This post is the first post of the eight-part-series Web Analytics with Adobe’s Customer Journey Analytics, showing how web sites can be analyzed better using Adobe’s next evolution of Adobe Analytics. In this part, we discuss the motivation and scope of this project and why, eventually, existing Adobe Analytics and new customers will start moving to Adobe’s Customer Journey Analytics.
If you found this article, chances are high you work in or adjacent to the field of digital analytics or web analytics. It doesn’t really matter if you are an existing Adobe Analytics user, on the Google stack, or just looking for your very first web analytics tool. If you have been following the trends and discussions in our industry in the recent time, you will likely already have caught on the massive changes that both our industry and Adobe’s products go through. With changes to privacy requirements and cookie functionality on one side as well as new expectations and functionality in terms of cross-device-capability, there are some big things happening right now.
At the same time, Adobe has earned a reputation for building the most sophisticated and feature-rich web analytics solution on the market with its tool Adobe Analytics. While I’m a bit biased by my own, excellent experiences with the product, Adobe Analytics can stand any comparison with any other product out there in terms of how it can bring value to a mature analytics practice. But even with this incredible offering, the trends described above have been recognized by Adobe and caused them to develop a new, even more sophisticated tool called Customer Journey Analytics.
While a lot of the value that lies in Adobe Analytics today comes from it’s ecosystem (integrations with the Adobe Experience Cloud and other Adobe tools, existing 3rd party integrations, etc.) I would say the majority comes from it’s own features, like the data model and Analysis Workspace. With Customer Journey Analytics, Adobe has fundamentally reworked this core of how Adobe Analytics creates value in this new product. And even if Adobe Analytics still has some advantages today, there is little doubt that most (if not all) companies will eventually move from Adobe Analytics to Customer Journey Analytics, migrating their existing implementations to this new ecosystem of tools, processes, and ways of thinking.
With this series, I want to take you along on my personal journey of going from zero to hero with Customer Journey Analytics for a digital analytics use case. Since I’ve been doing Adobe Analytics implementations and value creation for years, I will try to not only show you what needs to be done, but also why this fundamentally changes our approach to how we model our business, plan implementations, collect data, enrich data, and set our tools up in a way that, ideally, can create scaleable value to the business. If you are involved with any of those activities (or just curious) I hope that you will find value in those posts. To start things off, let’s take a look at why we should even bother moving from Adobe Analytics to Customer Journey Analytics.
The flaws of a perfect product
I could (and occasionally do) spend whole days raving about Adobe Analytics. For me, it’s not just the best solution available today, but an actually very enjoyable user experience once you understood how to handle it. I’m not (only) talking about the frontend, but the whole offering as a tool. A look on the list of features confirms this further, with analysis features like event-level segmentation, unsampled data, plenty of automatically collected data points, lots of custom dimensions and events, calculated metrics, builtin integrations like Analytics for Target, and so on.
But on the other hand, a lot of the potential value of any analytics tool comes from its database engine. And while Adobe Analytics has the best most people know, it has been built some years ago and, as such, has some limitations from today’s view. For example, there are plenty of dimensions and events, but there is a limit on how many we can have. Also, those dimensions have limits on how long they can be. If we want to track things as lists of values, we can generally do that, but only a very limited number of variables support lists. And once data is collected, it can’t be altered or removed without major effort and investment.
From a data model perspective, there are some inherent limitations to the backend engine (which feels a bit dated by now) as well. For Adobe Analytics (and practically every other web analytics tools as well), data needs to be a flat list of dimensions and events. To be able to collect data, we need to fit the information we want to track in a list of dimensions and events that don’t hold any relation to each other. But in reality, there is a natural structure to data: Websites have pages. Pages have elements on them. And a shopping cart holds its items. Those are some of the natural structures that we need to abandon when collecting data today. Also, there are properties and context to both the elements on websites or apps (think of the positioning of a teaser on a page) as well as to the interactions that a user can do. Respecting any of those structures and hierarchies can be tricky, since Adobe Analytics only really supports structured data in the eCommerce use cases with the product variable.
Lastly, there is the architectural perspective. If you want to have data in Analytics, there are some restrictions on how you can actually bring it to the system. That is because there is a lot of processing involved to both make the data digestible for the system and also enable some of the use cases. The best example for this is the Visitor Profile, which holds things like a Visit Number or the original referrer to a page. If you have ever wondered why you need to can’t feed hits into Analytics that are not correctly ordered on the time scale, this is why, because it would possibly affect the processing that needs to happen to figure out the Visit Number.
So while Adobe Analytics is by far the best web analytics system today, you can find some limitations when you start looking. Full disclosure here: I wasn’t aware that those things were actual limitations until I have seen and used a system that didn’t have any of those limitations. Once I have learned that things can work differently, there was no way for me to not feel limited with traditional analytics systems. Of course it will take a while for the industry to adopt this new set of expectations and see the limitations of existing solutions, but it will undoubtedly happen.
Adobe Analytics, but unlimitedly better
With Adobe’s brand new Customer Journey Analytics, they addressed a lot of the things users have been asking for a long time. With CJA, we have unlimited(!) dimensions and events available. Let that sink in for a second. One of the most fundamental limitations that we assumed to be universal is just gone. And not only that: We are used to dimensions holding text values and events holding numbers, without any way to go from one to the other after data has been collected, right? Guess what: That limitation is also gone. Values can be numeric, text, objects, lists, all of the above, mixtures of all, whatever you like. And it doesn’t just end there, because data can now be hierarchical and nested. We can finally respect the natural structure of our pages and websites, just as I described above. I can’t help but feel that it’s really liberating!
In terms of architecture, there is also a lot of progress. Since Customer Journey Analytics gets its data out of Experience Platform, you have plenty of options to fill your data storage: Import it from Adobe Analytics, use FTP, APIs, manual uploads straight from your computer’s desktop, just as you want. And once you have your data in Customer Journey Analytics, you can just as easy delete it again and start over. This opens up completely new use cases we could never even dream about with plain old Adobe Analytics!
On top of all of that, Adobe has invented Query Service on Platform, which allows us to completely rework our data using standard SQL with some clever additions to help us get started. That way we can invent completely new data points and analytics capabilities unique to our business. We are no longer limited by the data we either collect or rely on Adobe to be included in their backend processing. If we want to have a certain value in our data, we can just create it out of what we already have! In the future, we could even create an open and free Github repository collecting the best queries and custom functionality for everyone to use.
While all of that is great, Adobe still has some homework to do in terms of integrations (for example, Analytics for Target is not there yet), just as all the other companies on the market whose products previously worked very easily with Adobe Analytics. Keep in mind that Customer Journey Analytics and Experience Platform both are brand new, so the market needs to react just like Adobe needs to react internally. But if those integrations are not a big concern for you (in fact, some can be rebuild manually if you put in the work), you may just make the move now and start migrating to Customer Journey Analytics to get all the advantages outlined above.
Times change, tools should too
For this series of posts I want to show how we would approach a complete web analytics implementation with Experience Platform and Customer Journey Analytics while pointing out all the (sometimes very minute) differences in approaches and mindsets. While it obviously breaks my heart to leave Adobe Analytics behind, gathering those experience and evaluating migration paths will be an important task for all of us to ensure future fitness to business challenges.
In the series, we will look at all of the steps we need to take:
- Create the solution design and architecture
- Model our business and its data in Experience Platform
- Create the actual implementation with the new Web SDK and Launch
- Enrich and process data with Query Service
- Create the crucial backend config in Customer Journey Analytics
- Build our first project in Customer Journey Analytics based on the data model
While those new ways of thinking and innovative tools might feel intimidating to some, I think it will be an awesome adventure. It is important to leave behind some of the preconceptions we have accumulated over the years to make the most of the new world we are going to conquer and replace them with something much more versatile. I can only hope you are as excited as I am.
Happy to have you with me on the journey! See you very soon in the next post.