Measuring Product Evolution Through Core Features - Miro Analytics Setup

Imagine spending months meticulously tracking every feature of your product, collecting data on every user interaction, only to realize you're missing something crucial: how your core product is transforming. When a product grows by adding new features - like Asana adding goals or Slack adding huddles - measuring success is relatively straightforward. But what happens when your product evolves by transforming its core offering?

This is where traditional analytics often falls short. Using Miro as our example, where a simple whiteboard has the potential to become an innovation workspace, we'll explore the fascinating challenge of measuring not just feature adoption, but fundamental product transformation.

In this content series - season 1, I create a tracking plan for a typical start-up tool every day for four weeks (I take a break on the weekend), so 20 in total. This is the 7th one: Miro.

The Evolution Challenge: Why Measuring Product Transformation Is Different Than Feature Adoption

The Hidden Complexity of Core Feature Evolution

When we talk about product evolution, the story usually goes like this: you start with core features, then add new ones over time. Think Asana adding goals and workflows, or Slack introducing huddles. These are easy to track - you measure adoption, usage patterns, and impact. Simple enough, right?

But sometimes, products take a different path. Instead of adding new features, they transform their core. This is Miro's journey - taking their fundamental concept of a "board" and expanding what it means.

As I explained in the video: "What Miro did, or what Miro's doing right now with their product direction, is they're taking their core entity, the board, and making it more powerful. They extended the capabilities of the board significantly over time."

This creates fascinating challenges:

• You're not just tracking adoption of something new
• The same feature needs to support both simple and complex use cases
• Users might transition gradually without clear "moments" to track - because they don't recognize the transition
• Success looks different for different types of users

Think about it like this: When Asana adds a new goals feature, it's clear when someone starts using it. But how do you measure when a Miro board transforms from a simple whiteboard into a true innovation workspace? When does a board cross that threshold from brainstorming tool to becoming the central hub for a team's entire project?

This is the hidden complexity of core feature evolution - measuring not just what users do, but how their entire way of working with your product transforms over time.

This work is based on the chapters about event data design in my book Analytics Implementation Workbook. There, you can read more details about the D3L framework.

The Limits of Traditional Feature Analytics

Traditional analytics loves clean, countable moments. A feature gets launched, users try it, some adopt it - metrics go up or down. But when you're transforming a core feature like Miro's board, these clean moments disappear. You can't just count how many people "activated" the innovation workspace feature because it's not a switch that gets flipped.

"I think we still have to find what actually defines this," as I mentioned in the video. "We don't know yet which kind of patterns we can put into activities. This is where I want to get to."

The usual analytics approaches fall short in several ways:

• Click tracking becomes meaningless (who cares if someone clicked a button when you're trying to measure workspace transformation?)
• Feature adoption metrics don't capture evolution of use
• Simple counts (like number of assets on a board) create too much noise without signal