Minimalist approach in analytics – less is more

Updated 6.11.2024

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.

Start small, grow up later

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 traffic, campaigns, and conversions. Maybe one or two micro-conversions.

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

relevant and
correctly tracked.

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.

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