Activation

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.

Feature Update:

Adds a Keep historical data checkbox when editing the flow, enabling control over historical data persistence when flow settings change. This enhancement controls whether historical data is maintained across flow setting changes, allowing complete data refresh for major changes and persistent data for minor updates.

Note: By default, the Keep historical data checkbox is selected.

Click to expand the image

Use Case:

Preserves historical flow data for most setting updates, enabling flow adjustments based on trend analysis and performance tracking over time. The setting is especially beneficial during the initial flow setup phase as iterations with different settings can help refine flow definitions.

More Details: Metric Builder.

 

Feature Update:

Enables network requests associated with Flows based on a list of rules defined by Host and Path, helping pinpoint services or APIs that impact flow conversion.

Use Case:

To pinpoint services or APIs that impact Flows' conversion, analyze the network request durations and network request failures after enabling network requests associated with Flows.

More Details: Metric Builder.

Event-based metrics support duration, conversion, and event aggregation type metrics for measuring user experienced based on events and event combinations. Create event-based metrics in Trends and Activation.

1. Start with the location:

   • Activation Home Page

   • Metric Builder

   • Select metric page in Trends

2. Enter the metric name, and provide the metric information.

   1. (Optional) Metric description.

   2. From the Metric Type list, select the desired metric type.

      • Conversion Metric: Measures the count or conversion rates between eventsr, such as the count of sessions with successful login after opening the login screen to measure the successful login conversion and find highlights to improve user experience.

      • Duration Metric: Measures the time interval between two events, such as the duration between the login screen event and the login success event to measure login durations and quickly respond to impacting login issues.

      • Event Aggregate Metric: Measures the number of events, or calculates the value of event attributes, such as the average duration of network requests, such as the average duration of network requests to measure the network request performance and do optimization accordingly.

3. Identify which events to select:

      Check Predefined Events

   Predefined events include Conviva predefined events and events that are already mapped. Creating custom metrics based on these predefined events can often save time.

   Check the predefined events from the drop-down list for event selection. For example, to analyze the registration process and identify bottlenecks, review the predefined events in the drop-down list. Look for events that indicate the start of registration and successful completion.

   For the details of Conviva predefined events, check Out-of-Box Events.

   Note: To update predefined events, clone them first, then configure the tag keys and values.

      Discover New Events with EventLooker and LiveLens

   In most cases, predefined events may not fully meet metric creation requirements. Need to map the required events and create metrics based on the newly mapped events.

   Use the tools to check raw events and map the events for requirement:

   • EventLooker: Allows browsing and searching events and tag keys. Use the drop-down list to apply a selected event.

     

   • LiveLens: Provides real-time event names and tags while interacting with an application. To learn how to use LiveLens, see LiveLens.

4. Select the events based on the Metric Type selection and configure metric details based on the selected metric type.

      For Conversion metrics

   1. Select events:

      • Initial Event: This refers to the initial event as defined previously.

      • Complete Event: This refers to the follow-up event as defined previously.

        For example, to measure the conversion rate from login start to successful login, select Login Process Start and Login Success.

   2. Validate the event coverage. If the event coverage is not as expected, make updates for the events.

      For example, this screenshot shows that the events apply only to the mobile platform. To track the web platform, update the events accordingly.

      

   3. Configure metric details:

   1. Select the conversion type, Conversion Rate or Conversion Count.

   2. Set a criteria for the desired conversion window (or time boundary/constraint) for the paired events’ occurrence per pairing logic for the fields of Calculate Conversion Rate / Conversion Count of successful conversions within a given seconds/minutes/hours.

      As the same events can appear multiple times in an app process, pairing logic specifies the first or last sequential instance of the initial and follow-up mapped events across multiple event instances.

      • First-First Pair: Uses the first Initial Event and the first Follow-up Event as a pair.

      • Last-First Pair: Uses the last Initial Event and the first Follow-up Event as a pair.

      For example, to set Calculate Conversion Count of successful conversions of First-First pair within 30 seconds and the Initial Event is Login Screen View and the Follow-up Event is Login Success, the criteria is that the two events must occur within 30 seconds to be considered a successful conversion.

   3. Select an option from the drop-down list to define whether a higher or lower metric value is considered positive. For example, choosing Higher is better indicates that a higher value is desirable, whereas choosing Lower is better indicates that a lower value is desirable.

      For Duration metrics

   1. Select events:

      • Initial Event: The first event that triggers a particular process or workflow. It serves as the starting point for measuring and tracking user interactions or system behavior.

      • Complete Event: A complete event that occurs after the Initial Event. It is a continuation or a result of the initial action. The Follow-up Event helps track and measure user engagement or system behavior beyond the initial interaction.

        For example, to measure the average duration from login start to successful login, select Login Process Start and Login Success.

   2. Validate the event coverage. If the event coverage is not as expected, make updates for the events.

      For example, this screenshot shows that the events apply only to the mobile platform. To track the web platform, update the events accordingly.

      

   3. Configure metric details:

   1. Set pairing logic:

      1. Select either of the options:

         • First-First Pair: Uses the first Initial Event and the first Follow-up Event as a pair.

         • Last-First Pair: Uses the last Initial Event and the first Follow-up Event as a pair.

      2. Choose either first pair or multiple pairs to define count in the same session.

         • first pair: selects only the first pair in a session.

         • multiple pairs: selects all pairs that meet the pairing logic in the same session.

         Pairing Logic

      As the same events can appear multiple times in an app process, pairing logic specifies the first or last sequential instance of the initial and follow-up mapped events across multiple event instances.

      For example, suppose there are two mapped events: Login Screen View indicates viewers opening the login page, and Login Success indicates viewers successfully logging into their accounts. In a session, the events are displayed in the following time order: Login Screen View1, Login Screen View2, Login Screen View3, Login Success1, Login Screen View4, Login Success2.

      

      	first-first pair	last-first pair	If selecting first-first pair and first pair	If selecting first-first pair and multiple pairs	If selecting last-first pair and first pair	If selecting last-first pair and multiple pairs
      Event name	Login Screen View1 and Login Success1	Login Screen View3 and Login Success1	Login Screen View1 and Login Success1	Login Screen View1 and Login Success1; Login Screen View4, Login Success2	Login Screen View3 and Login Success1	Login Screen View3 and Login Success1; Login Screen View4, Login Success2

   2. Set the maximum duration limit.

   3. Select an option from the drop-down list to define whether a higher or lower metric value is considered positive. For example, choosing Higher is better indicates that a higher value is desirable, whereas choosing Lower is better indicates that a lower value is desirable.

   4. (Optional) To show the distribution of the metric in DPI, select the Distribution check box and fill out the range.

      Note:  Enabling or disabling the Distribution option for an existing metric does not impact the metric's historical data.

      For Event Aggregation metrics

   1. Select the source aggregation event. For example, to measure the total number of application errors, select Application Error.

   2. Validate the event coverage. If the event coverage is not as expected, make updates for the events.

      For example, this screenshot shows that the events apply only to the mobile platform. To track the web platform, update the events accordingly.

      

   3. Configure metric details:

      1. Set the value calculation type. When Total values or Average values are selected, a tag key must be selected from the drop-down list

         • Number: Sets the value to the count of the source events.

         • Total Values: Sets the value to the sum of the event attribute values.

         • Average Values: Sets the value to the averge or mean of the event attribute values.

      2. Select an option from the drop-down list to define whether a higher or lower metric value is considered positive. For example, choosing Higher is better indicates that a higher value is desirable, whereas choosing Lower is better indicates that a lower value is desirable.

      3. (Optional) Select the Specify the unit of measurement for the collected data check box, and select the required unit.

5. Click Save and Deploy to complete the deployment.

   Note: When updating custom metrics, use the Keep historical data checkbox to control whether existing data is retained. Select the checkbox to keep the data, or clear it to remove it. The checkbox is selected by default.

   The deployment takes several minutes to take effect.

Metric	Definition
Flow Name Init (Use the first initial event)	The number of first initial events within the Flow completion window, maximum 1 hour. Count the First initial event if multiple initial events happen (user configured).
Flow Name Init (Use the last initial event)	The number of first initial events within the Flow completion window, maximum 1 hour. Count the Last initial event if multiple initial events happen (user configured).
Flow Name Complete # %	Complete #: The number of first initial events followed by a complete event within the Flow completion window, maximum 1 hour. Complete %: Complete # / Init
Flow Name Avg Duration	The average time from the first initial even to the complete event within the Flow completion window, maximum 1 hour.
Flow Name Success # %	Success #: The number of completed event pairs (first initial event followed by a complete event within the configured flow success window) Success %: Success # / Init
Flow Name Error # %	Error #: The number of errors encountered after the first initial event within the Flow completion window, maximum 1 hour. Error %: The number of Flows with errors divided by the total number of Flows (represented by Init).
Flow Name Critical Error # %	Critical Error #: The number of errors encountered without Flow complete within the Flow completion window, maximum 1 hour. Critical Error %: The number of Flows with critical errors divided by the total number of Flows (represented by Init).
Flow Name Non-Critical Error # %	Non-Critical Error #: The number of errors encountered despite complete within the Flow completion window, maximum 1 hour. Non-Critical Error %: The number of Flows with Non-Critical errors divided by the total number of Flows (represented by Init).
Flow Name Network Request Avg Duration	The average network request response time after the first initial event within the Flow completion window, maximum 1 hour.
Flow Name Network Request Failure # %	Network Request Failure #: The number of network request failures on or after the first initial event within the Flow completion window, maximum 1 hour. Network Request Failure %: Network Request Failure # / Network Request Count

1. Start with the location:

   • Activation Home Page

   • Metric Builder

   • Flow Widget in Trends

2. Enter the flow name in the Flow Name field.

   For example, to monitor critical steps within user journeys, such as tracking the journey starting from search and ending with video playback, enter 'Search to Video Play'.

3. Identify which events to select:

      Check Predefined Events

   Predefined events include Conviva predefined events and events that are already mapped. Creating custom metrics based on these predefined events can often save time.

   Check the predefined events from the drop-down list for event selection. For example, to track the journey starting from search and ending with video playback, review the predefined events in the drop-down list. Look for events that indicate the a search button click and a video play button click.

   Note: To update predefined events, clone them first, then configure the tag keys and values.

      Discover New Events with EventLooker and LiveLens

   In most cases, predefined events may not fully meet metric creation requirements. Map the required events and create metrics based on the newly mapped events.

   Use the tools to check raw events and map the events for requirement:

   • EventLooker: Allows browsing and searching events and tag keys. Use the drop-down list to apply a selected event.

     

   • LiveLens: Provides real-time event names and tags while interacting with an application. To learn how to use LiveLens, see LiveLens.

4. Select events that signal the start and end of a user flow.

   • Initial Event: signals the start of the user flow in the app.

     It is usually a user interaction in the app (such as a search button click or a page view indicating that users exposed to the search page).

   • Complete Event: signals the positive outcome of a user flow in the app.

     For example, a Page View of Payment Acknowledgement Page or Network Requests with response code indicating payment success

5. (Optional) Validate whether events have sufficient coverage across integrated platforms. If coverage is not as expected, update the events accordingly.

   For example, this screenshot shows that the events apply only to the mobile platform. To track the web platform, include the event and mapping rules from the web platform, or integrate the DPI sensor into the web platform if that is not done yet.

   

6. (Optional) Set experience and performance settings.

   • Error Event: includes errors that matter in the defined flow.

     Select error events from the drop-down list, or add them using the Event Looker Tool.

     Conviva presets default errors, such as "5xx server error" and "app crash", enabling a breakdown of total errors by each error event in the Flow Preset dashboard.

     To make error analysis more actionable, include custom error events that capture login-specific errors such as login errors during the login flow.

   • Network Request: associates the network request with a flow.

     Click +Add Network Request, define the list of Host and Path that representing service and APIs running underneath the flow. The system only consider the network requests that match these rules in the metrics including Network Request Avg Duration, Network Request Failure, and Network Request Count.

     Note: Based on above association, below metrics and dimensions are available in Flow preset dashboard.
     Dimensions: Network Request Endpoint, HTTP Method, Response Code, URL Host, and URL Path
     Metrics: Network Request Avg Duration, Network Request Failure, and Network Request Count
     

7. Configure Flow Widget, such as time constraint and distribution range.

   1. Set the success time boundary which determines flow success.

      For example, setting the login flow success to completion within 90 seconds, labels any flows that exceed 90 seconds unsuccessful.

   2. Set the Flow duration limit.

      For example, setting the custom metric duration limit to 60 seconds excludes any app sessions with durations that exceed the 60 second limit from the metrics.

   3. Set the percentage thresholds to determine whether the success rate is considered Good or Poor.

      Note: Success rate is calculated by dividing the total number of successes (initial events followed by a complete event within the defined time boundary) by the total number of initial events.

   4. Define the expected value range for durations to display on the distribution chart. Toggle to distribution chart from the metric card Avg Duration in the Flow preset dashboard.

      By default, DPI sets the maximum value to double the success time boundary, with the option to manually adjust the range. For example, setting the range to 0–120 seconds displays 40 bars representing the distribution of durations for each completion.

      For example, to visualize acceptable user interaction times, set the duration range from 20 seconds to 200 seconds.

8. Click Save and DPI creates the corresponding metrics automatically. For the flow metric details, see Flow Metrics.

   Note: When updating flows, use the Keep historical data checkbox to control whether existing data is retained. Select the checkbox to keep the data, or clear it to remove it. The checkbox is selected by default.

9. Click Deploy to use the metrics for analysis on the Trends dashboard.

   For example, after deploying the metrics based on the 'search for playing videos' flow, go to the Trends dashboard to review the metrics and identify key insights for optimizing application performance and improving the user experience. An increase in the duration between the initial and complete events indicates slower performance while a decrease in success % indicates issues or blockers to user journey completion within the time constraint.

   Note: A flow can span multiple sessions as long as the time between sessions does not exceed the 5-minute inactivity timeout window and the overall duration is within the 1-hour flow completion window.

This option provides a comprehensive mapping process that enables step-by-step definition and configuration of semantic events.

Explore the Events

The Semantic Mapper page allows the viewing of common events and exploring which events require mapping. To optimize event management, Semantic Mapper displays only high-volume events. Other less frequent events can be mapped manually.

Note: If the required events and their corresponding tag keys and values are already known, the exploration process can be skipped.

1. To access the Semantic Mapper page, from the DPI application, select Activation.

   

   Under the Semantic Mapper tab, drop down lists display on the page and allow the viewing of all events, event tag keys, event tag values, and mapped events. Click Add Dimension to add dimension drop down list to help explore events.

   

2. To view the unmapped events in the specific time range, in the time range bar, select the desired time range, such as May 24 to May 26.

3. Check the unmapped events and confirm which unmapped event requires mapping based on these points:

   • Consolidate event names to simplify event recognition.

   • Group related events to optimize event presentation and correlate app activities.

For example, the event screen_view does not indicate which kind of screen viewers viewed. Map the event with the tag key screen_name and the tag value login_welcome to Login Screen View. This mapping assists in clearly identifying and analyzing a viewer behavior when the viewer views the login screen. This mapping assists in clearly identifying and analyzing a viewer behavior when the viewer views the login screen.

When using Semantic Mapper to map events displayed in LiveLens, keep in mind that some event tag keys names may have slight variations as shown below.

Predefined Tag Key Name in LiveLens	Tag Key Name in Semantic Mapper
Button Click Element Text	buttonClickText
Button Text	conviva_button_click / elementText
Duration	requestDuration / duration
Screen Title	pageTitleAndScreen / conviva_screen_view
Page Url	pageUrl
Page Title	pageTitle / conviva_page_view / page_title
Link Click Element Target	linkClickTarget
Link Target	conviva_link_click
Target Url	requestTargetUrl / targetUrl
Previous Screen Title	previousScreen
Network Request Response Code	requestResponseCode / responseStatusCode

Note: When using requestSize as a tag key, note that the request size is determined by the Content-Length header in the HTTP request, which defines the size of the HTTP body.

4. Confirm the event tag key and event tag value for the event which will be mapped.

   1. To filter all data related with the event, from the dimensional list, click the event. For example, double click screen_view.

   2. From the drop-down list, select Event Tag Key, and then click the specific item of tag key. For example, to map screen_view to a clear event that shows which kind of screen viewers viewed. Click screen_name.

   3. From the drop-down list, select Event Tag Value to view all the tag values of the event tag key, and confirm the event tag values.

      For example, for the event screen_view with the screen_name tag key, the event tag values are login_welcome, login_username, login_link, and so on. To use a single mapped event that indicates viewers viewed login screen, the event tag values is login_welcome.

Set Mapping Rules and Deploy Mapped Events

After confirming the event that needs to be mapped, and the related event tag key and event tag value, set mapping rules for mapping events.

1. To access the page, click +Map New Event from the main page.

2. Fill up the fields in Set Mapping Rules by using one of the options:

   Note: If the event rule matches an existing one, a reminder message appears. Click Yes to apply the duplicate rule to the new event mapping. Click No to cancel the mapping.

   • If there are previously explored events using the mentioned steps, click Map This to set the rules automatically.

     When setting the tag value, there are several conditions and rule options available, such as "contains", "not contains", "equals", "not equals", "is empty", "is not empty", "starts with", and "ends with", as well as "or" and "and" rules.

     Note: Is not empty: Indicates the string contains at least one character. Is empty: Includes cases where the string is either empty (contains zero characters) or null (has no value).

     

     Example:

     For the event _user.login with the event_action tag key, the event tag values are Success and success. To have a mapped event that indicates the success login, use one of the options.

     • Option A: Set the tag Value to contain uccess.

     • Option B: Set a tag value to equal Success, click “Add OR Rule” and then set the tag value to equal success.

   • If there is a clear understanding of the events that need to be mapped and the corresponding tag key and tag value, follow the step:

     • From the drop-down list of Event Name, Tag Key and Tag Value, select the event name, tag key and tag value, or fill them out directly. For example, select screen_view, screen_name, and login_welcome.

       Note: Use multiple conditions and rule options at the disposal when configuring the tag value. Refer to the previous note for further details.

3. Select a suitable category and type the event name in the Set Mapped Event field. For example, use Login Screen View as the mapped name and select Login/Logout.

4. Click Save.

5. On the Semantic Mapper page, click Deploy, select the specific items on the Deployment Confirmation page, and click Deploy.

   

6. After deploying, check the total number of the mapped events on the Semantic Mapper page and also use the mapped events as dimension values on the Trends page.

   Note: Conviva DPI applies event mappings to only newly initiated sessions. Updates to on-going sessions and sessions that restart within the 5-minute inactivity period appear after the next session starts.

1. To access the Block Event page, click Block Event from the main page.

2. On the “Set Block Rules” section, from the “Block Event Name” drop-down list, select the event that needs to be blocked. For example, choose conviva_network_request.

3. To set block rules, choose one of the options based on the requirements.

   • To block all the events selected in step 2, select Block All.

   • To block the specific events that are based on the events selected in step 2, select Block if…, and fill out the key values that the specific event owns. Add multiple block rules for different specific events by clicking Add OR Rule.

     For example, to block the conviva_network_request event that triggered when loading images, select Block if…, and fill out the key values as .png, .jepg, and .jpg.

     

4. To apply the block rules, click Block Event from the top right corner.

 

For example, create a custom dimension based on the Player Name event tag key and select mapped events to determine the dimension values. This new dimension enable the analysis of application player performance across different player dimensions in the Trends dashboard.

1. Click New Custom Dimension.

   

2. Select a tag key from the drop-down list or enter it directly.

   Note: If the entered tag key is not in the drop-down list, after entering it, select the event type which is either global or event-specific.

   

   Note: When creating a custom dimension and entering a tag key name, the Tag Key list displays up to 500 matching tag keys. If the desired tag key is not listed, locate it in the Tag Key table, click the kebab icon next to the tag key name, and select New Custom Dimension to add the tag key to the Tag Key field in the Custom Dimension area. Alternatively, use the global filter in the Semantic Mapper to find the tag key in the Tag Key table.

3. For event specific tag keys, select the events from which the dimension values are extracted. By default, select all mapped events. To use specific mapped events, perform these steps:

   Note: Global tag keys exist in all events, so mapped event selection is not required.

1. Select the mapped event containing the tag key selected in the previous step from the field.

   

2. (Optional) To add more mapped events for the custom dimension values, click Add and select a mapped event. For example, selecting both play start and play end events to extract dimension values for a play duration dimension.

   

   Note: If the event does not appear in the mapped event list, use the Map New Event tab to map the event so it appears in the list.

4. In the Set Custom Dimension field, set the dimension name.

   

5. In the Trends dashboard, click + Dimension from the Dimension table, and check whether the created dimension is in the Custom list. Select the check box to apply the new dimension to the dimension tables.

   

6. (Optional) To edit a custom dimension, choose either:

   • Go to the Management tab, edit the custom dimension from the Custom Dimension table. For more information, check Management.

   • Go to the Semantic Mapper tab, select the desired custom dimension from the dimension drop-down list, click edit, and make necessary updates.

Custom metrics are created based on mapped events. With custom metrics, analyze application performance and user behaviors more efficiently and flexibly.

The Metrics tab provides easy access to review created custom metrics and associated details such as status, mapped events, and user updates.

Searching Custom Metrics

To quickly find a custom metric, enter a portion of its name. For a more refined search, select the Show Filters check box and select the filter conditions to match the desired metrics.

 

Use the Mapped Events table to easily review the current mapped events names and associated details such as status, category, and related metrics. Perform various management actions to optimize the mapped events, such as undeploying and deleting unused events.

Searching Mapped Events

• To quickly locate a mapped event, simply enter part of its name.

  

• For a more refined search, select the Show Filters check box and select the filter conditions to match the desired events.

  

Editing Mapped Events

If the mapped event is unsuitable, directly update the mapped event in the Management tab.

1. Find the mapped event that needs to be modified and click its name.

2. The pop-up window displays event details. Click Edit, edit the event, such as the name, category, and mapping rules, and click Save Changes.

 

Custom dimensions are extracted from event keys to provide enhanced levels of dimensional analaysis in Trends dimension tables and AI alerts. With custom dimensions, analyze application performance and user behaviors more efficiently and flexibly.

With the Custom Dimension , easily review created custom dimensions and associated details such as status, mapped tag keys, and updates made by users. Perform various management actions such as deploying or undeploying each dimension, as well as editing and deleting these dimensions. Create custom dimensions using Semantic Mapper.

Note: Deleting a dimension that is already used in dashboards or filters results in the dimension becoming unavailable for further use. Additionally, pop-up messages appear in dashboards and filters after deletion.

Searching Custom Dimensions

To quickly find a custom dimension, enter a portion of its name.

Editing Custom Dimensions

To make updates to a custom dimension, such as modifying its name or tag key:

1. Locate the custom dimension that needs to be edited.

2. To access the pop-up window for editing, click the custom dimension name.

3. In the pop-up window, click Edit, make the necessary changes, and click Save Changes.

To simplify the process of creating metrics, dimensions, and events similar to existing ones, start with a clone of an existing settings and modify only the required changes.

1. Click the Clone icon in:

   • Tables for the Metrics, Custom Dimensions, and Mapped Events in the Activation dashboard Management tab

   • Metric, Custom Dimension, or Mapped Event Details pages

2. Click Save to apply the updates.

With the LiveLens, efficiently explore application events that occurred to monitor the event flows (initial and follow up events) that can be used to create the most meaningful and actionable metrics.

Note: After opening the LiveLens, data from the past 5 minutes displays and new data continues to be appended from the time when opening the LiveLens.

1. From the DPI menu, click Activation, then click the LiveLens tab on the Activation page to access the LiveLens page.

2. Locate the device running the application in which events will be monitored.

   Note: By default, the Find Myself By IP Address option and the current IP address are automatically selected.

   Select one of the options based on the requirements:

   • Find Myself By IP Address: If the DPI application and the device running the application are on the same network, the current IP address is automatically fetched by LiveLens. If not, enter the IP address that the device is using. Use the search engine on the device to find the IP address.

   • Find Myself By User ID: Use the user account ID to locate the device.

   • Find Myself By Client ID: Use the client ID to locate the device.

     

3. Locate the active user from the pop-up table and click the client ID to open the Real-time Timeline area for that user. Use the search function to find the required user through user ID matching.

   

4. Explore the events in the Real-time Timeline area.

   For example, all the raw events related to the user activities that occurred within 5 minutes after clicking the login button.

   

   Click the Data Expander to display the data details. The details are useful for mapping events. For example, use the conviva_button_click data to determine elememtID: menu-toggle, and elementType:submit. Map the conviva_button_click raw event, by using elementID or elementType as a tag key and menu-toggle or submit as the corresponding tag value.

   

   When using Semantic Mapper to map events displayed in LiveLens, keep in mind that some event tag keys names may have slight variations as shown below.

   Predefined Tag Key Name in LiveLens	Tag Key Name in Semantic Mapper
   Button Click Element Text	buttonClickText
   Button Text	conviva_button_click / elementText
   Duration	requestDuration / duration
   Screen Title	pageTitleAndScreen / conviva_screen_view
   Page Url	pageUrl
   Page Title	pageTitle / conviva_page_view / page_title
   Link Click Element Target	linkClickTarget
   Link Target	conviva_link_click
   Target Url	requestTargetUrl / targetUrl
   Previous Screen Title	previousScreen
   Network Request Response Code	requestResponseCode / responseStatusCode

   Additional functions for efficient event exploration include:

   • Clear button: Clears all displayed raw events and starts showing new events that occur after clicking the Clear button.

   • Auto Scroll: Enables automatic scrolling of the data for raw events, so the displayed events refresh without user intervention.

   • Expand all: Selects the check box to display all data for raw events.

   • Search box: Allows input of keywords to quickly locate matching raw events.

   • Display: The drop-down list shows all raw events grouped by raw event names, with options to clear and select specific events.

   • Network Request: Enables or disables the display of events related to network requests. By default, this option is disabled.

   

Block event allows preventing irrelevant or meaningless events to get collected from the sensor, ensuring the accuracy and relevancy of data ingestion. The Blocked Events list shows the events that are blocked along with the block rule, blocked by user, and blocked date. Use this section to perform various actions such as, searching, modifying, and unblocking the blocked events.

Searching Blocked Events

• To find a blocked event, click the search icon adjacent to the Custom Event column, enter part of the event's name in the search window, and click Search.

  

• To find a blocked event by the blocked by user, click the search icon adjacent to the Blocked By column, enter part of the user's name in the search window, and click Search.

  

Editing Blocked Events

Change the block rule and note for an existing blocked event.

1. Click an event name under the Custom Event column to see details of the block events.

   

2. Click Edit in the event details section.

   

3. Change the block rule and/or update note, and click Save Changes.

   

Viewing Events in Block Panel

View a blocked event in Semantic Mapper page.

1. Click an event name under the Custom Event column to see details of the block event.

   

2. Click View in Block Panel in the event details section to see the blocked events related dimensions in the Semantic Mapper page.

Unblocking a Blocked Event

Unblock an existing blocked events to resume the event collection via ConvivaDPI sensor.

1. Click the Unblock button corresponding to the blocked event, and click Yes on the confirmation message pop-up.

   

   After unblocking an event, it is removed from the Blocked Events list and Conviva DPI sensor starts collecting the respective event data from the integrated app.

Configure Network Request allows defining the collection rules for conviva_network_request events. When the rules are configured, the Conviva sensor collects network request events accrding to the matched rules. Select the collected events as raw data for mapping, if necessary.

Configure the network request data collection rules for multiple categories, such as metadata usage, EMP query fields, GraphQL, tag value, and metrics API.

Collection Rule Order

Network request collection rules are applied based on the most recently changed rules, and then rules following the displayed category order. If a network request does not meet any configured rules in a category, the collection rule matching process stops and that network request data is not passed to DPI.

There are two methods for collecting conviva_network_request events:

• Blocked events: blocks events matching blocked event criteria and collects all other conviva_network_request events.

• Collection rules: collects events matching allowed event criteria and blocks all other conviva_network_request events.

Note: Events matching the blocked events criteria are blocked even if they also match any collection rules.

Creating Collection Rules

1. Click Add New in the Browse Configuration section, on the left pane.

2. Set the collection rule in the New Configuration secion, on the right pane:

   1. Condition Name: Type a name for the collection condition.

   2. Define the URL Conditions by selecting:

      • All: Enables rules to take effect on all conviva_network_request events.

      • Contains: Enables rules to take effect on events containing the Target Urls.

   3. Set collection rules using Request Header, Request Body, Response Header, Response Body, separately.

   Note: Click Add to include more rules if necessary.

   

      Example

   Suppose you want to gather information about the keywords users search for and conduct in-depth analysis based on that information. To obtain the necessary data, you need to collect the relevant conviva_network_request events.

   • Configuration Name: Search Keywords

   • URL Condition: Select Contains and type search in the Target Urls field. This is required.

   • Set Collection Rules with:

     • origin as Request Heade.

     • keywords as Request Body.

     • Leve the Response Header field empty.

     • result.count, result.name, and result.category as Response Body.

     

3. Click Save. Conviva DPI sensor starts collecting the conviva_network_request events that match the rules.

 

Customize the user-click tracking across the application, beyond tracking the click events for buttons and links. This section consists of two tabs, Collect Attributes and CSS Selectors. Use the default attributes and CSS selectors, and also create additional custom tags, CSS selectors, and attributes to track user clicks for various purposes, such as defining Out-of-Box (OOB) metrics, resolving UX issues, and even creating meaningful user Flows.

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.

 

Use the toggle button at the section header to enable/disable this feature for the DPI sensor.

Collect Attributes

Specify the non-standard HTML attributes (such as data-test-id or app-custom-button-id) using their key value. By default, DPI sensors support collecting user-click events for certain attributes, such as, id, class, name, type, placeholder, value, a.href, area.href, a.target, area.target, and xlink:href.

In the Custom Attributes section, click the Add Attribute button to track user clicks on more custom attributes such as, src, title, alt. Click the Save button to deploy.

Note: Sensors may take 30 minutes to reflect the changes.

CSS Selectors

Define tags, classes, IDs, or attributes to precisely target the elements you want to track. By default, DPI sensors support collecting user-clicks for the native clickable selectors, such as button, a, select, textarea, input:not([type="hidden"]), summary, video, and audio.

In the Default Virtual Clickable Selectors section, use the toggle button to enable/disable the pre-defined click tracking for the virtual selectors.

In the Custom CSS Selectors section, click the Add CSS Selector button to specify the custom CSS Tag or custom CSS attribute for click tracking.

• To define custom CSS tag:

  1. Click the Add CSS Selector button.

  2. Select the option Tag from the drop-down list.

  3. Specify tag name in the adjacent field.

  4. Click Save to deploy.

Note: Sensors may take 30 minutes to reflect the changes.

 

• To define custom CSS attribute:

  1. Click the Add CSS Selector button.

  2. Select the option Attribute from the drop-down list.

  3. Specify attribute name in the adjacent field.

  4. Select a condition (Starts With, Ends With, Equals, and Contains) from the drop-down list.

  5. Specify the value to match based on the selected condition.

  6. Click Save to deploy.

Note: Sensors may take 30 minutes to reflect the changes.

For more information about Auto Collection of Clicks, see Sensor Remote Control.

Cohort Replay enables the visual reconstruction and review of user navigations and actions within a customer's app. This feature captures how users interact with the app, including clicks, navigation, scrolling, media engagement, and text input, and then replays these interactions in a video-like experience. Cohort Replay provides direct visibility into how customers interact with digital platforms and user behaviors, based on a sampling percentage of user sessions. For data compliance, Conviva applies configurable masking levels (light, medium, or advanced) to protect Personally Linked/Locatable (PLL) data.

Note:  Cohort Replay is disabled by default. As a prerequisite, customers must upgrade to the latest sensor version. To enable and use cohort replay, contact your Conviva representative to configure the sensor remote control settings, adjust the sampling percentage, and set the masking level according to your specific application requirements.

Supported Platforms and Versions: Web (JS) version 1.4.3 and above.

Use Cohort Replay to:

• Understand the details in user behaviors and friction points

• Link from Funnel and metric anomalies to visual behavior representations for quick user behavior optimizations.

• Analyze the impact of user experience on flagged AI Alerts and check for anomalies.

• Monitor the exact sequences of user actions to analyze behavior and UI improvements.

Launching Cohort Replay

Access the Cohort Replay from the Trends or Users Dashboard,

1. Navigate to Trends >> Cohort Replay Tab

   

2. Click a Client ID or Cohort Replay icon associated with the user session to see the session details in a new User dashboard tab.

   

3. On the Users timeline, click the Cohort Replay icon adjacent to the Event Time Stamp to see the user timeline details with the cohort replay.

   

   The player enables detailed session analysis through the following features:

   • Events Indicator: Colored dots display events, network requests, and errors. Hover the legend next to the share button to see the different types of events associated with the User Timeline.

   • Playback Controls: Options are provided to pause playback, seek to a specific position, fast forward by ten seconds, and rewind by ten seconds.

   • Session Sharing: Share the sessions for collaborative investigation of issues.

   • Settings: Speed control options allow adjustment of playback speed.

   These capabilities support precise navigation, issue identification, and collaborative troubleshooting.

4. On the Cohort Replay screen, click the Configure drop-down and select Configure Replays to view the Cohort Replay configuration page in Sensor Remote Control.

   

Configuring Cohort Replay

Use the Session Remote Control tab to enable the Cohort Replay, which allows to set the sampling percentage to specify the proportion of user sessions captured for Cohort Replay, and define the masking level to secure the PLL data.

• Cohort Replay: Toggle to start recording the user sessions based on the specified sampling percentage, with the capability to replay the sessions.

• Sampling: The sampling percentage specifies the proportion of user sessions captured for Cohort Replay. The value entered in the Sampling Percentage field determines the percentage of sessions recorded.

• Masking: Conviva offers a data masking feature to ensure the security of Personally Linked/Locatable (PLL) data during session recordings. When this feature is enabled, sensitive information is not stored or exposed in recorded sessions. Conviva supports different configurable masking levels, allowing customers to strike a balance between data protection and operational visibility. This setting provides robust protection while maintaining operational visibility.

  There are three levels of masking:

  • Light (Minimum): The cohort replay does not capture any user email IDs, passwords, telephone numbers, or mobile numbers.

  • Medium (Default): In addition to the Light masking, the cohort replay does not capture text or text areas. Conviva recommends the Medium masking level.

  • Advanced (Conservative): In addition to Medium Masking, the cohort replay does not capture the Date Picker, Color, Search, and All Text Inputs.

Use Cases

• Cohort Replay provides visibility into funnel conversion and related metrics. It captures the exact user experience with visual context, enabling the identification of anomalies or performance deterioration that may impact business outcomes.

• The timeline view provides a visual representation of the user experience, facilitating drill-down analysis and supporting the identification of friction points, as well as analyzing user behavior.

• Cohort Replay links anomalies to the corresponding user sessions. It displays the related replay when you select an anomaly in AI Alerts, providing a detailed analysis of user behaviour associated with the anomaly.

Technical Features and Limitations

During this beta release,

• Input Masking: While the system masks sensitive fields (passwords, emails, text, etc.) by default, it does not yet mask Date, Month, Range, Time, or Week inputs; instead, it replaces them with static placeholder values (e.g., dd/mm/yyyy for Date).

• For any static resources that are required to be downloaded from a remote server, cross-origin access is required.

• Does not support cohort replay for full-screen video playback using the native <video> element.

  Cohort Replay supports navigation through multiple tabs and subdomains, providing a seamless playback experience during replay sessions.

• Recording Persistence

  • Incomplete Uploads: If a user closes the application or browser before the recording has been uploaded, the corresponding session content will not be visible in Pulse. Recording resumes only when the browser or application is relaunched.

  • Remote Disablement: If recording is disabled remotely (through configuration) after a session has been captured, there is a risk of losing up to the last one minute of replay data.

• Performance and Rendering Constraints.

  • UI Rendering Behavior: Lazy-loaded interfaces may cause playback inconsistencies, including occasional overlaps or artifacts in the recorded session.

  • Event Volume Limit: Automatically discards recorded events once the event count exceeds 5,000 entries, which may truncate longer or more interaction-heavy sessions.

• Content Capture Gaps

  • PDFs Not Recorded: Content rendered within PDF viewers is not captured and do not appear in cohort replay playback.

  • Shadow DOM:

    • Does not capture inputs within the open Shadow DOM.

    • Does not capture elements in the Closed shadow DOM and therefore remains invisible to replay.

• This beta release supports Cohort Replay for web applications only. Cohort Replay for mobile devices is not available in this release.

• Sampling percentage and masking levels are read-only within the platform interface. Contact your Conviva Representative for setting adjustments.

• Cohort Replay does not support session recordings with Canvas and other player interaction tools.

• Session recordings do not support session recordings of web applications embedded with multiple <iframe> elements.