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Adobe Summit EMEA 2018 Recap
July 10, 2018      Joshua Barratt      Adobe Analytics


Every year, thousands of marketing and analytics professionals descend upon the ExCel in London for one of the most prominent, anticipated events of the year; the 2018 EMEA Adobe Summit.

Alongside the two day event, I was also lucky enough to attend the prestigious Partner Day. Partner Day attendees have the opportunity to meet Adobe partners and gain an insight into how Adobe is supporting the developer community.

The behind the scenes development of Adobe’s Cloud Platform was one of the primary focuses of this years’ event, making this year’s summit of particular interest for me. Just a few months ago, I joined a fantastic team of experienced and passionate analysts and marketing professionals at an American-based company named Blast Analytics and Marketing, which is an Adobe Specialised partner.

This was my second Adobe Summit. The first I attended was in 2016, and although only two years ago, I have since ventured down a new career path as an Analytics Implementation Consultant, and can now quite proudly (and humbly) refer to myself as an expert Adobe Analytics consultant, making this event not only equally exciting but a particularly valuable experience for my new role.

Having graduated from the position of analyst, I expected this year’s Adobe Summit to be a different experience. Not only because this time I at least knew how to get to the ExCel (and which hotel to use). This year, I was eager to come away from the event with different insights and knowledge and couldn’t wait to take the opportunity to join the technical workshops. A few weeks prior to the Summit, I had already planned the sessions I intended to join, and in this post I will go into a little detail of each.

Adobe Summit EMEA Partner Day
I can’t say too much about partner day (it’s top secret, of course), but I do want to share a photo, depicting a potential upcoming feature of Experience Data Warehouse. For Data Analysts this sounds really exciting. Adobe has been developing their Data Science workspace features and I managed to snap a cheeky photo of the Experience Data Warehouse with what looks like SQL code in an Analysis Workspace interface. That’s all I know for now but let’s keep our fingers crossed for this exciting new development!

Extreme Implementation Makeover with Adam Greco
This was by far the best session of the summit and it was only 45 minutes long! Adam packed so much into this session that it was difficult to keep up, including Merchandising eVars, Abandoned revenue and Classifications. Here, I want to talk about an Adobe tip that really inspired me.

Using a Counter Event as a Denominator
The position of a product in a search results list can vary, day to day, hour to hour, making it a particularly tricky exercise to identify the average product position in Adobe Analytics. But fear no more! Adam has an outstanding solution, allowing us to make analysis far simpler through clever implementation.

I think many of us have tried sending the product position in a merchandising event.

Consider this scenario:

A search is made and a jacket is shown 5th in the list. You would set event1=5
A subsequent search is made and the same jacket appears 10th in the list. You would set event1=10
Unfortunately when this data is available in Adobe Analytics, these values are cumulative for each product so the value of event1 is now 15.
What we actually want to identify is the average product position, which in this case would be 7.5 because two searches were made.

You may have tried dividing the value of event1 by the number of internal searches for that product, which will work okay in some cases but it’s likely that your event for internal searches aren’t actually product-specific or merchandised.

On this subject, Adam says you could always send in another merchandising event at the same time, which is your denominator, and always set it to 1. So let’s review this scenario again:

A search is made and a jacket is shown 5th in the list. You would set event1=5 and event2=1
A subsequent search is made and the same jacket appears 10th in the list. You would set event1=10 and, again, event2=1
Now when this data is available in Adobe Analytics, event1 now equals 15 and event2 equals 2.
Simply divide event1 by event2 to get the average product position in search results or a category list.
Your analysts will thank you for this! Not only does this method save significant time by simplifying the analysis process, but they’ve gained far more accurate data.

By using merchandising events, the solution is scalable for complex product catalogues; your analysts no longer need to create their own segmented calculated metrics to work this out for themselves.

You can create the calculated metric for them and share it with your whole group as an approved metric; data governance win!

And that’s just one tip – Adam shared multiple quick-fire tips in this session that blew my mind! I came out of this session feeling wholly inadequate with my current implementation skills but with so much inspiration to do better, smarter things.

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