Quantifying Content Velocity in Adobe Analytics – Part 2
Last week, I shared how to quantify content velocity in Adobe Analytics. This involved classifying content with the date it was published and looking at subsequent days to see how fast it is viewed. As part of this exercise, the date published was added via the SAINT classification and dates were grouped by Year and Month & Year. At the same time, it is normal to capture the current Date in an eVar (as I described in this old blog post). This Date eVar can also be classified into Year and Year & Month. The classification file might look like this:
Once you have the Month-Year for both Blog Post Launches and Views, you can use the new cross-tab functionality of Analysis Workspace to do some analysis. To do this, you can create a freeform table and add your main content metric (Blog Post Views in my case) and break it down by the Launch Month-Year:
In this case, I am limiting data to 2018 and showing the percentages only. Next, you can add the Blog Post View Month-Year as cross-tab items by dragging over this dimension from the left navigation:
This will insert five Blog Post View Month-Year values across the top like this:
From here, you can add the missing three months, order them in chronological order and then change column settings like this:
Next, you can change the column percentages so they go by row instead of column, but clicking on the row settings gear icon like this:
After all of this, you will have a cross-tab table that looks like this:
Now you have a cross-tab table that allows you to see how blog posts launched in each month are viewed in subsequent months. In this case, you can see that from January to August, for example, blog posts launched in February had 59% of their views take place in February and the remaining 40% over the next few months.
Of course, the closer you are to the month content was posted, the higher the view percentage will be for the current month and the months that follow. This is due to the fact that over time, more visitors will end up viewing older content. You can see this above by the fact that 100% of content launched in August was viewed in August (duh!). But in September, August will look more like July in the table above when September will steal a percentage of content that was launched in August.
This type of analysis can be used to see how sticky your content is in a way that is similar to the Cohort Analysis visualization. For example, four months after content was launched in March, its view % was 3.5%, whereas, four months after content was released in April, its view % was 5.3%. There are many ways that you can dissect this data and, of course, since this is Analysis Workspace, if you ever want to do a deeper dive on one of the cross-tab table elements, you can simply right-click and build an additional visualization. For example, if I want to see the trend of February content, I can simply right-click on the 59.4% value and add an area visualization like this:
This would produce an additional Analysis Workspace visualization like this:
For a bonus tip related to this concept, click here.