Who is Losing Interest in Adobe Analytics? — 3 Key Indicators

Am I doing a good job in terms of user adoption of Adobe Analytics at my organization? Which co-workers are starting to lose interest? Which ones are emerging Power Users? Which data topics deserve more attention, i.e. enhanced reporting & tracking? This post reviews account logs, their issues and shows 3 key reports Admins should focus on.

The interface of Adobe Analytics gives you the most powerful ad-hoc analysis tool on the planet in Analysis Workspace. With it, you can answer most questions on the users of your website. However, when it comes to your organization’s users of Adobe Analytics, the interface does not offer much.

But Adobe at least gives you account usage logs. And if you are one of those admins who takes data democratization, governance and curation seriously, you may have discovered the Adobe Analytics API 2.0 already. For a while now, you can also pull account usage logs with that API (thanks to Julien Piccini also via Python). Those are basically the same logs you have always been able to download from the Admin section (restricted to 90 days per download).

Guides on how to leverage the Account Usage Stats

There are some guides out there on how to get started with these logs themselves. Frederik Werner has a very nice example with Power BI and Python. Another guide by Adobe shows how to import the logs back into Adobe Analytics so you can monitor your org’s usage of AA through the Analysis Workspace interface. While this is a powerful option, I wonder why Adobe does not integrate those stats into Adobe Analytics by default. Because the approach of that guide basically amounts to a large data engineering project for which many won’t have the necessary time, skills nor resources.

For example, you need to reformat the legacy format of the log file exports into the very different format expected by the Adobe Analytics Bulk Data Insertion API. You also need to set up a Report Suite with numerous components, Classification Rule Builder rules and more. And then of course the whole process needs to be automated.

The wacky format of the Adobe Analytics account usage logs

As I mentioned, you need some serious data engineering to get the logs into a normalized format. Not only because the format of the logs (no matter if retrieved via API or CSV export) has a “legacy” feel to it, it also comes with surprises. For example, the logs are in the language of the user being logged! So if Detlef Schrempf and his German team members prefer using Adobe Analytics in German (for whatever reason, as the translations are a universe of surprises of their own), your logs will look like this:

Surprise! Adobe Analytics logs in the language of the user whose actions are logged. Have fun normalizing all potential values for the over 20 different languages and even more event types.

3 Key Indicators to Monitor Adobe Analytics Usage

Once you have invested enough time to figure out how to get to terms with the logs, there is a lot you can do with them. I will share 3 things that I think are key for any Adobe Analytics Admin. You can build these examples yourself, my examples however are from my Adobe Analytics Component Manager for Google Sheets. The focus of the Component Manager is on managing the 1000’s of components (segments, calculated metrics etc.), e.g. you can enforce naming conventions, delete unused segments in bulk or manage multiple Virtual Report Suites’ curated components at once), but it recently also added a section on account usage stats.

1. Logins

If you do the absolute minimum with the logs, it is monitoring logins. It is probably the easiest indicator for user adoption. If people log in a lot, they use the tool. Note that total logins can be inflated in the past by Adobe’s changes to its login timeout and technical issues related to the login, so your primary metric should always be “Users with at least {1 or more} Logins”.

Login stats are the easiest argument to prove user adoption and avoid discussions like “do we still really need Adobe Analytics” or “who is using this tool anyway”.

2. Workspace Views

Another key indicator and a decent counter metric for login stats (logging in does not mean you actually use the Analytics data) is the number of users who view workspaces. As another surprise, for all accounts I have looked at, Adobe lost most of their logs in March & parts of April 2020 (logins are there, project-related Events aren’t). At last, an anomaly in early 2020 that is not related to COVID!

Workspace views should back up the login stats

What is powerful for me as an admin, especially when I do an Adobe Analytics account cleanup, is combining the account stats with metadata from the Workspace methods of the API.

Combining account logs data with metadata on Workspaces

See the example in the screenshot above. You can see …

  • how many Workspaces you have in total (2,095)
  • how many of those Workspaces had at least one view in the last 90 days (the 90 days are configurable) (647)
  • how many Workspace views you had in total (7,521)
  • how many Users viewed at least one Workspace (131)
  • individual view stats per Workspace

You can thus easily identify the workspaces that are of interest to most users. This helps you focus your admin work on topics that are of interest to your users. The most popular workspaces should also be candidates for regular maintenance or enhancements.

On the other hand, those workspaces with no views and no actively scheduled email PDFs are clearly candidates for deletion, something you should do regularly to keep things neat and fast (and you can delete thousands of Workspaces with 2 clicks with the Component Manager btw).

3. Churning and Burning Users

At my clients, I usually try to establish a system of decentralized “Power Users”. Those Power Users get special training, priority support and regular checkins with the core Analytics team, and their role is to evangelize their teams with what they need in terms of data.

As everywhere, some of these Power Users turn out to be too busy with other things or just not the right fit for this kind of work. Others turn out to be absolute Jesuses. Even others you never had on the radar turn out to be truly heavy users → potential Power Users!

All this can be monitored by regularly checking out logins per user. Make sure you have some visualization that shows development instead of just fixed numbers, e.g. a column for logins per rolling quarter, like in the example below. This way you can see who is trending up and who is on the verge of churning. Is that formerly very active user marked in red in the screenshot below simply someone who has changed responsibilities, or is our Adobe Analytics setup not doing a good job for her?

User Activity monitoring is the basis of data-driven data democratization

Step out of the cave

Until now, you were the one who kept teaching others how to take more data-informed decisions, while your own admin job was like working in a cave with a tea light. By leveraging account usage logs, you can do your own work in a much more data-informed way than ever before. So step out of the cave and focus on breeding the right users and curating the hottest topics. Your users will love you, and your AA setup will get only better!

P.S.: And if you are interested in a free test account for the Component Manager for Google Sheets, contact me.

Also check out part II: Want to dig into Adobe Analytics Account usage logs yourself? A checklist of “surprises & should-knows”

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Digital Analytics Expert. Owner of dim28.ch. Creator of the Adobe Analytics Component Manager for Google Sheets: https://bit.ly/component-manager

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