Funnels tab

  • Release version: Xanadu
  • Updated August 1, 2024
  • 3 minutes to read
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    Summary of Funnels tab

    The Funnels tab in Virtual Agent provides a powerful way to perform cumulative filtering on conversation flows. This feature helps you analyze how effectively users engage with different steps in a conversation, enabling you to identify drop-off points and completion rates. Funnels can contain up to 10 filtering steps, each refining the previous step's results to narrow down insights on user interactions within a conversation flow.

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    When you run a funnel for a specified date range, you receive key metrics such as the number and percentage of users at each step, drop-off rates, and completion percentages. This granular visibility helps you understand user behavior and conversation flow performance in detail.

    Note that the existing Conversational Analytics dashboard is being deprecated and replaced by a new, FedRAMP-authorized Conversational Analytics dashboard available in the Platform Analytics experience. Migration guidance is provided for existing users.

    Key Features

    • Cumulative Filtering: Funnels apply step-by-step filters to conversation flows, allowing detailed analysis of user paths.
    • Step Configuration: Each funnel step consists of a field, an operator, and a value to define precise filtering criteria.
    • Metrics Reporting: Displays user counts and percentages for each step, including those who drop off or complete the flow.
    • Comparison Over Time: Funnels compare performance metrics for the selected date range against a prior equivalent period to track trends.
    • Management: Users can create, edit, and delete funnels to tailor analysis to their needs.

    Practical Use Case

    For example, an administrator can create a funnel to track how users interact with the "Software Access" topic in Virtual Agent. The funnel might include steps that filter users who followed the topic, requested drive access, and then requested a transfer to a live agent. Metrics from this funnel reveal how many users proceed through each step and where drop-offs occur, highlighting areas for improvement—such as reducing transfers to live agents.

    Benefits for ServiceNow Customers

    • Gain actionable insights into user engagement and conversation flow efficiency.
    • Identify bottlenecks or drop-off points to optimize Virtual Agent interactions.
    • Measure the impact of changes by comparing conversation flow performance over time.
    • Support compliance requirements with the new Conversational Analytics dashboard in Platform Analytics.

    Using funnels empowers you to continuously refine and improve Virtual Agent performance, ensuring better handling of user queries and enhanced customer experience.

    Funnels provide cumulative filtering of conversation flows. Using funnels, you can identify whether your conversation flows are performing effectively when users chat with Virtual Agent.

    Important:

    Conversational Analytics dashboard is being prepared for future deprecation. It will be supported until deprecation but will no longer be available for installation. A new Conversational Analytics dashboard in Platform Analytics experience, which meets the compliance requirements of Government Community Cloud (GCC), and thus FedRAMP authorized, is available. See Conversational Analytics dashboard in Platform Analytics experience.

    For details on the deprecation process, see the Deprecation Process [KB0867184] article in the Now Support Knowledge Base.

    If you are an existing user of this dashboard and want to migrate analytics data to the new dashboard, see Migrate data to Conversational Analytics dashboard in Platform Analytics experience [KB1651556].

    Figure 1. Funnels page
    Select the Funnels option on the menu to view the Funnels page.

    Video link to Funnels demo. Funnels demo Watch this video for an overview of Funnels.

    Overview of funnels

    Funnels filter conversation flows using steps that are defined when a user creates the funnel.

    A funnel can contain up to 10 filtering steps for a conversation flow. Each subsequent step further refines the results from the previous step. This type of cumulative filtering helps you to easily narrow down on the data of interest at each step of the conversation flow.

    When you run a funnel for a particular date range, the system displays the following metrics that show the number of users at each step:
    • The percentage and number of users who have used the specified conversation flow.
    • The percentage and number of users who proceeded to the next conversation step specified in the funnel.
    • The percentage of users who dropped off at a particular conversation step.
    • The percentage and number of users who completed all specified conversation steps in the flow.
    • The biggest drop-off point or step where users left the conversation flow.
    Each step in a funnel consists of the following:
    • Field: The item on which the step is evaluated.
    • Operator: A list of operators that is contextually generated based on the selected field.
    • Value: A text entry field or a list that is contextually generated based on the selected field.

    For more information about filtering options in steps, see Legacy - Filter options in funnels.

    Use case for funnels

    Consider an example scenario where an admin has to get insights about how Virtual Agent is handling user queries in a conversation flow. To review the efficiency of the conversation flow, the admin might look for information such as the following:
    • What percentage or number of users have interacted with Virtual Agent.
    • Out of the users who interacted with Virtual Agent, what percentage or number of users have followed a specific node in the topic during the conversation.
    • Out of the users who used the specific node, what number of users requested for a transfer to a live agent.
    For example, see the following funnel for fetching metrics on a conversational flow that provides software access.
    Figure 2. Funnel
    The filter specifies the Software Access standard topic in which the Drive Flow Executed topic node has run and the user requested a transfer to a live agent.
    Here, the funnel has three filtering steps:
    • Step 1 fetches users who have followed the Software Access topic while interacting with the Virtual Agent.
    • Out of the retrieved users from step 1, step 2 fetches users who requested for a drive access in the Drive Flow Executed node.
    • Out of the retrieved users from step 2, step 3 fetches users who requested transferring to a live agent.

    Metrics for funnels

    Using Funnels, you can easily filter conversation flows and get information as metrics. Metrics indicate what percentage or number of users are active at each step of the conversation flow.

    You can improve the conversation flows based on the performance metrics derived from using funnels. The metrics help identify opportunities for improving conversation flows so that Virtual Agent can handle your user queries better.

    Using the previous example, that funnel displays the following metrics:
    Figure 3. Metrics for funnels
    The metrics show the percentage and number of users who made it through the steps and the step that experienced the biggest drop-off point for users.
    Here, the metrics indicate the following for the selected date range:
    1. 134 users followed the Software Access topic.
    2. Out of these 134 users, 85 users requested for a drive access.
    3. Out of these 85 users, 23 users requested for transferring to a live agent.

    These example metrics indicate an opportunity for improvement because 23 users requested for a transfer to a live agent.

    Other benefits of using funnels

    Users can create funnels to get insights on their conversation flows. Additionally, they can edit and delete existing funnels created by other users. For more information, see Legacy - Create and manage funnels.

    You can compare the performance of previous and current conversation flows. Funnels show metrics for the specified date range. Additionally, it shows the comparison for the same number of days in the date range prior to the specified start date. You can know the increase or decrease in users who have made through all the steps.
    Figure 4. Previous metrics
    The change from previous metrics displays at the bottom of the card. For example, the percentage of users may display as a 25% increase from the previous 8 days.