Frequency

Last update: 2024-09-30
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Learn how frequency in Customer Journey Analytics lets you analyze user engagement with events in your product.

 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 frequency trends analysis in Adobe Product Analytics. The frequency trends analysis allows me to analyze how engaged users are with events in my product. My goal is to find highly engaged users to see what makes them frequently interact with my product so that I can encourage less engaged users to adapt similar behavior. The guided analysis user interface lets me formulate a question in the query rail on the left and then answers with the written insight chart and table on the right. And that’s the format. I’ll select the key events including view home screen, add to my list and media start to measure user engagement. Now there are several ways you can count the events. I want to see the data as percentages of users. The percentage based metrics use the denominator of users who did the selected events. I can also add segments to my query. For example, I’m interested in comparing two segments of my users, free and paid subscribers. I want to see if there are behavioral differences between these two groups. Now after making these selections in the query rail, I could sit back and let the frequency trends analysis do the work. Let’s review the chart first. Across the top are the events I selected in the query rail and along the Y axis is a number representing the number of times a user has performed the event for the time period selected. Spanning the X axis is the scale for percentage of users. And then at the bottom is the legend for the bar colors used for the two segments. Further below is the table with data for both segments. They’re grouped in rows and the percentages are laid out in aggregate for each event number. Back to the bar chart, it defaults to the horizontal bar format. Now I can change this to stack bar to see if this visualization enhances the way I want to analyze the data. Now that I’ve made this update, I like this chart format for what I’m doing. It’s easy for me to compare the relative percentage of users who performed the event based on the number of times engaging with the event. For example, it looks like, visually anyway, that paid subscribers are more engaged than free subscribers for the first and third events. I could do more customizations. For example, I can update the buckets used for this event engagement. I’m going to update the size to two. And once I do this, the chart is updated to condense engagement numbers in multiples of two. Now in doing this, I see the result remains that paid subscribers are more engaged than free subscribers as engagement intervals increase for the first and third events. Next I’ll layer in time comparison. There’s quite a few options available, but I’ll select previous period to compare the last 30 days to the previous 30 days. In the bar chart, the comparison data is added above the current data and it uses a dashed line format. It’s interesting to see where engagement has changed. The comparison data is also reflected in the table below the chart. The rows are grouped by previous period and last 30 full days for each event and segment. I can also toggle the chart data to show trended data. This lets you see how your users are grouped over the time period selected. The paid subscriber segment is very engaged with watching videos. This is a high value event because it generates ad revenue. If I click on the six times or greater bar for the current period, I have the option to save this segment and then I could use it in other analyses to dive into more details about what makes this group active. On the flip side, I can also save a segment of low engaged free subscribers and then target them with in-product messaging to encourage upgrading to a paid plan. If I want to see these key events trended over time, I can quickly change my view to usage. My current query is used in this new view and I don’t have to recreate it. And that’s a nice time-saving feature. Check out the usage trend analysis video for more details. I hope this video comes in handy the next time you want to find high and low engaged user groups in your product.

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