Last weekend, I attended the 1st Snowplow Meetup in Munich, followed by MeasureCamp Munich the next day.
As always, MeasureCamp far exceeded my expectations, not only in terms of the unconference content but also for the venue where it was held (the Allianz Arena!).
I want to share some takeaways and comments about the sessions I attended.
Web Analytics ≠ Google/Adobe Analytics: rethink your stack
For many years, Google Analytics and Adobe Analytics have been the leaders in the web analytics market.
However, it is important to remember that they are also big black boxes and not very flexible solutions, especially when it comes to collecting behavioural data for product analytics and AI purposes.
Nicolas explained how and why BurdaForward transitioned from Adobe Analytics (and other solutions) to Snowplow Analytics after a year of work.
He discussed the various advantages of Snowplow, such as its flexibility, the ability to enrich data, and having control over the entire process.
Additionally, he highlighted the responsibilities that come with it, such as ensuring everything functions properly, managing infrastructure costs, implementing new customizations, and additional steps in the process.
In this constantly changing world, I really liked the presentation made by Alexander Kirtzel about Walker.js.
In this ever-changing world, I thoroughly enjoyed Alexander Kirtzel’s presentation on Walker.js. According to the official documentation, “Walker.js is an open library to capture user events and send them to any analytics tool. It was created to enable data ownership, improve web data quality, and scale tracking implementation processes.“.
I appreciate the concept of a vendor-agnostic solution that prioritizes the definition and delivery of superior data to any analytics provider.
I’ll definitely look into it as I believe it holds the potential to be a future-proof solution.
Changing technology is easy, changing people is hard
The difficulty of adapting to new solutions and changing established habits (GA4 I’m looking at you) has been a prominent and widely discussed topic during different sessions.
For instance, Snowplow lacks a user interface, which prompted BurdaForward to invest efforts in implementing and customizing Apache Superset, a data visualization platform.
This was done to ensure a certain level of data democracy across the organization and assist data analysts in conducting their daily analyses seamlessly.
Archit Goyal delivered an insightful presentation on the essential role of web analysts in building successful AI models.
A significant point of discussion revolved also around the lack of communication between web analysts and data scientists in many companies.
Web analysts often possess a deeper understanding of the data that should be incorporated into the models and have extensive expertise in the field. As you can see in the picture below, it has been observed that data scientists often do not frequently change the factors they utilize in their AI model development. This practice is problematic since it often overlooks valuable behavioural data, focusing solely on purchase data.
In conclusion, fostering better collaboration between web analysts and data scientists is crucial for leveraging their combined knowledge and optimizing AI model development.
The era of “track everything” has come to an end
Last but not least, probably the hottest topic of the last few years. AdBlockers, Cookie Rejection, ITP,… are impacting the quantity and quality of the data you can collect.
The era where “everything” is trackable has come to an end, you need to focus more on defining business objectives and the specific decisions you aim to address.
Marketing-Mix Modeling, incrementality testing, and attribution are widely discussed and relevant instruments but it is crucial to complement these tools with real customer feedback.
Finding similarities among customer feedback, Marketing-Mix Modeling (MMM), and attribution can serve as evidence of specific behaviors occurring.
During the event, Steen Rasmussen presented an interesting slide about the decay of the business value of data over time.
For instance, he highlighted the fact that everyone is trying to get all of the data out of GA3, despite the fact that much of it may not be utilized due to constantly changing customer behaviours and market conditions.
In summary, the ability to swiftly collect and utilize data is key if you want to maximize its business value.