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.

Metrics: Adds a Metric for Mins with High Network Response Duration (Beta)

Feature Update:

Introduces the Mins with High Network Response Duration metric to identify repeated slow network performance within the app.

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Use Case:

Slice spikes in this metric time series to focus dimensional data on periods with high network request response times to isolate the dimensional entities, such as specific network request hosts and paths, contributed to the delayed service performance. This metric complements the average network request duration metric by highlighting repeated minute intervals with unusually long requests, allowing for deeper investigation and targeted optimization.

More Details: Metrics.

AI Alerts: Supports AI Alerts for Mins With High Page Load Time and Mins With High Screen Load Time Metrics (Beta)

Feature Update:

Introduces the AI Alerts notification for Mins with High Page Load Time and Mins with High Screen Load Time metrics. These AI alerts serve as proactive notifications based on the configured persistence level. Also, notify users of any impacts in real-time. Conviva DPI lists each Mins with High Page Load Time and Mins with High Screen Load Time metric alert on the AI Alerts page, marks the related time series, and links to the diagnostics page for detailed analysis and impact insights.

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Use Case:

Click the AI Alert in the list to open the AI Alert Diagnostics page. Review the Root cause leads, Minimum or Peak value, and other issue details. Additionally, utilize the dimension data for further analysis, which helps to resolve the issue quickly.

More Details: AI Alerts.

Sensor Remote Control: New Option Auto Collection of Clicks

Feature Update:

Introduces a new option, Auto Collection of Clicks, to customize the tracking of user-clicks with the help of:

• Collect Attributes: Specify non-standard HTML attributes (such as data-test-id or app-custom-button-id) using their key value.

• CSS Selectors: Specify tags, classes, IDs, or attributes to precisely target the elements to track.

Note: This feature is available only for the DPI Web (JavaScript) Sensor version 1.1.15 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 Auto Collection of Clicks option under the Sensor Remote Control tab to customize the tracking of user clicks in the app, beyond the buttons and links. Define the non-standard HTML attributes and CSS selectors, such as tags, classes, IDs, or attributes, to customize the auto-click tracking as per business requirements. This helps in defining Out-of-Box (OOB) metrics, resolving UX issues, and even creating meaningful Flows.

More Details: Sensor Remote Control.

Webhooks: Enhances Webhook URL Authentication with Custom Authorization

Feature Update:

Introduces an additional option, Custom Authorization to authenticate Webhook URLs securely. Admins can now provide the Header Key and Header Value to authenticate the URL, enabling more secure and flexible integration with external systems. This enhancement strengthens the overall webhook authentication process, providing a flexible mechanism for validating requests based on organisational requirements.

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Use Case:

Admins can specify a unique header key and value recognized by the internal server. Header-based authentication reduces the risk of spoofing or unauthorized triggers and ensures compliance with internal security protocols, without requiring changes to external webhook configurations. This method supports stringent authentication requirements when the receiving server expects specific authorization.

More Details: Webhooks.

AI Alerts: Supports AI Alerts for Flow Success Metrics (Beta)

Feature Update:

Introduces AI Alerts for Flow Success Rates that trigger when the success rate degrades below the configured success level. These AI alerts provide proactive notifications based on the flow success rate and the configured persistence. Conviva DPI lists each success alert on the AI Alerts page and adds alert indications to the flow time series with links to the AI Alert Diagnostics for detailed analysis and impact insights.

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Use Case:

Monitor the flow success rates for proactive notification when the flow success rate degrades below the configured success rate and persistence. For example, create an AI alert for the login success flow and receive a notification when the login success rate drops below the configured rate. Utilize the AI Diagnostics to identify the root cause and maintain high application availability.

More Details: AI Alerts.

AI Alerts: Supports AI Alerts for Min with 0/4xx/5xx Metrics (Beta)

Feature Update:

Introduces the AI Alerts notification for Min with 0/4xx/5xx network request response code metrics. These AI alerts serve as proactive notifications based on issues with HTTP response code processing, client-side request handling, and service-side request response failures, as well as the configured persistence level. Conviva DPI lists each Mins with x Network Response Code alert on the AI Alerts page, adding alert indications to the flow time series with links to the AI Alert Diagnostics for detailed analysis and impact insights.

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Use Case:

Monitor the network request response code for proactive notification when the performance drops below the threshold. For example, create an AI alert for 5xx response codes and receive the notification for server-side response failures. Utilise the AI Diagnostics to identify the root cause and ensure optimal server-side performance.

More Details: Minutes with Network Request Response Code Metrics.

Settings: Renames Service Integration to More Channels

Feature Update:

Renames the Service Integration to More Channels.

More Details: More Channels.

Activation: New Sensor Remote Control Tab for Blocked Events and Configured Network Request

Feature Update:

Introduces a new tab, Sensor Remote Control, with the existing DPI sensor configuration options, which shifted from the Management tab:

• Blocked Events: To set rules for blocking unwanted events.

• Configured Network Request: To set rules for collecting specific conviva_network_request events.

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Use Case:

After integrating the DPI sensor with the app, use the Sensor Remote Control tab to handle post-integration sensor configurations from one place. Define the collection rules for configuration options, like Blocked Events (to block unwanted events) and Configured Network Request (to collect specific conviva_network_request events).

More Details: Sensor Remote Control.

Semantic Mapper and Metric Builder: New Filter Operator – Match Pattern

Feature Update:

Enables a new match pattern operator to support more flexible event mapping.

Note: The asterisk (*) wildcard represents one or more characters, depending on the data, and must be used with other characters, such as v*/livestreams to represent v1/livestreams or v2/livestreams. A standalone asterisk as a match pattern is not supported. The metadata names, including Cities, Countries, and States, do not support the match pattern operator.

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Use Case:

To track views of livestream experience pages across all versions, instead of listing each version, simply use: page_path, match pattern, /experience/v*/livestreams.

More Details: Semantic Mapper.

Trends: Updated Calculation of Critical and Non-Critical Error Percentages

Feature Update:

Updates the calculations for Critical Error %, and Non-Critical Error % to align with the overall Error %. This update improves clarity and consistency in error reporting.

Note: Error %: Number of Flows with errors divided by the total number of Flows.
Critical Error %: Number of flows with critical errors that caused the conversion to fail, divided by the total number of flows.
Non-Critical Error %: Number of Flows with non-critical errors divided by the total number of Flows.

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Use Case:

Use metrics such as Critical Error % and Non-Critical Error % to understand how errors impact flow completion. To prioritize fixes, focus on Critical Error %, as these are more related to conversion failure.

More Details: Trends.

Trends: Enables Secondary Filters for Flow Avg Network Request Duration Metrics

Feature Update:

Enables secondary filtering on the Flow Avg Network Request Duration metrics. Use a distribution of average request durations to select a secondary filter data range, such as durations greater than five seconds. The distribution ranges provides an easy way to focus the data selection on specific sets of durations, e.g., long-duration network requests that may require higher priority.

The Trends dashboard displays the secondary filter values with a purple line in the time series view and underlined columns in the dimension tables.

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Use Case:

Analyze the PDP To Cart_Change1 Network Request Avg Duration metric for platform-specific PDP to Cart data, and apply a secondary filter to highlight the long average durations to determine the scope and severity of the long durations.

• Select this metric and set VivaMart iOS - 2.5 from the App Name dimension data as the primary filter.

• Click the distribution icon next to the PDP To Cart_Change1 Network Request Avg Duration metric, and select the distribution range >= 5 seconds to apply a secondary filter.

In this example, applying a secondary filter with a duration of 5 seconds or more enables a comparison of the success rate between the overall network request and the subset of sessions with long durations. This primary and secondary filter combination displays the primary filter data alongside the secondary filtered data, highlighted in purple. In this case, the secondary filter highlights the long-duration network requests generated by the VivaMart iOS-2.5 app, which affect overall network performance.

More Details: Trends.

Presets: Introduces New Web and Mobile Experience Presets (Beta)

Feature Update:

Introduces new presets for Web Experience and Mobile Experience, to accelerate experience-based data analysis and anomaly detection. Each preset includes a predefined set of key metrics and corresponding dimension data, offering a structured starting point for investigating trends and identifying issues across critical user experience areas. These presets reduce setup time and promote consistent data analysis across teams.

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Use Case:

Select a platform type as the primary filter and use the presets to gain quick insights into user experiences on that platform.

Set the primary filter to Platform equals to Web and use the Web Experience preset to display Web experience metrics, such as page load time, web errors, active devices, and sessions, segmented by page and web attributes. Similarly, set the primary filter to Platform equals to Mobile and use the Mobile Experience preset to highlight Mobile experience metrics, including screen load time, crashes, and ANRs, segmented by app, device, and screen title.

These presets support efficient issue detection, performance optimization, and root cause analysis, enabling faster resolution of user-impacting issues across web and mobile platforms.

More Details: Presets.

Metrics: New User Session and Active Time Metrics (Beta)

Feature Update:

Enhances the Audience and Engagement metrics in Trends with new user session and active time metrics:

• Users Sessions Metrics:

  • Introduces new metrics for tracking user sessions and active user time, providing more accurate insights into user engagement and session behaviour:

    • User Sessions (Interval)

    • User Sessions (Ended)

    • User Active Time (Interval)

    • User Active Time (Ended)

  Note: Conviva Sensors for Android, iOS/tvOS, and JavaScript support these metrics.

• App Sessions metrics:

  • Renames the following metrics to segregate the users and app session metrics.

    • Active Time is renamed to App Active Time (Interval)

    • Sessions metric is renamed to App Sessions (Interval)

    • Ended Session Count is renamed to App Sessions (Ended)

  Note: These renamed app session metrics will be deprecated soon.

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Use Case:

Use the new user session metrics to identify usage patterns associated with a session.

Similarly, use the renamed app session metrics to evaluate app engagement and retention details. Also, track the active session count, total active time, and average session duration to identify peak usage periods, quantify engagement levels, and evaluate user engagement. Utilize the session interval breakdown to analyze shifts in session length.

More Details: DPI Metrics.