AI KPIs, Pivots, Measures

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Overview

GermainUX provides AI KPIs that capture the results and performance of AI analysis across user sessions, errors, conversations, recommendations, and AI requests. These KPIs make AI-generated insights available throughout GermainUX for dashboards, analysis, reporting, alerting, and automation.

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KPI Catalog

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AI Error Analysis

AI Error Analysis contains an AI-generated analysis of detected error events. Instead of reviewing individual error occurrences manually, AI can analyze the available error information and summarize what happened. Use this KPI to: A typical investigation can progress from:

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AI Session Analysis

AI Session Analysis contains an AI-generated analysis of a recorded real user session. It summarizes significant activity within the session so teams can understand what happened without manually replaying the entire session. Depending on the monitored data, the analysis can identify: When additional context is required, teams can open the associated Session Replay to see exactly what the user experienced.

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AI Session Friction

AI Session Friction contains friction identified by AI while analyzing a real user session. Detected friction can include conditions affecting: Examples can include confusing interactions, repeated actions, unsuccessful attempts, abandonment, errors, application slowness, or inefficient workflows. These friction KPIs make it possible to aggregate findings across sessions and determine which problems affect the largest populations.

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AI Session Recommendation

AI Session Recommendation contains recommendations generated by AI from a collection of analyzed user sessions. Rather than focusing only on what happened in one session, this KPI can surface recommendations based on patterns observed across multiple sessions. Recommendations can identify opportunities to improve: For example: This helps teams move from identifying individual issues to determining what should be improved across a broader user population.

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AI Chat Friction

AI Chat Friction contains friction detected by AI within a monitored chat or conversation. AI can analyze conversations involving customers, employees, agents, or bots to identify interactions where the conversation may not be progressing successfully. Examples can include: When chat data is associated with a monitored user session, teams can investigate the surrounding user experience for additional context.

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AI Speed

AI Speed measures the time required for GermainUX AI to process a question and return a response. This KPI provides visibility into the responsiveness of AI Speed itself. Use it to monitor:

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GermainUX AI Request

GermainUX AI Request measures the duration of queries executed against GermainUX by AI. It provides visibility into the time required to retrieve and process GermainUX data used by AI capabilities. Use this KPI to:

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Analyze AI Insights at Scale

Because AI findings are stored as KPIs, GermainUX can analyze them individually or aggregate them across larger populations. For example: This allows teams to move from understanding one user’s experience to identifying the most important issues and opportunities across thousands of users.

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From AI Insight to Evidence

AI KPIs can be correlated with the underlying data collected by GermainUX, including user sessions, application activity, errors, transactions, performance, and other KPIs. A typical investigation can progress from: This allows teams to use AI to quickly identify what deserves attention while retaining access to the underlying evidence for further investigation.

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Pivots

AI KPIs can be analyzed using pivots to understand where AI-generated insights, frictions, recommendations, or performance issues occur. Available pivots vary by KPI and can include:

Pivot Category	Examples
User	User Name, ID, Full Name, Department, Group, Role, Type, Location
Application	Application Name, Component
Session	Session ID, Business Object, Last Friction, Converted, Success
Page	URL, Path, Query, Title
Device	Device, Browser, Operating System
System	System Name, Hostname, Type, Environment, Location
Target	Target Name, Hostname, Type, Environment, Location
HTTP	URL, Method, Path, Query, Status, Message
Business	Business Levels 1–4
Hierarchy	Hierarchy Levels 1–4
Campaign	Campaign Name, Source, Medium, Term, Content
Classification	Category, New Category
Time	Year, Month, Day, Hour, Minute

For example: can help identify which applications generate the most friction, which users are affected, and the individual sessions where the friction occurred.

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Pivots by AI KPI

KPI	Key Available Pivots
AI Error Analysis	System, Target, User, Device, Application, Page, HTTP, Hierarchy, Business, Campaign, Session, Category, Time
AI Session Analysis	System, Target, User, Device, Application, Page, HTTP, Hierarchy, Business, Campaign, Session, Category, Time
AI Session Friction	System, Target, User, Device, Application, Campaign, Session, Last Friction, Converted, Time
AI Session Recommendation	Target, Application, Business Level, Category, Time
AI Speed	System, Target, User, Device, Application, Page, Business, Session, Success, Last Friction, Converted, Time
GermainUX AI Request	System, Target, User, Node, Engine, Source File, Source Trigger, Process/Thread, Category, Time
AI Chat Friction	System, User, Device, Application, Session, Converted, Time

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Measures

Measures provide the numerical values available for analyzing AI KPIs. KPIs without a dedicated numerical measure can still be aggregated, counted, filtered, and analyzed using their available pivots.

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Example Analyses

Combining AI KPIs with pivots and measures makes it possible to answer questions such as: These combinations allow teams to move beyond an individual AI result and determine what is happening, where it is happening, who is affected, how frequently it occurs, and how it changes over time.

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Additional Resources

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The content of this document is proprietary to GERMAIN SOFTWARE LLC.