• Jane@JaneAnsara.com

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Why Your Adobe Analytics Reports are Hard to Understand

Over the years I have met many marketers that have Adobe Analytics implemented but are unhappy with their reporting. It's such a shame to see so much time and effort invested in such a valuable resource that does generate return. A common mistake that is found over and over again is a poorly structured data model. Adobe offers a mass of customization that can easily be overwhelming. So let's reel it in.

To get started, it makes sense to understand the difference between Custom Traffic and Custom Conversion variables. You can read about these here.

There are 2 types of variables that can be used to measure a user action; Data and Event (metric). Which means there are 2 different ways we can track a key engagement;

1. We can create Events for each action we want to measure and name them accordingly

OR 2. We can create key Event metrics and use a Data field to capture the event description

Because Data variables can capture unlimited values and an Event metric can only count 1 event, it makes sense to capture the value that can change; “text” in a Data variable and use custom Event metrics to measure key engagements, such as; View, Click, Download etc. 

Another common mistake is to populate multiple Data variables with the same piece of data

Scattering data this way limits reporting capabilities. If we want to see all the activity for Login ID #123, we need to run 3 different reports!

This is why we always want our Data values stored in one place. Here is an example of a single report that uses key Events to expand the reporting capabilities of our data

Following these best practices for data modelling will ensure:

Less complexity

Consistent results

Data reliability

And most of all SCALABILITY (my word)

It's hard to see the future and predict our analytics needs. Websites grow and content changes. If we build a reliable and scalable solution design, we don't have to predict what may come. We'll be ready for it.

For more tips on solution designing for Adobe Analytics visit 3-top-data-strategies-for-adobe-analytics

I welcome your comments and feedback


Reliable Data Is Within Reach: 6 Steps to Data Confidence

Data may not be the most exciting part of marketing, but it is the foundation for gaining the marketing insights you need to continuously optimize your site.

An analytics strategy is only as good as its data: you need to be sure the data being collected is "clean" before gathering insights from it. If the data being analyzed is defective to begin with, no matter how good the analysts are, the insights derived and given to decision makers will likely be misleading or outright wrong.

6 Steps to Data Dependability

To prevent these kinds of situations, we outlined the following specific steps to ensure you are working with dependable and reliable analytics.

1. Implement a Tag Manager

If you are not using a tag manager, now is the time to start. It reduces your dependence on IT, allows you to quickly measure the impact of new features and fix bugs you uncover in data collection methods.

Tag managers can also be used to build reliable connections to all your analytics tools. Adobe’s Dynamic Tag Manager and Google Tag Manager are two popular, easy to use tag managers. And best of all? They are absolutely free.

2. Test your analytics implementation across all browsers and devices

Testing should be a critical part of your implementation strategy. Since data collection is JavaScript-based, its behavior can vary across different browsers and devices.

Take a tip from your IT teams and test analytics implementations across platforms to ensure data is being collected reliably. Write test cases for analytics, just like a QA tester does for code.

3. Implement data governance processes

Unless you are the analyst who just finished testing all the code, how do you know what the data means? Far too many analytics implementations do not come with documentation, which leaves marketers and other analysts with very little to rely on when it comes to interpreting reports. Clear documentation can make all the difference between deriving reliable insights and making embarrassing claims.

First and foremost, keep solution design documentation current and make it available to all analytics users. Then share starter dashboards and reports that demonstrate best practices for custom reports, offer analytics training, and establish communication channels to ensure reporting consistency across all departments.

4. Data pre-processing

Reporting tools give you the ability to pre-process and filter click stream data before it makes it into the reporting suite. While mechanisms like these are powerful, they need to be used carefully.

Fiddling with incoming data before it is analyzed can lead to confusion and is often difficult to debug. To minimize complications, configure your data process to touch the data as little as possible during collection.

5. Reporting

Is testing the data the first thing you do when reports show anomalies? Are you sure that the drop in conversions was caused by actual events or is it a tracking failure? Do you have sufficient diagnostics to prove the data is reliable? Since analytics platforms are so customizable, build diagnostics into your analytics process from the beginning.

6. Segmentation

Today, analytics reporting software can produce much more than simple reports. Now we can create advanced segmentation that significantly expands the capabilities of reporting. We can also nest statements and conditions to derive the specific results.

But how do we know when a segmented report is reliable? Start with simple segments that can be tested on related reports. For example, a segment for US traffic on a country report should only return US traffic. Knowing that the filter conditions are returning the expected results are a critical first step to advanced segmentation.

Reliable Data Is Within Reach

Implementing an analytics reporting system that you can trust to report reliable data is within reach. With these simple steps, your company’s analytics team will be better positioned to deliver meaningful insights that transform your company’s path the greater success.

Originally Posted on CMS Wire, April 2017