> ## Documentation Index > Fetch the complete documentation index at: https://docs.germainux.com/llms.txt > Use this file to discover all available pages before exploring further. # 4-Analysis Dashboard > The Analysis Dashboard is GermainUX's root-cause investigation interface. Components [`Enterprise- OnCloud`](https://docs.germainux.com/main/deployment-on-our-cloud) [`Enterprise- OnPremise`](https://docs.germainux.com/main/deployment-on-premise) Availability 2014.1 or later > **[Summary](#summary)** > > Spot abnormal KPI behavior > **[Duration Breakdown](#duration-breakdown)** > > Find where time goes > **[Recursive Analysis](#recursive-analysis)** > > Investigate each trace level > **[Configure Analysis](#configure-analysis)** > > Tune analysis per KPI ## Overview The **Analysis Dashboard** is GermainUX's root-cause investigation interface. It helps teams understand **why a business, user-experience, workflow, or technology KPI is behaving the way it is** by automatically bringing together the evidence needed for investigation: * Baselines and trends * Relationships with other issues * Conversion, Adoption, Experience, and Technology Frictions * Leading factors * Segmentation * Affected users, sessions, and instances > **Show all 11** > > * Comparison with what worked > * Technical traces > * Session Replay > * Ai-powered analysis > * Recommendations ![Abandonment Rate Analysis](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-174101.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=98eff33bdc3a85ed322bb3679b54c596) Abandonment Rate Analysis - GermainUX **Instead of manually searching** across dashboards, logs, traces, and user sessions, teams can move from **symptom → impact → likely cause → supporting evidence** within one analytical workflow. The Analysis Dashboard helps answer: * Is this abnormal? * What changed with it? * What frictions may be related? * Where is the issue concentrated? * Who or what is affected? * What is the likely root cause? ## Aggregate View vs. Instance view One of the key benefits of the Analysis Dashboard is the ability to investigate an issue from **two complementary perspectives: Aggregate and Instance Analysis.** | Analysis | Purpose | Example | | - | - | - | | **Aggregate Analysis** | Understand what is driving a KPI across many users, sessions, transactions, or systems. | Why did conversion decrease across thousands of sessions? | | **Instance Analysis** | Understand exactly what happened during one specific occurrence. | Why did this particular user fail to complete checkout? | ### Aggregate Analysis — Find What Matters at Scale Aggregate Analysis looks across the **entire selected population and time period** to identify patterns that would be difficult or impossible to discover by investigating individual instances one at a time. ![Technology Freeze Aggregate Analysis](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-175119.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=e689ecf701c22a3cbbd184d9c39179e9) Technology Freeze Aggregate Analysis- GermainUX It helps answer: * What are the **largest factors associated with the issue?** * Which users, sessions, workflows, applications, products, regions, devices, browsers, or versions are most affected? * Which related KPIs changed at the same time? * Is the problem widespread or concentrated within a specific segment? * How many users, sessions, or instances are affected? * Which issues should be investigated first? For example, if conversion drops across **100,000 sessions**, reviewing individual sessions would not efficiently reveal what is affecting the population. Aggregate Analysis can identify that the decrease is concentrated among a particular **checkout step, browser version, product, error, performance condition, or user segment**, helping teams quickly focus on the factors with the greatest impact. **Benefit:** Find the patterns, relationships, and leading factors affecting the population **without manually investigating thousands of individual occurrences.** ## Instance Analysis — Explain a Specific Occurrence Once Aggregate Analysis identifies an important pattern or affected population, users can drill into an individual instance to understand **exactly what happened**. ![Technology Freeze Instance Analysis](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260918-170531.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=298786babf03e5a10589f67e8173c332) Technology Freeze Instance Analysis - GermainUX ![Slow User Click Instance Analysis](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260823-214302.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=c9edd44363c19627e506f190ccc43286) Slow User Click Instance Analysis - GermainUX GermainUX can compare the selected instance across: INSTANCE SESSION USER POPULATION OVERALL This helps determine whether the behavior is: * Isolated to one occurrence * Specific to one session * Specific to one user * Concentrated within a population * System-wide Depending on the KPI and available data, the investigation can include: * Duration breakdown * Technical traces * HTTP activity * Errors * Related transactions * User activity * Events occurring before and after the issue * Session Replay **Benefit:** Move from identifying **what is affecting users at scale** to understanding **exactly why a representative occurrence happened.** ## From Population-Level Insight to Individual Evidence Aggregate and Instance Analysis are designed to work together: **Detect a KPI change** \ → **Analyze the population** \ → **Identify leading factors** \ → **Find affected segments** \ → **Measure users/sessions/instances affected** \ → **Open a representative instance** \ → **Analyze its trace and surrounding activity** \ → **Replay the user session** \ → **Identify the likely root cause** This approach avoids two common investigation problems: **Looking only at aggregate data** can reveal a pattern without showing exactly what happened to an individual user or transaction. **Looking only at individual instances** can explain one occurrence without showing whether it represents the broader population. GermainUX combines both views so teams can understand **what matters at scale and then validate it with instance-level evidence.** ## Summary The Summary helps determine whether the selected KPI is behaving abnormally. ![4-Analysis Dashboard: Summary](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260823-212431.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=401a95620224f8e3106cb2a0c5ba0867) Depending on the KPI and available history, GermainUX can compare: * Vs Recent * Vs Last Quarter * Trend * How Extreme Percentiles can also help determine whether an issue affects the overall population or only a subset. For example, a normal median combined with a high p95 or p99 may indicate that most users are unaffected while a smaller population is experiencing significant degradation. ## Relationships The Relationships view overlays the KPI being analyzed with related signals on a common timeline. ![Relationship with Potential Contributing Factors](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-180422.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=37176cdef61008008de57532ce0eeeaf) Relationship with Potential Contributing Factors - GermainUX It can include: * Selected KPI * Overall population * Related KPIs * Frictions (Conversion, Adoption, Experience or Technology) * Discrete events * Errors * Alerts * Dimensional pivots This helps identify signals that changed at the same time as the KPI being investigated and provides direction for further analysis. ### Filter KPI by Friction The Relationships graph can be filtered by the type of friction relevant to the investigation: * **Conversion Frictions** — conditions that may interfere with a visitor's ability to convert or complete a desired business outcome. * **Adoption Frictions** — conditions that may interfere with successful application, feature, or workflow adoption. * **Experience Frictions** — conditions affecting the user's experience, such as errors, failed interactions, or other detected user friction. * **Technology Frictions** — application or technology conditions such as errors, failures, or performance issues that may affect users or business outcomes. Select one or more friction categories to focus the Relationships graph on the signals most relevant to the investigation. For example: **Conversion Rate Declines** \ → Filter on **Conversion Frictions** \ → Identify frictions occurring around the decline \ → Determine which populations are affected \ → Investigate representative instances \ → Review Session Replay and related technical evidence ![Cart Abandonment Related Issues](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260922-170152.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=091fa085be0729200d82714b027be84f) Cart Abandonment Related Issues - GermainUX And you can replay the session behind that negative user feedback to understand the **context**around the cart abandonment. Or: **CRM Adoption Declines** \ → Filter on **Adoption Frictions** \ → Identify relevant workflow or user frictions \ → Compare affected populations \ → Investigate individual sessions \ → Identify likely contributing factors Friction filtering helps reduce noise and focus the investigation on the type of outcome being analyzed. ![Productivity Loss-Related Issues](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260922-165524.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=dc50b7c8a8d30e4d200c4959cf0f91b5) Productivity Loss-Related Issues - GermainUX ## Related KPIs Configured **KPI Relationships** allow GermainUX to automatically surface related signals during an investigation. ![Related KPIs](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-180632.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=6b3d8d21fb50696a8ab56a3f35cadc7d) Related KPIs - GermainUX Related KPI information can include: * Spike or drop * Current value * Baseline * Variation * Trend * Co-movement These signals help identify **where to investigate next**. Relationships and correlation provide supporting evidence but do not, by themselves, prove causation. \ \ And you can pivot to further understand an issue: ![Pivot on Related KPIs](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260922-163836.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=5025968b4c218ec2e42a3b267f39a69c) Pivot on Related KPIs - GermainUX ## Leading Factors Leading Factors analyze available KPI dimensions to identify the populations or conditions most strongly associated with the observed outcome. ![Leading Factors](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-180720.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=6be8b430da1b56cfaa507700a1c92382) Leading Factors - GermainUX Depending on the KPI, dimensions can include: * User * Session * Page * Application * Product * Journey step > **Show all 11** > > * Workflow > * Region > * Device > * Browser > * Version Segments can be identified as: * **DEGRADED** * **OUTLIER** * **NEW** * **BETTER** ![Degraded, Outlier, New, Better](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260925-180922.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=a79c0f243021c90884430bbda18829cd) Degraded, Outlier, New, Better - GermainUX Affected instance, session, and user counts help determine both the **scale and concentration** of the issue. ## Duration Breakdown For supported duration-based KPIs, elapsed time can be separated into areas such as: * Service * Network * Rendering * Document Processing * Idle or Unmonitored Duration can be analyzed using **Exclusive or Cumulative time** to identify where time is actually being spent. ## Trace Timeline The Trace Timeline displays the selected transaction and related activity on a common timeline. ![Trace Timeline](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260823-213028.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=985ea75f52275b949642c8f89087481a) Trace Timeline - GermainUX Depending on the monitored data, it can include: * Child spans * Server timings * HTTP requests * Browser activity * Errors * Related transactions * User activity For HTTP activity, available evidence can include connection timing, SSL, waiting time, download time, response size, caching, compression, URL, response information, and HAR export. ## Comparison of Bad vs Good GermainUX automatically compares a transaction or event with another matching instance to identify differences in performance, behavior, and outcomes. Select one of four comparison options: * **Worst** — Compare with the worst-performing matching instance. * **Most Recent** — Compare with the most recently recorded matching instance. * **First Seen** — Compare with the first recorded matching instance. * **Last Seen** — Compare with the last matching instance recorded before the selected event. ![Image](https://mintcdn.com/germain-ux/SL7MVE0wC9He3YKh/images/image-7.png?fit=max\&auto=format\&n=SL7MVE0wC9He3YKh\&q=85\&s=ba183d789b2e5ddedf5dfc318db9ac4b) ### Identify What Changed GermainUX highlights the differences between the two instances, including: * **Duration** — Compare execution times and quantify performance differences. * **Processing breakdown** — Identify which operations, such as DOM processing, JavaScript execution, network requests, or server processing, contributed to the difference. * **Missing or additional activity** — Identify operations that occurred in one instance but not the other. * **Business outcomes** — Compare results such as conversion, abandonment, or other recorded conditions. Differences are ranked by their contribution, helping teams focus on the most significant changes first. Each instance can be opened for further investigation. ### Example: Investigating a Slow Click A user interaction that took **40.444 seconds** is automatically compared with a matching instance that completed in **3.650 seconds**. GermainUX identifies: * **11.1× slower execution** in the problematic instance. * **38.445 seconds of DOM processing** in the slow instance, compared with none in the faster instance. * **8.869 seconds of** `imageryFetch `**processing**, compared with 4.105 seconds in the faster instance. * Differences in network activity, JavaScript execution, and recorded business outcomes. Instead of manually reviewing two transactions, teams can immediately see **what changed, where time was spent, and which differences warrant further investigation**. The comparison provides direct access to the underlying events and the full investigation view to help determine the likely root cause. ## Extended Context Expand the Trace Timeline to investigate what occurred **before and after** the selected transaction. Available context can include: * Console errors * User-facing errors * Rage clicks * Popups * Navigations * Related requests * Related transactions This is particularly important when the event responsible for a symptom occurred **before the symptom itself became visible**. ## Recursive Analysis Analysis does not have to stop at the original KPI. Select **Analysis** from a supported trace span to investigate that KPI independently. For example: Slow User Click Slow HTTP Request Slow Service Call Slow Database Operation ![Slow SQL Statement](https://mintcdn.com/germain-ux/5aT05xBIVKmBUMJK/images/main/4-analysis-dashboard/image-20260918-164029.png?fit=max\&auto=format\&n=5aT05xBIVKmBUMJK\&q=85\&s=57cb4c01e00d3e73a0b9025db5fcf817) Slow SQL Statement - GermainUX Each level can then be analyzed using its own baselines, relationships, segmentation, aggregate patterns, and instance evidence. ## Session Replay When Session Replay is available, open the affected user's session to see **what the user was actually doing and experiencing around the issue**. This connects analytical and technical evidence with the actual user experience. Learn more about **Session Replay**. ## Investigation Workflow A typical GermainUX investigation becomes: **Symptom** \ → Confirm abnormal behavior \ → **Analyze impact across the population** \ → Review Related KPIs \ → Identify Leading Factors \ → Identify affected segments \ → Measure users, sessions, and instances affected \ → **Select a representative instance** \ → Review duration and trace \ → Review surrounding activity \ → Open Session Replay \ → Identify likely root cause \ → Take action \ → Validate The result is a continuous investigation path from **population-level impact to instance-level evidence**. ## Configure Analysis Analysis behavior depends on KPI configuration, including: * Fact Type * Fact Category * Measures * SLAs * Statistical SLAs * Dimensions * KPI Relationships * Matching fields Configuration is available under: **Germain Workspace > Settings > Analytics** ## Related Documentation > **[Analytics](https://docs.germainux.com/main/analytics.md)** > > More Analytics Features > **[Dashboard & Reports](https://docs.germainux.com/main/dashboard-and-report.md)** > > Dashboard & Reports > **[KPIs, Pivots & Measures](https://docs.germainux.com/main/kpis.md)** > > Read more ## Get Help > **[Contact GermainUX Support](https://docs.germainux.com/main/support.md)** > > The Germain Team can help you set this up. > **Can't find what you need?** > > Search all GermainUX docs with **Ctrl + K**, or use the feedback buttons at the bottom of this page to tell us what is missing. 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