Tour Analytics

Understand user interactions to improve tour performance and identify challenges.

Tour analytics offers a detailed look into how users engage with tours, helping to uncover patterns and troubleshoot issues. Tracking metrics like views, completions, and drop-offs gives a clearer picture of where users are succeeding or getting stuck. Features like comparing different time periods or user segments, along with step-by-step breakdowns, allow for more focused analysis. This approach makes it easier to refine tours based on real user behavior and identify areas that need attention.

You may find that results in our new analytics don’t perfectly match the old reports. Don’t worry—this is expected. Our latest system applies refined data collection techniques and enhanced calculations, which can lead to discrepancies from older figures. These variations simply reflect an improvement in how we track and report on your activities, giving you deeper and more reliable insights.

Getting Started

Select a tour from the dropdown menu in the Tours Analytics section to start analyzing your tours. The dropdown menu shows recent items, but if the desired tour isn’t immediately visible, begin typing its name to locate it.

If you don't select a specific tour, the analytics will automatically display combined metrics for all tours. However, you have the option to select one or more individual tours. Published tours are clearly marked, making it easy to identify them at a glance.

Set a Date Range

You can define a date range to analyze your tours over a specific period by selecting either a preset range or a custom interval. Preset ranges offer quick options such as the last 7 days, while custom intervals allow you to manually choose specific start and end dates to customize the analysis according to your specific requirements. This feature is particularly useful for evaluating the performance and impact of changes over time. For example, you can compare two time periods to measure the effect of a specific campaign or modification in the tour, such as a marketing effort, or the introduction of a new itinerary. It enables data-driven insights that help identify trends, optimize strategies, and improve overall performance based on real-world results.

Compare

The comparison feature enables you to analyze tour performance in two ways: by comparing relative periods or by comparing user segments. Relative time period comparisons allow you to evaluate tour performance against a previous time frame, while user segment comparisons let you select two distinct user groups to see how the tour performed within each segment.

Key KPIs are shown with percentage differences for a quick and clear overview. Below the KPIs, there’s a detailed graph that visually highlights the differences between periods or segments, making it easier to spot trends and changes.

For a deeper dive, the KPIs are also broken down at the card level, where you can see the specific metrics for each comparison. When comparing, the interface provides two tabs—one for each period or segment—so you can easily navigate and examine the results side by side.

Filters

Filters help narrow down analytics based on user segments or custom rules.

User Segment Filters: Analyze users grouped into predefined segments (e.g., new users or returning customers). These segments reflect users’ status when they interact with the tour. Learn more about creating segments by consulting this article.
Custom Rule Filters: Define custom rules to create a more tailored analysis. It lets you create a more focused analysis by defining specific conditions for user segments. These conditions can be based on inbound parameters or data provided by users, giving you flexibility in how you analyze user behavior. You can analyze nearly any group of users—whether by role, signup date, actions taken during a tour, or interactions with specific content. This makes it easy to see how different segments of users engage with content and provides more detailed insights into user behavior. You can use custom rules to track how specific segments interact with content, allowing for a more granular view of user actions. This lets you refine your analysis and focus on particular user groups or behaviors, providing more control and flexibility in your reporting.

Metrics

You can view metrics in two ways: Total and Unique. When the 'Show Unique' toggle is enabled, it counts only one interaction per user, regardless of how many times they engaged with the tour. This is useful for understanding individual user behavior. On the other hand, Total views calculate the overall number of times a tour was watched, skipped, or interacted with, including multiple interactions by the same user.

When analyzing tours, the following metrics are available:

Eligible Users: The total number of users eligible to see the tour based on targeting and segmentation criteria. This metric applies only to individual tours that have specific segmentation defined.
Total Views: The total number of times the tour was displayed to users. The list of viewers is exportable.
Unique Views: The number of individual users who viewed the tour (one view per user).
Finished: The percentage of users who completed the tour out of those who started it. The list of viewers is exportable.
Issues: The total number and percentage of issues that occurred during the tour, due to the wrong URL or problems with element selectors.
Skipped: The total number and percentage of users who quit the tour before finishing it. The list of viewers is exportable.
Average Time to Finish: How long it takes a user on average to complete the tour from start to finish. You can select any metric (up to two) to generate a graph in the Visualization section. Choose the granularity of data display, such as daily, weekly, or other intervals, to suit your analysis needs. However, these metrics must share the same unit of measurement (e.g., both in percentages)

You can select any metric to generate a graph in the Visualization section. Choose the granularity of data display, such as daily, weekly, or other intervals, to suit your analysis needs.

Cards Funnel

In the Cards Funnel section, you can view detailed analytics for each card in the tour:

Views: The number and percentage of users who viewed each card.
Drop-Off Rate: How many users exited the tour after viewing the card.
Average Time Per Step: The average time users spent on each card before proceeding.
Issues: The number of errors triggered by the card, such as navigating to the wrong URL or encountering a faulty element selector.
Click Action: The number of times users clicked buttons or custom buttons on each card.

The geometric symbols clearly represent the growth or decline of views, making data trends easy to understand at a glance.

Viewers and Drop-offs Export

You can see detailed information about users who interacted with your card. First, go to the section where you can view the list of users. The first image shows where to find this option.

In the second image, you’ll see the list of users who have viewed the card. This gives you a clear idea of who engaged with it.

The third image shows the list of users who dropped off, meaning they exited the tour after viewing this card. This helps you identify where users stopped engaging. You can also export both lists if you want to analyze them further or share them.

You can easily export the list to a Microsoft Excel document by clicking the Export button in the top right corner. The Excel file will include all the information displayed in the table.

Best Practices for Using Tour Analytics

Identify Tours with Low Finish or Skip Rates: Pay close attention to tours with low finish or high skip rates. Analyze the specific cards where most users are dropping off. By understanding where users are disengaging, you can make improvements to those parts of the tour. You can also view the affected users or export their data, and then target them with a survey to gain insights into potential issues or barriers they encountered.
Monitor Average Completion Time: Keep an eye on the average time taken to complete the tour. A high number of users spending an unusually long time on a specific step can indicate a potential problem—whether it’s unclear instructions, confusing content, or technical issues. Adjusting these steps to improve clarity or simplify the process can enhance user experience.
Compare Performance Before and After Changes: If you make adjustments to your tour, it’s important to compare performance before and after the change. Look at key KPIs like completion rates, engagement, or drop-off points to evaluate the impact of your changes. This comparison helps you measure whether the changes are improving the user experience or if further adjustments are needed.
Duplicate Tours for Targeting Specific Segments: You can also achieve targeted insights by duplicating the same tour and directing it to different user segments. By segmenting your audience, you can see how different user groups interact with the same tour, helping you identify patterns or issues that may only affect certain demographics or behaviors.
Use Filters to Segment Analytics: Utilize filters to segment your tour analytics based on various criteria, such as user role, location, device, or other custom attributes. Segmenting your data helps you identify trends or issues that may not be apparent when looking at the overall performance, allowing you to make more targeted improvements to the tour.
Track Engagement with Specific Cards: Don’t just look at overall tour performance—zoom in on individual cards within the tour. Understanding how users engage with specific content helps you identify which sections are resonating well and which may need refinement. For example, a card that gets skipped frequently may need a clearer call to action or more engaging content.
Review Conversion Metrics: Conversion metrics, like the number of users who complete the tour and move on to the next step (or take the desired action), are crucial. By tracking these metrics along with other KPIs, you can gain insights into how effective the tour is at driving user behavior. If your conversion rates are low, you may need to adjust the flow, timing, or content to better align with user expectations.

By following these best practices, you can continuously improve the user experience, track performance more effectively, and make data-driven decisions to optimize your onboarding tours