Try as we might to identify dimensions that would effectively create buckets of content from which we can analyze and get insights, there will be some oversights and gaps. Content groups are a great way to address these blind spots in Adobe Analytics.

This article will introduce you to what content groups are and why they are important, walk you through how to set them up, and go through the different ways you can use Analysis Workspace visualizations to analyze them. By the end, you would have some ideas on how to integrate content group data into your analysis.

What are Content Groups?

Content groups are simply custom buckets of your content, but while you may have dimensions by which you create content clusters, content groups tend to be more complicated and span more than one dimension. You cannot get data for content groups by simply making a freeform table and filtering one or two dimensions.

While you can modify data views in Adobe Customer Journey Analytics to create dimensions that will retroactively account for these complicated clusters, that feature is not available in Adobe Analytics. That is where content groups can help.

Here are other scenarios where you might need to create content groups:

• Your organization may track general sections of content, such as sports, but you want to know how much content you produce per team (including content about the athletes in each team). You may need to create a content group by tracking keywords in either page titles or tags (if applicable) to create a cluster per team.
• Content about health is very complicated. There are a lot of overlaps so creating an infrastructure that firmly puts different content into separate buckets may not be possible. Content groups are a great way for your organization to track topics that have these overlaps across the different sections of your platform.

In this article, I will be using the example of tracking organization-wide Paris Olympics coverage at my organization. Doing so is not just searching for a specific website section or filtering for keywords in a headline. Getting data for all content around the Olympics required filtering for multiple keywords in the headline, video name, audio name, and URL. When you are a news publisher with websites in about 45 languages, you must set up a filter for each keyword in every supported language. Once that segment is created, we can now measure almost all content related to the recent Olympics.

Why is it Important to Measure Content Groups?

Content groups are ideally created as a result of an organization's needs. Stakeholders may want to know about the return on the effort required to produce the content, so creating the bucket will provide cumulative data and/or actionable insights about the content.

In the case of tracking Olympic coverage, we want to understand the breadth of our coverage on our website and mobile apps, and we want to also understand if our audiences are consuming or engaging with the content. By creating a content group that spans not just article content but also video and audio, we can also determine which medium draws the most traffic and effectively engages its audience.

SETTING UP A CONTENT GROUP

Using Adobe Analytics segments is the key step to content groups. Building one is not just about dropping dimensions and filtering for specific elements. It requires some planning and forward-thinking to build a more solid content group infrastructure. It is not just searching for relevant keywords. You may want to consider creating containers based on keywords to easily use those containers for future use.

For example, when we created the Paris Olympics segment, we made a container for searching “Paris” in the key dimension and another for “Olympics.” This setup can let us reuse the different containers for other segments in the future, so in 2028, we can take the container for the Olympics and set up a container for all keywords associated with “Los Angeles.”

Custom Date Ranges for Event-based Content Groups

Similar to the Paris Olympics content group, your custom bucket of content may be based on a particular event. It will save you a lot of time and mental space by creating a custom date range associated with the event. You can then apply this custom date range to the panels in your Workspace project.

Metadata is everything when organizing components such as segments and date ranges. Incorporating keywords into the description and having some form of structure around tags are key to easily finding the different components, especially around a content group.

Sometimes, setting the date range to end at a future date may not be ideal, especially when dealing with time-based calculated metrics and visualizations. Rolling dates are a great way to track events by setting a fixed start date and having a rolling end date until the event has passed. You can always go back to edit the final date once the event is finished.

Leveraging Annotations with Content Groups

Annotations are a great tool for Adobe Analytics admins to note significant milestones related to content groups. You can use annotations to flag if an event-based content group can be attributed to anomalies in your data. When setting these annotations up, you will have to make sure that you do not apply the segment associated with the content group as the annotation can only be visible in Workspace projects with the filter applied. You could add a note in the description that they can use the specific segment associated with the content group to dive deeper into the data.

You can also use annotations to note any insights already discovered in previous analysis of the content group. By applying the content group segment to these annotations, colleagues and other stakeholders in Adobe Analytics can see what analysis has already been done on the content group to avoid duplication of work.

USING ANALYSIS WORKSPACE TO ANALYZE CONTENT GROUPS

Now that we’ve set up our content group(s), what insights can we discover about said segment in Adobe Analytics?

Using Key Metric Summary Visualization to Get Quick Insights

The key metric summary visualization serves as a great introduction to understanding how your content group reaches and engages your audience. If your Workspace project recipients gain nothing else from your report, they can get an idea of the outcomes that can be attributed to the content group.

To set up the key metric summary, you can apply the custom date range that is associated with your content group. Depending on the context of the data, you can also select which date range to compare the data against. Usually, the default, which is the same number of days prior to the selected date range, should suffice, but sometimes, you may need to consider comparing to other date ranges based on what your report is trying to communicate. Is the content your organization is publishing around a specific topic performing better (or worse) this year than the previous year? You may need to compare the data to same time period the previous year.

Applying the content group segment to key metric summary settings is optional, especially if you have applied that segment to the entire panel. However, there are cases where you may need to compare the key performance indicator to another content group or to all visits. Applying the segment here and creating a similar key summary metric with the other content group segment or no segment at all (for all visits) can quickly give you another layer of context to understand about your content group.

Using Freeform Tables to Break Down KPIs

Freeform tables are one of the most powerful tools in Adobe Analytics. When it comes to content groups, you can uncover a whole new world of insights just from freeform tables alone.

You can apply different metrics as columns and break down by dimensions to understand what the top content pages are and which ones generate the best retention, what traffic sources drive the most visits to your content group pages, and in our case, which newsrooms drew the most traffic and content consumption to their coverage.

You can also use freeform tables to create a time-parting heatmap to understand when your audiences come to your platforms to engage with your content group. While other visualizations can give you insights on what type of content or specific topics related to your content group resonates with your audience, this heatmap can help your organization optimize your audience engagement by identifying when your visitors are most likely to be on your platforms.

Using Line Visualizations for Performance over Time

Seeing the key performance indicator (KPI) trends over time is another great way to understand the reach and engagement of your content group.

A line visualization will allow you to track changes in the KPIs over time. To create this graph, you will need to create a freeform table with a time dimension (usually day) as data rows and the different KPIs for the content group as columns. You can apply the segment for the content group to the panel.

Putting that data in the context of a line visualization will help you understand when certain parts of your content group generate spikes in traffic, content consumption, or specific outcomes (and when they did not). If you are also tracking outcomes by media type, format, and other dimensions, you can also see how those might affect the surges in your data; this is a great starting point in diving deeper into the data to see what specific content resonated with your audience.

When you want to compare time-based trends for the content group against either all visits or other content groups, you can remove any segment applied to the panel and apply those segments directly to the freeform table. Adobe Analytics has an All Visits segment available that you can use.

This line visualization provides great context on how your content group contributes to the overall KPI. You can then question whether that contribution or return is worth the effort to develop that content.

Using Area Stacked for Dimension-Based Impact over Time

Another time-based visualization that is great to use with content groups is the area stacked. This visualization also requires building a freeform table with time dimensions as data rows. You can select one metric and break that metric down by the different dimensions you are trying to compare.

This table can then be transformed into an area stacked visualization, where you can easily see the contribution of each referrer type to overall visits. These trends can be used to assess which channels are more effective in generating visits (and converting them into outcomes such as content consumption). In the example below, we learned when search engines can start picking up our new content group so we can adjust for that delay for future event-based content groups. We also learned that as interest dies down, social media is still effective in generating traffic to our platforms.

Using Maps to Identify Geo-based Markets for Your Content Group

When you have a global or regional audience, it is best to assume that your content group(s) may not resonate with all of them. Understanding which geographic locations respond to your content the best can help you allocate resources accordingly to the groups responsible for those geolocations. Using the map visualization provides this insight.

You will need to apply the content group segment to the panel, and you can apply any relevant KPI to the visualization setting.

What data source to use as the dimension for your map is based on what devices your audiences mostly use or what platform you are analyzing. If your audience is mostly on mobile devices or if you are assessing your content group consumption in your organization’s mobile app, using the latitude/longitude data source might be more effective in capturing your audience’s geolocation. If your audience mostly uses desktop devices, if you are analyzing your website, or if you are not sure, apply geographic dimension as your data source.

Using Scatterplot Visualization to Measure Effectiveness

The scatterplot visualization in Analysis Workspace is one of the most underrated features in my opinion. It is a powerful tool to assess the effectiveness of certain dimension elements based on relevant KPIs.

For content groups, you can apply different dimensions to a scatterplot visualization and analyze their relationship to traffic and consumption of the content group. For example, you can see which platform is more effective at driving visits and converting them to article views, video plays, or audio plays.

Especially if you or your marketing team have limited resources, this is a great way to assess which marketing channel to focus your time and effort on.

This graph is not limited to marketing channels. You can compare audience markets (geolocations) to see which are more likely to consume your content group. Cross-referencing media types with visits and content views can give you insights into which medium appeals to your audience when it comes to a specific content group.

Using Component Dropdowns to Focus on Specific User Segments

If you frequently do deep dives into your content group data, make life easier for yourself by leveraging component dropdowns. Creating one (or more) for the filters you frequently use to analyze content groups can be such a time saver.

Filtering by traffic sources or by newsroom (language) is a frequent task for me when assessing content groups, so I add those dropdowns at the top of each panel in my Workspace project.

New features to these dropdowns have also made it easier to segment the data. Dynamic dropdowns update other dropdowns based on user selection to a previous one. For example, selecting a newsroom will update the list in the countries dropdown based on countries that visited that newsroom’s website. This feature makes a potentially long list (like countries) less overwhelming to Workspace end users AND avoids errors because a combination of country and newsroom does not yield any results.

Being able to select multiple items in a dropdown is another time saver. Previously, if you wanted to see data for more than one element in a dimension, you would have to create that segment. Now you can select the relevant elements in one dimension to only show data that applies to any of the elements selected.

THIS IS JUST THE BEGINNING..

There are a lot of possibilities when it comes to content groups in Adobe Analytics.  I hope that this information gets you thinking about the different content overlaps or scenarios in your organization that may require creating a content group and how you can start generating some actionable insights about that content group in Analysis Workspace.