Minimalist digital analytics - less can be more!
Mikko Piippo
Digital Analytics = Collect, Analyse, Recommend & Experiment | Open for client work | Piwik PRO | Matomo Analytics | GA4 | BigQuery
Most digital marketers and website owners want to get the best out of their analytics.
For many, this means tracking as much data as possible.
Maximalist approach in analytics
I call this the maximalist approach in digital analytics.
Maximalists track every click and every scroll. They create a lot of data. But then it can be difficult to identify the important events and metrics.
Some metrics go up, some down, and they don’t know which ones are most important.
This is where the minimalist approach comes in.
Minimalist approach in analytics
The minimalists focus on gathering and analysing only the necessary data. The goal is to get the most out of the data without drowning in details.
This is beneficial especially for small businesses. They don’t need to spend as much time or money on collecting and analysing data.
For big businesses, the minimalist approach helps them focus on the most important metrics.
The first step is to identify which events and metrics are critical to your success. This could include purchases, add to cart events and leads generated. Once you have identified the critical data, track it as accurately as possible.
Another key part of the minimalist approach is to avoid tracking unnecessary events.
It is easy to collect data that is never used for anything. If you do this, it will be difficult to see the forest from trees.?To prevent this, take the time to consider which events are most important to you, and then only track those.
领英推荐
Start with Minimum Viable Analytics
Minimum viable analytics (MVA) is another interesting concept.
With MVA, I mean a simple, minimalist digital analytics implementation needed for decision-making. This includes measuring
A MVA implementation helps?a company to become data-driven quickly and cheaply.
After some growth, the company can build a more complex digital analytics implementation. This could involve complex integrations,?and machine learning technologies.
Start small and build up over time.?It avoids building too complex infrastructure, too early.
Focus on relevance and quality
It is also important to regularly review the data that you’re collecting and make sure that it’s still
If the data is no longer useful, delete it or archive it to prevent it from becoming clutter.
One way to do this is to use a short data retention. If you don’t need old data, let it go.
The minimalist approach to digital analytics can help you to get the most out of the data that you collect. At the same time, it saves you a lot of work: it is much easier to have a high-quality minimalist implementation than a high-quality maximalist implementation.
Less can be more, also in digital analytics.
This was first published as a blog post in Hopkins' blog.
CTO & Co-Founder at Arkkeo
1 年Interesting. Every now and then I come across this same idea of tracking only what is relevant. I completely agree that from a cost- or privacy point of view not everything should be tracked. Otherwise I fail to understand why more data would not be more? And please note that by data I mean raw data (like raw events) and not metrics.? Regarding metrics, I agree to have only a few key relevant to the business and over time maybe add some leading indicators to some key metrics if the real ones are too lagging, or maybe track a few “micro goals”, etc. but following and optimizing for too many metrics will lead to a disaster. Personally, I am a huge fan of the “One Metric that Counts” approach, where a startup at any given time has one metric which is the most important right now and most optimization effort is focused on that one.? In my experience tracking more events is better than fewer, especially in the beginning. Very often when some key metric suddenly drops or something seems to be off it is good to have a lot of data to explore and understand what really goes on. But I would like to hear counter arguments to this. BI work eats up too much of my time and I am eager to explore new approaches if they can save time.?
Senior Solutions Consultant | Generative AI, Data & Insights, CDP, Martech, Adtech, CX @Adobe | Nordic & Baltic ???? ???? ???? ???? ???? ???? | CMB Chair / HHJ PJ
1 年Interesting thoughts Mikko Piippo. About 2 years ago I wrote about "Stop Wasting Data." Google it and you might find it interesting. Unused data is actually potentially huge waste of electricity and clean water, globally. To certain extent I agree your thought of minimalistic approach. The only thing that would potentially be left out in this approach is the unknown potential that could be uncovered with Machine Learning models. AI features by Analytics Platform can find the anomalies and patterns and it could require most likely also the data points that are out of the minimalistic scope. Then again, minimalistic approach is very useful in case of Realtime Customer Profile Data or Realtime Customer Journey Orchestration where the most important data is as recent and as accurate snapshot of the profile and audience as possible. So, definitely not black and white topic, but very interesting.
Tired of being ripped off by your AB Testing provider?
1 年Love the term MVA. It's all about being able to do something with what you track. With Glassbox, for example, we have auto tagging, but it's the heatmaps, funnels and struggle and error detection which makes it actionable.