• Jane@JaneAnsara.com

Category ArchiveData

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


Omnibug Logs

Good News, Omnibug (a top analytics debugger) was recently updated and Log Files have returned.

To continue using this feature, I’ve updated my Omnibug Template. This is an Excel file I created a very long time ago to help with testing analytics by formatting the debug log data. Help your self to a copy and let me know how you have improved it.

Omnibug Template on GitHub

How to Build Reports in Excel

Spreadsheets are great for playing with data. Their free form structure is ideal for designing custom reports. And because Excel is so widely adopted, it is often the tool of choice for creating and sharing information. But with such a range of user experience, we often get spreadsheets that are not well structured, cannot scale or can easily become unmanageable. So I’d love to share some of my best practices for building reports in Excel.

When we want to use Excel to summarize data, our best approach is to treat it as a free form database. We can use the worksheets to replicate database features; Data Tables, Queries and Reports. Here’s how:

Data Tables are defined as a range of cells that contain related data. The first row of data is reserved for Field Headers and are needed to describe the data in each column. Each column is a “field” (or category, ex: Name, Date, Price etc.) and each row is a “record” (or list item).

When data is formatted in a proper table, Excel recognizes the structure and offers a selection of tools to analyze your data. This tells us that Excel is designed to work with structured data tables.


  • Always place your header in row 1, plot your data directly below leaving no blank rows between records. A record must have data in at least 1 of the columns for Excel to “see” that the records below also belong to the table
  • Use Freeze Panes in the View tool bar to keep the headers in view when scrolling down through rows of data

Excel PivotTables are fantastic for reading and summarizing the data in your Table. Because they form a connection with your data, any changes you make, (for example: add more records) can automatically flow through to the PivotTable report.

Let’s create a PivotTable. Start by selecting all the columns on your “Data” worksheet that contain values. Then select PivotTable in your Insert tool bar. Choose to place your PivotTable on a New Worksheet.


  • Because we built our data with header values in row1, we can select the entire column of data rather than just a few cells. This way our data table can grow and still be inside the range of the PivotTable. If you choose to only select the cells that currently contain data, you may not remember to expand the range when new values are added

You can then shape your PivotTables to create the views you need for reporting. If you need several views, you can create more than one PivotTable on the Query worksheet

If your PivotTable looks good enough for you, you’re all set, you can use that for your report. If you want to combine data from different sources, run calculations and enjoy freedom to format as you like, then you will need a Report. Create your ideal layout and use the GetPivotData formula to pull data into your report. Create calculated metrics and other automations to make updating easy.

This is an example of a simple Excel file that follows the best practices of; Data, Query and Report. You are welcome to download it here. Take a look at the formulas on the “Report” worksheet, even the date can be automated. I’m hoping this helps and would love to hear from you if you need more. There is so much we can do with a solid foundation for reporting. Please feel free to reach out to me directly if you have any open needs for reporting and analytics.


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