Retention

Last update: 2024-09-30
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Explore retention in Customer Journey Analytics. Learn how to use start and return events to measure user engagement and retention effectively.

Learn how to use start and return events to measure user engagement and retention effectively. The tutorial also covers how to adjust retention settings for different business models, whether you’re running a travel website, e-commerce platform, or productivity tool. Gain practical tips on using retention data to optimize product market fit and drive sustained user engagement.

 Transcript

Hi, this is Michelle Hajala and I’m a technical marketing engineer for the product enablement team. In this video, I’ll show you the Retention Rates view in Adobe Product Analytics. The Retention Rates view allows me to analyze the rate at which users continue to engage with my product over time, which can help me understand my product market fit. The guided analysis user interface lets me formulate a question in the query rail on the left and then answers with insights, a chart, and a table on the right. The first thing I’ll do is select the start and return events I want to use in my analysis. The start event is used to qualify users for inclusion in the analysis. Any event will show me overall retention of my user base. In this case, I’ll select media starts so I can measure the retention of my engaged user base. The return event is what a user must engage with to be counted as a returning user in the analysis. By default, the start and return events are linked, meaning a user must do the start event once to be included and then again to be counted as a returning user. You can unlink the start and return events if you want the returning action to be different from the inclusion event or if you want to compare return events. Let’s do a comparison of add to favorites, searches, and add to my list to see which capability drives stronger user retention over time. On the right, an answer to my retention question is provided in the form of a written insight, chart, and table. The bar chart is divided into bars showing retention and churn. The x-axis represents the time since the user’s initial start event and the y-axis shows percentage of users. From this view, I can very quickly see that favorites in search drive a 2x 14-day retention compared to my list. Knowing this, I might apply more investment to improving the discoverability of those features so that more users engage with them and retain better over time. Below the chart, a table provides aggregated data with the option to show individual cohorts which are a group of people who did the starting event on the same date. In the query, I can also compare retention across key groups of users. For example, free versus paid subscribers. Now what if you have a more customized definition of retention for your business? 30-day or 12-week, bound or unbounded. You can configure the retention model to best meet your business needs under counted as. Returning settings affect how return users are counted. On or after will count a user in retention if they return on or after the specified duration. For example, on day seven or any time after day seven. In contrast, on exactly counts the user in retention if they return on the specific duration exactly. Each and duration settings will affect the duration column shown in the chart and table. You can show up to four bars of any duration. With the on exactly setting, you can also create bracketed retention if you want users to be able to engage across a range of time. The selections you make should align with your business’s expected return interval. If you operate a travel website, you may only expect a user to visit quarterly, while an e-commerce platform may be looking at monthly retention and a productivity tool may be looking at weekly retention. Notice how the chart and table have updated to reflect my retention settings. Available duration options will be dependent on the calendar. Retention analysis always gives all users the chance to be included in all duration buckets. As a result, the analysis range will be shorter than the selected calendar range, the closer that range is to today. I can also look at the chart view as aligned to more clearly see my retention curves. This is where product market fit comes in. Once I have identified my organization’s appropriate return interval, product market fit can be found when the retention curve flattens. If it continues to trend downward over time, that’s an indication that your product team still has some work to do to find your fit and drive stronger retention among your users. As with all guided analyses, the insights gained may lead you to want to take action. From any retention or churn bar, you can click to create segments of users to analyze further or target with a re-engagement campaign. This has been an overview of retention rates guided analysis. I hope this view makes it easy to understand how users continue to engage with your product over time and help you find your product market fit.

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