To everyone who found time to join my session at Adobe Summit 2014 on Adobe Analytics tips and tricks: Thank you! I had a great time preparing and presenting, and hopefully you found the content engaging and instructive. For those who did not get a chance to grab the handout after the session, it is now available for you to download. (I apologize for the file size, which is about 65 MB, the cost of high resolution. I tried to get it down as low as I could.)

In the remainder of this post, I am going to share the nine tips that made up the bulk of my session, similar to what is provided in the handout but with a bit more detail. Please share this post, in addition to the handout, with any of your colleagues who didn’t get a chance to attend my session.

Tip #10: Classification Rule Builder

One of the topics that generated more interested than expected as part of this tip was regular expressions. The best way to build flexible rules to add metadata to your tracking codes, product SKUs, article IDs, etc. is using regex. If you’re like me, you’ve tried a few times to master regular expressions, and failed each time. I still haven’t really mastered them, but the Rule Builder makes it much easier to piece together the matches you need by offering a great set of examples which you can copy and use without leaving the tool.

  • Key Takeaway: Classifications make it easier for you and your colleagues to interpret and segment your data by adding metadata; Rule Builder makes it easy to add the metadata automatically, as data is collected. Use Excel for spreadsheets, not as much for building classification files!
  • Best Practice: Use sub-classifications as additional lookups to populate meta data another level deep. For each classification that has sub-classifications, upload a smaller Excel file, which serves as VLOOKUP file for the classification engine.
  • Documentation Keyword: classification rule builder
  • Additional content: Brian Au (blog post) and Matt Freestone (part 1, part 2, part 3, and part 4)

Tip #9: Real-Time Reporting

Session attendees were treated to a photo of Wild Bill, the Utah State University basketball superfan, donning a hula outfit in an attempt to distract an opposing free throw shooter. Yes, real-time analytics can be a distraction if used inappropriately. However, there absolutely are valid use cases for real-time data that apply across multiple verticals and business models. What I shared in the session was just one of those that comes up again and again as I talk to customers all over the world: I need to know immediately when there is a problem on my site.

  • Key Takeaway: Real-time data can enhance your digital marketing operations by providing insight into what content or products are resonating right now, but also what isn’t working at all—what might be broken or offline.
  • Best Practice: To start, set up error reporting in real-time, and make this data available to teams that can take action on errors if/when they occur on your site. Adobe Dynamic Tag Management or another TMS can make it easy to capture this and other relevant data in a prop for use in the real-time report.
  • Documentation Keyword: real-time report
  • Additional content: Me (blog post) and J.D. Nyland (blog post 1, blog post 2)

Tip #8: Tableau Integration

It was nice to see our friends at Tableau do a blog post (see link below) on this integration during Summit. We know that many of you have been exporting data from Adobe Analytics and importing it into Tableau, but this has always been painful because Tableau doesn’t natively understand which fields coming out of Adobe Analytics are dimensions, which are metrics, and which are time ranges. The result is that you’ve had to do a lot of manual work to prep your data for import. This integration aims to solve that problem.

  • Key Takeaway: Tableau is a popular dashboarding and data visualization tool, and Adobe Analytics is a key component of your marketing data. It is very easy to create Tableau exports out of Adobe Analytics.
  • Best Practice: Use Data Warehouse to create Tableau exports for consumption by your Tableau instance. Click “Advanced Delivery Options” in Data Warehouse to select Tableau Export. Then set up Tableau Server for automated import using cmdtab.
  • Documentation Keyword: tableau
  • Additional content: Tableau Software (blog post).

Tip #7: Simplified Left Nav

This one is all about helping your colleagues self-serve with data more effectively. With a single click, you can turn the existing left-hand navigation menu—which is usually 10-15 items long, depending on which features you have enabled—into a streamlined six-item navigation; additionally, the groupings of reports are based on the way that users typically ask questions of data, rather than the traditional grouping by the underlying variable type.

  • Key Takeaway: Adobe Analytics gives you a ton of critical data, which is great—but which can make navigating the reports confusing for less-experienced users. The simplified menus allow your users to find the data they need far more quickly and easily.
  • Best Practice: Select the simplified menu, then make edits to customize the simplified, streamlined menu to your needs. Go to Edit Settings > General > Customize Menus, then click “Restore Simplified.” Once the setting has been applied, you can make additional edits customized to your business needs.
  • Documentation Keyword: simplified menu

Tip #6: Admin Console improvements

Participation, Pathing, Merchandising eVars, geoSegmentation, Event Serialization, Hiding report suites from the drop-down menu, and more. This one is a win for everybody. It frees you up to spend less time on the phone with ClientCare or your account team. It frees ClientCare and your account team up to provide higher-value interactions.

  • Key Takeaway: Adobe Analytics offers unparalleled flexibility and power around your on-site/in-app implementation. With recent changes, you can now control a dozen settings that were previously only available by calling ClientCare.
  • Best Practice: Expand your implementation! The Traffic Variables, Conversion Variables, and Success Events pages in the Admin Console allow you to turn on participation, pathing, serialization, merchandising, and more. You can also hide report suites from displaying to your users in the drop-down menu.
  • Documentation Keyword: admin console enable features
  • Additional content: Me (blog post)

Tip #5: Anomaly Detection

You asked us to tell you not just when something relevant changes, but when you should care about it. We took a big step in that direction with Anomaly Detection. It’s worth calling out for emphasis that this is also available via the API. A second best practice that I could have shared is to create a simple script based on Anomaly Detection that runs the report each morning and sends an email to interested parties with its observations.

  • Key Takeaway: No human can sense everything that is worth investigating, but Adobe Analytics can. Find the “unknown unknowns” by calling out statistically significant changes in trend.
  • Best Practice: Build custom “filtered” metrics to focus in on anomalies for specific pages, sections, products, campaigns, or other dimensions for any metric that you choose. Click “Add Filtered Metric” when configuring Anomaly Detection.
  • Documentation Keyword: anomaly detection
  • Additional content: John Bates (blog post)

Tip #4: Adobe Analytics API

In my session at Summit, I shared the Marketing Technology Landscape Supergraphic compiled by Scott Brinker over at It includes 947 different technologies across 6 major categories and 42 sub-categories. The point is that no matter how many Adobe Marketing Cloud solutions you’re using, you are probably being asked to facilitate additional integrations with other tools. The Adobe Analytics APIs received 19 major enhancements over the past 12 months which make it much easier for you to do this, including the ability to bring Adobe Analytics data into R for customized statistical analysis.

  • Key Takeaway: As you are asked to integrate digital analytics data with other sources, APIs become key; they allow you to retrieve data programmatically so that you can operate or take action on them in your own applications or other systems.
  • Best Practice: Use “RSiteCatalyst” package with the R language to make easy-to-build Adobe Analytics API calls and perform your own statistical/predictive analysis against your data. Start with basics—standard deviation, scatter plots, histograms, and build out from there. You can get RSiteCatalyst, documentation, and the packages that it requires at
  • API Documentation:
  • RSiteCatalyst Documentation:
  • Additional Content: Randy Zwitch (blog post)

Tip #3: Compare Segments

Comparison is everything. It provides the context that you need in order to interpret your data correctly and arrive at the right conclusion. Reminder that you can always go to Ad Hoc Analysis to compare multiple segments and date ranges in a single view!

  • Key Takeaway: Comparison is critical in analysis, and Adobe Analytics has expanded its comparison capabilities to include segment comparison in the web UI.
  • Best Practice: Do basic comparison of segments to determine which products, articles, videos, etc. are most relevant to different groups of users, then run targeted campaigns or offers to improve conversion for various segments.
  • Documentation: compare analytics segments (this gives a specific example of segment comparison, but you’ll get the idea)
  • Additional content: Me (blog post)

Tip #2: Mobile App Lifecycle Metrics

Having designed a few mobile implementations for product teams and mobile agencies in my day, I know how painful this can be. That is why lifecycle metrics are so exciting: with literally a single line of code dropped into your mobile app code base, get everything your app teams need in order to get started on reporting and analysis. This also integrates with Adobe Mobile Services to allow you to do not just analytics, but also targeting and other marketing activities from a single tool.

  • Key Takeaway: Adobe Analytics has made it possible to implement SDKs in your mobile apps and get rich, powerful data with minimal effort from your developers. One line of code and the inclusion of the SDK will provide “lifecycle metrics” (and dimensions) including installs, launches, crashes, device name, app ID, days since last upgrade, and more.
  • Best Practice: Use the provided Mobile Overview report as baseline reporting for your mobile teams. Mobile cohort analysis should be your mobile product group’s best friend—and it is readily available with mobile lifecycle metrics.
  • Documentation Keyword: analytics SDK 4
  • Additional content: Ray Pun (blog post) and me (blog post)

What Happened to Tip #1?

Tip #1 was something coming soon to Adobe Analytics. I won’t rehash all of it here, but it’s something we have heard as a request from many of you, which we will help your colleagues use Adobe Analytics more confidently and without needing as much manual guidance from your analytics team.

There you have it! I’ll be posting additional tips and tricks as time allows over the coming weeks. As always, I’d love to hear from you if you have tried any of these tips, whether you’ve got questions, complaints, compliments, or anything else to say!


From someone who wasn't able to attend Summit this year, thank you for condensing the takeaways from the week long summit into an easily digestible post. Quick question: Tip #4 and Tip #8 - Isn't there an API for Tableau integration? Looking forward to tip #1 :)