main thread and can make the page temporarily unresponsive. See Long Tasks and Optimizing Long Tasks. |
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Browser Visibility |
A change in whether the browser or page is active, inactive, visible or hidden. |
🎞️ Journey and Replay
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KPI |
Data model |
Description |
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Story Beat |
A short interval of user activity labeled to describe what the user was doing, with up to five levels of abstraction. |
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User Session Replay |
A recorded user session that can be searched, analyzed and replayed to understand what the user experienced. |
📈 UX-Level and Technology-Level Signals
Some KPIs describe what the user did or experienced, while others also expose an underlying technical condition.
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Signal type |
Examples |
Primary purpose |
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UX-level |
User Feedback, No Search Results, User Change, Story Beat, Session Friction |
Understand user behavior, intent, satisfaction and workflow friction. |
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Technology-level |
Browser Visibility, JavaScript Long Task |
Understand browser or application behavior. |
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Combined UX and technology |
Browser Crash, Dead Click, Error Click, Rage Click, Phrase of Interest |
Connect a visible user problem with the associated technical or application behavior. |
This distinction helps teams determine whether an issue should be addressed through design, content, workflow, application code, infrastructure—or a combination of these areas.
💻 KPIs for Native Desktop User Experience
GermainUX also provides KPIs for monitoring user interactions in native operating-system and desktop applications.
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KPI |
Description |
|---|---|
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Native User Click |
A timed loading or processing interval initiated by a user interaction in a native application. |
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Native Mouse Click |
An individual mouse-button or touchscreen interaction captured from a native application. |
Native user activity can be correlated with application events, errors, performance and supporting Windows technology to explain what happened and why.
📏 Measures
A measure is the numerical calculation applied to a KPI. Available measures depend on the selected KPI and its data model.
🫂 Session and Audience Measures
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Measure |
Purpose |
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Sessions for New Users |
Number of sessions associated with new users. |
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Sessions for Returning Users |
Number of sessions associated with returning users. |
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Percentage of Sessions from New Users |
Share of sessions generated by new users. |
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Percentage of Sessions from Returning Users |
Share of sessions generated by returning users. |
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Count |
Total number of KPI instances. |
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Unique Session Count |
Number of distinct sessions represented in the result. |
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Unique User Count |
Number of distinct users represented in the result. |
✨ Engagement and Experience Measures
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Measure |
Purpose |
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Active Duration |
Time during which the user actively interacts with the application. |
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Inactive Duration |
Time during which a session remains open without active interaction. |
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Duration |
Time associated with a session, interaction, transaction or event. |
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Pages Visited |
Number of pages viewed during a session. |
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Error Count |
Number of errors associated with the selected KPI instances. |
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Feedback Comment Count |
Number of feedback comments submitted. |
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Positive Feedback Count |
Number of positive feedback responses. |
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Negative Feedback Count |
Number of negative feedback responses. |
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Color Threshold |
Configured severity or status value used in analytics and visualizations. |
⬇️ Drop-Off Measures
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Measure |
Purpose |
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Drop-Off Count |
Number of users or sessions that abandoned the expected journey or workflow. |
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Non-Drop-Off Count |
Number of users or sessions that continued or completed the expected journey. |
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Drop-Off Percentage |
Percentage of eligible users or sessions that dropped off. |
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Non-Drop-Off Percentage |
Percentage of eligible users or sessions that did not drop off. |
📈 Statistical Measures
For duration, activity, errors, feedback, pages visited and other numerical values, GermainUX can calculate:
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Measure |
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Minimum and maximum |
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Average and total |
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Standard deviation |
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50th percentile |
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90th percentile |
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95th percentile |
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99th percentile |
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99.9th percentile |
Percentiles reveal degraded experiences that averages may hide. For example, a satisfactory average duration can coexist with severe delays affecting the slowest 5% of users.
#️⃣ Distinct Counts
GermainUX can count distinct values across available dimensions, including users, sessions, applications, pages, campaigns, referrers, browsers, devices, operating systems, systems, environments and geographic locations.
🔀 Pivots
A pivot segments KPI results by a selected dimension. The available pivots depend on the KPI, data model and captured context.
📦 Application and Experience Pivots
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Pivot |
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Application name and component |
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Access method |
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Status and configured color |
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Drop-off state |
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Session and correlation IDs |
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End time and provenance |
📄 Page and Journey Pivots
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Landing-page path, query and title |
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Exit-page path, query and title |
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Referrer URL, type and subtype |
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New or returning user |
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Campaign name, source, medium, content and term |
🖥️ Environment and Technology Pivots
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Browser and browser family |
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Device and operating system |
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System name, type and environment |
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Target name, type, hostname and environment |
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Client or target city, region, country and continent |
👥 User and Organization Pivots
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User |
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User role and type |
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User group and department |
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User city, region, country and continent |
These pivots help identify whether friction is widespread or concentrated within a page, workflow, application, technology, audience or location.
🔗 Combining KPIs, Measures and Pivots
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UX question |
KPI |
Measure |
Pivot |
|---|---|---|---|
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Which pages generate the most frustration? |
Rage Click |
Count |
Application page |
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Which browser families experience the most freezes? |
Browser Freeze |
Count |
Browser family |
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Which searches fail most frequently? |
No Search Results |
Count |
Search term or page |
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Which user groups experience the slowest interactions? |
User Click |
95th-percentile duration |
User group |
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Which applications generate the most negative feedback? |
User Feedback |
Negative feedback count |
Application name |
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Which landing pages have the highest abandonment risk? |
User Session Replay |
Drop-off percentage |
Landing-page title |
Teams can drill from an aggregate result into affected segments and individual sessions, then use Session Replay, event context and technical traces to understand root cause.
🔎 Filters
Filters narrow an analysis to the population, condition or time period relevant to the investigation. Filter options vary by KPI and captured data.
Examples include:
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Drop-off or non-drop-off sessions |
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New or returning users |
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Application, page or workflow |
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Browser, device or operating system |
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User group, role or location |
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Campaign or referrer |
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Status, severity or error category |
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Time range or environment |
Analyze Drop-Offs
A drop-off occurs when a user abandons a page, journey or workflow before completing the intended action.
GermainUX supports the following analysis workflow:
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Quantify drop-offs at scale on an Aggregate Dashboard. |
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Identify the journey stage where users abandon through a Focused Flow or flow visualization. |
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Compare affected populations using pivots and filters. |
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Examine individual drop-offs on a Drill-through Dashboard. |
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Open the Analysis Dashboard to investigate leading factors, related signals and possible causes. |
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Replay relevant sessions to see what users experienced before abandoning. |
This connects the drop-off rate with direct evidence of UX, workflow and technology friction.
🔧 Customize the Analytics
GermainUX can support custom:
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UX KPIs and event definitions |
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Measures and formulas |
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Pivots and business dimensions |
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Drop-off and completion rules |
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Phrases and messages of interest |
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User segments and cohorts |
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Static and statistical SLAs |
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Dashboards, reports and alerts |
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AI prompts and scheduled session analysis |
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Automated diagnostic or corrective actions |
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Service: Analytics
Feature Availability: 2014.1 or later