Timeline

Last update: 2024-09-30
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Learn about timeline in Customer Journey Analytics, which helps you find experience patterns, tell better user stories, and validate implementation.

 Transcript

In this video, I’ll show you the User Stream Timeline Analysis in Adobe Product Analytics. The Timeline User Stream Analysis allows me to analyze sessions for a random set of users based on property values and segments selected. Viewing user streams in this way can help you find experience patterns, tell better user stories, and validate implementation. Let’s say I’m a product manager for our ecommerce checkout process. My goal is to examine if there are barriers to completing orders for users who add products to their basket. If you’ve used Fallout, Funnel, or Flow Reports to analyze the key user journeys in your product, you can use the Timeline User Stream Analysis to get additional insights. The guided analysis user interface lets me formulate a question in the query rail on the left, and then answers with the written insight, a user stream, and a list of randomized users that match the query filters, and that’s the format. I’ll select the property that I want to stream and then filter it to the ecommerce basket. In this case, it’s page name that contains basket in the value. Instantly, I get an answer in the form of an insight, user stream, and randomized users. The property value I filtered to in the query rail is highlighted in the user stream, making it easy to identify. I’m not done with my query, though. Segments allow me to filter the users by a saved segment or by a property filter. Since the segment I need doesn’t exist, I’ll create a filter where the page name does not contain receipt. You can also filter to a specific person ID in this section should you want to isolate your own behavior. This can be helpful in cases where you are a data admin and you want to validate implementation. Now that my query is completed, let’s review the insight at the top. 503 users match the criteria where page name does not contain receipt and had at least one session over the last 30 full days with page name containing basket. To glean insights quicker, I have options for how I want to view the stream. I can show all values, highlight those that match the query filters, or view only filtered values to remove the noise. Below the insight, I’ll click on the info icon for this person to get more details about this user’s history and our product in the last 30 days. The first property value in the session starts at the bottom, and the last property value in the session appears at the top. There is a timestamp to the left for each property value, again page name in my example. To the right, it shows the duration of each event. If people are spending a lot of time on specific pages where this behavior isn’t warranted, that might suggest a problem. The right rail contains up to 15 random people you can select from. This is helpful for gaining insights across different users experiencing product friction. You may even find some interesting patterns for users that use the product multiple times during the time period. Were they trying to purchase the items in their basket in another session but couldn’t? Were their product outages or error pages that haven’t been implemented properly for data collection? These are just some examples of insights you can uncover. I hope this video comes in handy the next time you need to analyze potential causes of user friction or errors in your product.

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