# User Path Analysis ## Introduction User Path Analysis is a **chart that visually represents users' navigation paths within your service**. It lets you **easily understand the routes users take within your service, along with the drop-off rate and conversion rate at each step**. User Path Analysis records all events a user generates within a single session and draws paths based on that data. Because it shows the points where users drop off or convert, the conversion and drop-off rates at each event, you can easily identify the overall behavioral flow and any problematic paths. At each step, you can perform detailed path analysis using event filters and user group settings, and the visual data output helps you analyze data intuitively and quickly. Because the User Path chart is useful for understanding how users navigate your service, you can use it to improve the service and enhance the user experience. ## Supported User Path Types ### 1. Step Selection **Step Selection** is an analysis type where you manually choose the analysis steps, allowing you to **see the subsequent journey** of users who passed through those events. For example, by clicking on event names such as login → search → product selection, you can visually see what percentage of users followed that flow. ### 2. Previous Flow Analysis **Previous Flow Analysis** lets you select the final event you want to investigate and add steps to trace **where users came from** before triggering that final event. You can easily view the flow even without selecting a specific event. For example, it intuitively shows the most frequently occurring events that users passed through before a "purchase complete" or "post created" event. ## Tutorial ### 1. Select Analysis Type and Path Click the User Path tab and select either **Step Selection** or **Previous Flow Analysis** in the upper right corner. Select the event for the screen where users first enter your service to analyze the flow. If there are multiple entry points such as a home screen or product detail screen, you can select the `$session_start` (session start) event that Hackle auto-collects to automatically see which events users most frequently triggered during service use and how much drop-off occurred. ![](/files/poKqa9aua0NI8Izytdqw) * Step Selection If you have a clear path you want to examine, use **Step Selection** to choose event names step by step and view the flow of users who triggered those events. By setting the path from the entry point to the final step (e.g., purchase complete), you can see the conversion rate and drop-off rate for the entire purchase journey at a glance. For example, to see the behavioral flow from "home entry" to "add to cart", you can set the sequence as `view_home > click_product > add_to_cart` as shown in the image below. ![Step Selection](/files/Sj7bx1TxaqnKSckxuqoW) * Previous Flow Analysis If you want to see the path users took before a specific event, select only the final event and add previous steps to trace the paths users went through before triggering that final event. You can filter the final event in more detail using property filters. This method is easy to use because it does not require you to think about event names or have specialized data knowledge to analyze which paths were taken. Since you can see the most frequently occurring events at each prior step, it is particularly useful for viewing user flows when selecting events that are important to your service, such as "purchase complete" or "review submitted". ![Previous Flow Analysis](/files/KtEbrcIL6YwvvNBz8BdM) ### 2. Set User Group for Analysis If you want to filter for a specific group of users who triggered the selected path, you can configure this under \[User Group]. In User Group, you can select from events, properties, A/B Test groups, feature flags, user groups, cohorts, and more. You can define the user group in detail through granular settings such as properties and date range. The example image below shows a configuration that includes only gold membership users who logged in within the last 30 days. ![User Group Settings](/files/qO0LEyWqsDsvC4yZp83j) ### 3. View and Interpret the Chart The chart display varies depending on the analysis method, but the query period and other common settings apply to both types. The chart results by analysis method are as follows. * **Step Selection** result chart You can view the path as shown in the image below, with the most frequently occurring events at each step displayed as a colorful flow. The gray shape at the top shows **the percentage (%) at which the event you selected appears at that step**. In the colorful flow below the gray shape, you can see **the names and percentages of the most frequently occurring events** at each step. **Other Events** represents the sum of all events excluding the top events. **Drop-off** represents the flow of users who left at that step without triggering any further events. ![Step Selection](/files/ks5T79sRJ67A5UPpfMyH) * **Previous Flow Analysis** result chart The Previous Flow Analysis result chart appears as shown in the image below. The previous flow is displayed visually starting from the last step, and you can add earlier steps using the **Add Step** button on the far left of the chart. You can see **the names and percentages of the most frequently occurring events** at each step, and all other events are aggregated as **Other Events**. ![](/files/JRf1wpnayyBLIBrAnD5g) * **Group by Property in User Path** You can apply "Group by Property" in the User Path chart to categorize events within the chart according to the configured property. When Group by is applied, property information is displayed below each event in the chart. ![](/files/hsxk4To1tbFIl01oY4mP) ### 5. Save User Path Cohort You can save the users corresponding to each node in User Path as a Static cohort. Based on the identifier selected under "User Identifier Criteria", you can see how many unique users belong to that cohort. You can then use the saved cohort with other features, such as sending push messages. ![](/files/MoFVyfCZPFsYDIQLbWtC) ### 6. Save and Share the Chart Click \[Save] on the chart you have reviewed to access it again at any time from the \[Data Report]. After saving, you can duplicate the chart and easily create a modified version. You can also share the chart in read-only mode as needed using \[Generate External Link]. ![Save](/files/Vq6TbB32ZmgPjpISxXC0) ## Usage Examples Using User Path, you can find data to answer the following questions: * What actions do users most frequently take after launching the app? * What did users do right after viewing the shopping cart? * Can I see only the flow of users who logged in or entered the app via a specific method? * What did users who dropped off from the key behavioral flow (product click > add to cart > place order) do instead? ## FAQ ### 1. What does "Other Events" mean in the data results? In **Previous Flow Analysis**, **Other Events** represents the sum of events at that step that do not fall within the top events displayed. For example, if 1,000 events occurred in step 3 and the top N events accounted for 800 of them, the remaining N events (200) are displayed as "Other Events". ### 2. When does "No Data" appear? **No Data** can appear in Previous Flow Analysis. This means there is no data for that step. For example, if you select `session_start` as the final step and query up to 4 previous steps, "No Data" appears when there are no events recorded at the 4th previous step. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.hackle.io/en/data/chart-types/user-path-analysis.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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