Metric Creation: Enhances Ratio Metric with Fixed Number as Denominator

Feature Update:

Enhances Ratio metrics to support using a fixed number as the denominator, expanding the flexibility and applicability of Ratio metrics.

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Use Case:

Use the fixed number as a Ratio metric denominator to create Ratio metrics for analyzing revenue across currencies, such as euros based on U.S. dollar data. Apply the current USD-to-EUR exchange rate as the fixed denominator to calculate the Ratio metric for common currency analysis.

More Details: Custom Metrics.

Metrics: Adds Metrics (Beta) for Excessive App Restart, Page Loop, Click → Web Error / Mobile Crash. Also, adds User Session and User Metrics for ANR, App Restart, Page Loop, Click To Web Error/Mob Crash, and Rage Clicks

Feature Update:

Frustration Signals are measurable indicators derived from user interaction data that reflect potential user dissatisfaction, confusion, or friction during a digital experience. These signals identify behavioral or technical anomalies observed in digital experiences.

Adds new Frustration metrics (Beta);

• Excessive App Restarts measures the frequency or the number of occurrences when an application launches two or more times within a non-overlapping 60-second tumbling window. The application restarts may occur because of the app not responding, app instability, crashes, or forced closures by the user, indicating app session instability and multiple relaunches in quick succession.

• Page Loop detects repeated page visits that likely indicate poor user experience caused by navigation issues or inconsistent state handling. This metric measures the number of 15-second intervals during which the same page URL was visited three or more times.

• Click To Web Error / Mobile Crash captures the number and percentage of user interactions that are directly followed by a web error or mobile crash.

  • Web Error – For web users, a web error (any recorded client or server-side error event (e.g., JavaScript error)) that triggered in response to a click.

  • Mobile Crash – For mobile users, a fatal error that resulted in the immediate termination of the mobile application after a click.

• Rage Clicks measures the frequency of users engaging in rapid, repetitive clicks on the same UI element, typically indicating a high level of user frustration. This metric tracks instances in which users perform three or more clicks on the same clickable UI element within a two-second time window without any meaningful interaction or response.

Enhances exisiting Frustration metrics with companion metrics for related users and user session analysis:

• User Sessions with Page Loop measures the number of sessions that contain a page loop.

• User Sessions With Excessive App Restarts measures the number of user sessions with Excessive App Restarts.

• User Session with Click To Web Error / Mobile Crash measures the number of user sessions with Click → Web Error / Mobile Crash.

• User Sessions with Rage Clicks measures the number of sessions that contain Rage Clicks.

• Users with Page Loop measure the number of users who visited the same page URL in a web app three or more times within 15 seconds.

• Users with Click To Web Error / Mobile Crash measure the number of users who experienced a web error or mobile crash after a click.

• Users with Excessive App Restarts measure the number of users who experienced the app restarts (>= 2), indicating restart loops within the 60-second intervals.

• Users with Rage Clicks measure the number of users who performed three or more clicks on the same clickable UI element within a two-second time window without any meaningful interaction or response.

Spikes and anomalies in a metric time series help quickly identify looping behavior caused by UX flaws, misconfigured redirects, or technical errors, thereby enhancing user journey efficiency and flow stability.

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Use Case:

Slice a Page Loop spike in this metric time series in Trends and use the dimension tables to diagnose the issue across the related Flow dimensions for page URLs and validation errors to highlight areas for UI improvements.

Use the time series for User Sessions with Page Loop to view the number of sessions that contain page loops.

Use the time series for Users with Click To Web Error / Mobile Crash to identify the number of users who clicked the “Play” button on Android and experienced a high rate of crashes.

More Details: DPI Metrics and Introduction to Frustration Signals.

Navigation: Access AI Cohorts from AI Alerts Dashboard and AI Config

Feature Update:

Updates the AI Alerts dashboard and AI Config page to include a direct navigation option to AI Cohorts, improving usability and streamlining workflows. This enhancement allows users to seamlessly transition between related AI modules, reducing navigation time and providing a more intuitive user experience.

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Use Case:

Instead of left navigation, use the option to navigate to AI Cohorts from the AI Alerts dashboard or the AI Config page for easier access.

More Details: AI Cohorts and AI Alerts.

AI Cohorts: Enhances AI Cohorts to Customize the Knowledge Graph for Every Metric and Add Custom Dimensions.

Feature Update:

Enhances the AI Cohorts with an option to add custom dimensions and customize the Knowledge Graph for every metric. This update helps to easily configure custom dimensions as single dimensions or in groups of two or three dimensions to focus AI alerting on specific performance areas, such as an underperforming channel or CDN edge server / OS family combination.

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Use Case:

Select the metric from the drop-down in this example Avg. Screen Load Time, and add the custom dimensions, along with the displayed standard dimensions, to customize the knowledge graph and focus on alerts for underperforming assets. Click the Alert to open the AI Alert dashboard, analyze the dimension data, and drill down to the anomaly.

More Details: AI Cohorts.

AI Cohorts: Removes the AI Cohort Link from the Pulse Navigation Menu

Feature Update:

AI Cohorts in the left menu is now removed. Use the option to navigate to AI Cohorts from the AI Alerts dashboard or AI Config page.

More Details: AI Cohorts and AI Alerts.

AI Alerts: Supports AI Alerts for Mins With High Network Response Duration

Feature Update:

Introduces AI Alerts support for the Mins with High Network Response Duration metric. AI Alerts for this metric serve as proactive, real-time notifications of potential client, network, or server performance issues based on the configured persistence level. This notification includes alert description and related root cause details, along with alert markers in the metric time series widgets of the Trends dashboard. Click an alert to see detailed troubleshooting and optimization on the AI Alert Diagnostics page. AI Alert data can also be retrieved using the AI Alerts API and Webhooks.

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Use Case:

When monitoring the Mins with High Network Response Duration metric time series in Trends, click the AI Alert marker that indicates a sudden spike in response durations beyond a configured persistence level. In the AI Alert Diagnostics page, use dimensional drill-downs to confirm the root cause and optimization steps.

More Details: AI Alerts.

Metrics: Adds Mins With xxx Network Request Response Code

Feature Update:

Introduces the Mins with xxx Network Request Response Code metrics,

• Mins with 0 Network Response Code

• Mins with 4xx Network Response Code

• Mins with 5xx Network Response Code

To help analyze the different types of network request performance issues in app sessions based on the number and percentage of one-minute intervals during which the performance issues occurred. These metrics actively categorize performance issues arising from HTTP response code processing, client-side request handling, and service-side request response failures. They continuously track the frequency and impact of response codes, enabling proactive notifications and facilitating cross-dimensional drill-downs to quickly isolate and analyze network request performance issues across various dimensions.

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Use Case:

Set up AI Alerts for Mins with xxx Network Response Codes to enable real-time alert messages and time series alert markers. For example, starting with a highly impacted device OS version, analyze response code impacts across network request URL hosts, paths, and endpoints to quickly focus on the most likely root cause of the persistent network request issue.

More Details: Minutes with Network Request Response Code Metrics.

Sensor Remote Control: New Option Intelligent Collection of Network Request

Feature Update:

Introduces a new option, Intelligent Collection of Network Request, to optimize the volume of network request collection by overriding the default URL blocklist with the Collect/Block rules.

Note: This feature is available only for the DPI Android 1.1.0 and above, Web (JavaScript) 1.1.1 and above, and iOS 1.0.1 and above. Conviva recommends updating the sensor to its latest version.

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Use Case:

After integrating the DPI sensor with the app, use the Intelligent Collection of Network Request option under the Sensor Remote Control tab to configure the rule using hostname, path, or scheme-based filters for collecting a specific set of network requests. If needed, use the response codes and duration-based filters for more granular-level collection. This feature offers the following benefits:

• Controlled Costs – Store and process only meaningful data, optimizing resource utilization.

• Optimized Performance – Smaller datasets mean faster insights and lower latency.

• Enhanced Insights – Eliminating noise helps surface key trends and anomalies.

• System Integrity – Prevents overloading downstream systems with redundant data.

More Details: Sensor Remote Control.

Dimensions: Adds UTM Parameters in Trends

Feature Update:

Introduces the Urchin Tracking Module (UTM) parameters in Trends, enabling dimensional drill-down for tracking digital marketing performance. The UTM parameters as dimensions in Trends, enable deeper analysis of the source, medium, and context of marketing-related traffic along with DPI metrics, supporting unified data-driven decision-making.

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Use Case:

Enable UTM Campaigns or IDs as a dimension in Trends to perform comparative analysis across marketing activities using DPI metrics, such as Active Users. Also, confirm app-related performance using metrics such as Mins Impacted, Avg Mins Per Page, and other engagement metrics.

More Details: Trends.

Metrics: Introduces App Restarts Metrics (Beta)

Feature Update:

Introduces the App Restarts as a Frustration metrics in Trends,

• Excessive App Restarts : measures the number of times an application launch two or more times within a 60-seconds. The Excessive App Restarts metric highlights potential crash–restart loops caused by technical issues, poor user experience when users repeatedly relaunched the app due to failures or sluggish performance, and rare cases when device or OS-level constraints forced repeated terminations.

• User Sessions With App Restarts : measures the number of user sessions with excessive app restarts.

  By monitoring this metric, administrators can quickly identify stability and usability concerns based on the scope of impacted user session.

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Use Case:

Comparing Excessive App Restarts and User Sessions with App Restarts can help quickly identify the abnormal app restarts impacting large number of users that may signal potential app crashes or a restart loop. For example, repeated spikes in Excessive App Restarts that impact large number of user sessions across a daily time series can indicate regular surges in high usage that strain system resources, such as memory, CPU, and storage, caused by memory leaks or misconfigured resource limits.Operations teams can monitor and correlate repeated-restart patterns to address stability issues before they significantly impact user experiences.

More Details: Metrics.

Trends: Enhances Dimension Support for Custom Metrics and Flows

Feature Update:

• Combines together event-specific dimensions from the Network & Request and Errors categories into the Custom Dimension category in the Preset dashboard. This change makes it easier to select flow-related dimensions from a single category and prevents duplicate dimensions from appearing across other categories when they are tied to flow creation events.

• Adds support for associating event-specific dimensions with custom metrics and predefined dimensions (Element Classes, Element Id, Element Name, Element Text, and Element Type) with Flow metrics, enabling drill-down analysis for both custom and Flow metrics.

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Use Case:

Use event-specific dimensions to drill down into multiple metrics within the same dimension table for deeper analysis. For example, in the flow Login Click to Success, apply the Element Text dimension to compare Flow metrics such as Init, Abandoned Flows with Errors, and Errors in Abandoned Flows side by side in a single table.

More Details: Flows.

Flow Builder and Flow Preset: Updates Flow Metrics

Feature Update:

• Renames several Flow metrics to improve clarity and enable deeper error analysis:

  Previous Metric Name	New Metric Name	New Metric Name Clarifies that:
  Error	Flows with Errors	An error occurred within the flow.
  Critical Error	Abandoned Flows with Errors	The flow failed to complete with an error.
  Non-Critical Error	Completed Flows with Errors	The flow completed with error.
  Avg Duration	Avg Complete Duration	The average duration applies to complete flows.
  Active Devices	Unique Devices	To specify the count is for unique devices.
  Active Users	Unique Users	To specify the count id for unique users.

• Adds new Flow metrics for Total Error Count, Errors in Abandoned Flows, and Errors in Completed Flows.

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Use Case:

Use the new error-related Flow metrics for deeper drill-down error analysis. For example, compare Errors in Abandoned Flows with Errors in Completed Flows to identify which errors are critical and prevent flow conversion.

More Details: Flows and Metrics.

Flow and Metric Creation: Adds Duration Limit Setting Option

Feature Update:

Adds support for a Flow and custom duration metric timeout setting, enabling a more focused Flow and metric analysis that excludes sessions with durations beyond the specified limit.

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Use Case:

Set a flow timeout limit to align flow duration limits with specific user-journey duration limits.

For example, a 5-minute timeout limit for cart checkout Flow sessions aligns with typical checkout page timeouts and prevents outlier checkouts that last more than 5 minutes from skewing the analysis of the overall checkout Flow behaviors.

More Details: Flows and Custom Metrics.

DPI Dashboards: Introduces Overview Dashboard for DPI (Beta)

Feature Update:

Introduces the DPI Overview Dashboard with a comprehensive view of application performance and user activities in configured Flows. This dashboard enables quick identification of performance trends, user engagement levels, and potential issues in a single, consolidated interface.

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Use Case:

For dips in payment checkout success, use the DPI Overview Dashboard to monitor overall Active Users and User Sessions, along with other Flow performances. A sharp drop in Checkout Success Flow at the payment step coincided with dips in other Flow performance. The AI-Alerts panel highlights multiple sessions affected by Page Load Time, causing it to time out, thus pinpointing the root cause. With this insight, the team can quickly address the anomaly, thereby reducing downtime.

More Details: Overview.

Dimensions: Introduces High Cardinality Dimensions

Feature Update:

Introduces a High Cardinality button that categorizes High Cardinality dimensions in Trends. A dimension is considered as high cardinality if more than 80% of the total dimension values are unique. Typically, High Cardinality dimensions contain millions of values where most entries are unique. Examples include Order ID, Error Code, or Search Query, with high levels of unique entries. Clicking the High Cardinality button displays the lists of up to 200 sample events from the last 24 hours. Use the Client ID or User ID links for further analysis.

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Use Case:

Using the Transaction ID dimension for e-commerce platforms with high cardinality of unique Transaction IDs, the High Cardinality page displays up to 200 recent transaction IDs in a separate event table from the last 24 hours, so analysts can check issues while still relying on aggregated views (e.g., payment method, order status) for broader insights.

More Details: Trends.

AI Alerts: Introduces New Sensitivity for Rate-based Metrics

Feature Update:

Introduces new AI Alerts sensitivities for rate-based metrics and Flow success levels, based on the incremental-impacted Flows and metric-related sessions, along with the percentage deviation above the anomaly baseline.

• Incremental Impacted Count exceeds (default set to 10)

• % Incremental Impacted Count of the Root Cause exceeds (default set to 0%)

AI Alerts monitor the impacted counts and anomaly deviation percent settings for alert generation. For example, if the success level of the Login Success Flow drops from 98% to 95%, the anomaly baseline was 96%, and the number of Flows represented by this 1% drop is greater than or equal to the specified impacted count of 10 Flows; these conditions meet the AI alert settings (incremental impacted count exceeds 10 Flows and the anomaly baseline deviation percentage exceeds 0%).

Note: Setting higher sensitivity levels throttles the AI alert firing until a higher count and percentage are impacted. Setting extremely high settings can effectively disable AI alerting. Admins should try different sensitivity levels for different Flows and rate-based metrics to determine which levels work best for their apps.

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Use Case:

Admins can set the sensitivity of rate-based metrics and Flows, such as Minutes with High Screen Load Time and Login Success flow, to determine when to receive AI Alert notifications. In this example, an alert is triggered when the impacted count goes above 10 Flows and the percentage of Flow success dips 0% or more below the Al alert anomaly baseline, notifying admins of issues impacting user logins. Clicking an AI Alert notification opens the diagnostics page with alert details and enables dimensional drill downs to analyze root causes.

More Details: AI Alert Sensitivity.

Usage Dashboard: Enhances the Usage Dashboard to Monitor VSI, DPI and API Usage

Feature Update:

The Usage Dashboard provides a centralized view of VSI and DPI consumption across all Conviva billing accounts. This dashboard enables teams to track usage at both a monthly and interval-level granularity, ensuring greater transparency and control over resource utilization.

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Use Case:

Admins can review the dashboard to identify peak viewing periods and correlate them with custom-mapped engagement events. By analyzing session averages and user journey durations, the admin can point the latency issues during high-traffic intervals and can initiates targeted optimizations.

More Details: Usage Dashboard.