[Huawei Developers] Jack Wu Shenzhen:
Path analysis can be enormously useful, since it displays the status of app usage and provides data support for continuous app iterations. Generally speaking, user preferences are analyzed based on various event nodes within a session. However, for an app with multiple channels through which it obtains business, due to a large number of event tracking configurations, behavior paths can be so complex that the Sankey diagram is unable to clearly display differences in user behavior.

To resolve this challenge, Analytics Kit 6.3.0 provides a page path analysis model, which takes each page as a conversion node. Focusing on abnormal pages with high churn rates, path analysis can help deepen your understanding of user requirements for page redirection to enhance user experience.

1. Streamlining the User Behavior Path

Path analysis used to be complex. Take a shopping app for instance. To analyze the key conversion path of the purchase, you would need to define the following events: View products, Add to shopping cart, Start check-out, and Complete payment. This is because the user may have performed a series of operations, including searching for products, viewing product details, and viewing comments, prior to making the purchase. To learn about the complete behavior paths and optimize user experience, you would have to track different events related to different pages.

* Session path analysis, for reference only

Therefore, the workload of developers could be overwhelming. Meanwhile, for product and operations teams, the path analysis Sankey diagram formed by a large number of events proved to be so complex that they can hardly quickly locate nodes where users are more likely to churn. Fortunately, this issue can be solved by page path analysis, since it can help you quickly determine the causes of user churn from a general overview down to the details, thanks to a simplified Sankey diagram that clearly shows the page redirections.

Different from session path analysis that takes events as nodes, page path analysis takes pages as the minimum nodes. It breaks down complex behavior paths into pages, helping you first find pages with abnormal churn rates, and then locate specific events responsible for such churn rates.

* Page path analysis, for reference only

For example, if the churn rate from the payment page to the payment completion page is high, and the payment completion rate is below expectations, consider the following questions: If users give up making payments, which pages will they go to instead? Are there any similarities among these users? If a high proportion of users return to the product details page from the payment page, then the design of the payment page may be suboptimal. Therefore, aspects like coupon usage and order details display would need to be optimized during app iterations.

This method of locating churn nodes from pages to events makes it easy for you to find the conversion nodes responsible for the high churn rates.

2. Focusing on Frequent Page Redirections for App Iterations

The desire for a better user experience is the driving force behind app iterations. With the page path analysis model, you can obtain in-depth insight into user requirements, as indicated by various behavior paths, to explore new possibilities for app iterations.

For example, in a community app, users usually browse the home page before browsing the search result page and details page. Using page path analysis, if you find that the rate of users returning to the search page from the details page is high, it may indicate that they have questions or encounter contents of interest when browsing the details page. Therefore, when making iteration plans, you can consider adding hyperlinks to related keywords to provide additional information users are interested in.

In addition, if users frequently switch between two pages, you may need to consider the connection between the two pages. You can combine them into a single page, which can be an effective way to make browsing your app more efficient.

In general, the page path analysis model can help you explore user requirements for page redirections and create iterations that maximize your app's value.

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