Every day, we make design decisions without seeing the whole picture. We go by assumptions, internal opinions, or superficial metrics. We see that a screen has traffic, and we assume it’s working. But… is it really serving its purpose?

If you want to build a real product that scales, makes an impact, and lasts, you need a radically deeper view of how every part of your UI is used. That starts with asking the right questions.

The RAAEE framework (Reach, Attractiveness, Adoption, Effectiveness, Engagement) changes the way we understand feature performance. It’s not just about whether something is used, but how it’s used, who is using it, and what real value it’s delivering.

This framework was originally created by Charles Carette, one of the best product leaders I’ve had the privilege to work with. He was Head of Product at Rappi and is currently Product Director at Monzo.

To really understand how the RAAEE framework works, we’ll use a real example and walk through each metric step by step.

Let’s say you’re working on an e-commerce app with 50,000 active users and you launch a feature that lets people save products to a favorites list ❤️.

Now let’s measure how that feature performs using the RAAEE framework.

Most teams don’t measure this. They assume that if a feature is “in production”, everyone sees it. False.

Between misconfigured feature flags, overly strict visibility rules or even rendering errors, it’s common for a portion of your audience to never see that feature. And if they don’t see it, you can’t expect them to use it.

Reach measures how many sessions or users have actual access to a feature. This ranges from whether it appears in the interface, to whether it is enabled in the corresponding environment, segment or version.
Many products lose golden opportunities simply because their functionalities are not well-exposed or do not load correctly. Never assume. Measure.

And here’s a critical point: teams often dive into complex algorithms, redesigns, or ambitious new UIs without first asking the most basic question: “Is anyone even seeing this?”

Reach is the very first metric you should check, and ironically, it’s the most ignored. Before optimizing or reinventing, ensure visibility. It’s the silent blocker of impact.

Example

• Formula
  users who viewed the feature / active users
• Result
  10.000 / 50.000 = 20%
• Interpretation
  20% of your active users had the opportunity to interact with the feature. The other 80% are probably in a control group, have not yet opened the app or have used a part that does not have this functionality.

Here we are talking about desire. If your interface captures the interest necessary for someone to interact with it, whether it is a click, scroll, drag and drop… Whatever interaction the functionality requires.

Attractiveness measures the ability of a feature to catch attention and invite action. It tells you whether the proposal is perceived as relevant, interesting, or useful when it’s seen.

That last part is key: attractiveness is measured only among users who had visibility of the feature. It answers the question: “When users see this, do they want to engage?”
This is not about reach, and not yet about sustained usage. It’s about first intent. You’re zooming in on the exposed audience and checking if the functionality is compelling enough to trigger interaction.

Example

• Formula
  users who interacted with the feature / users who viewed the feature
• Result
  3.000 / 10.000 = 30%
• Interpretation
  30% of those who saw the heart icon clicked at least once. This measures initial interest.

Same core idea as Attractiveness, but applied to a different user base.

Adoption measures what percentage of your entire active user base has actually interacted with the feature at least once. It gives you a clear view of how adopted the functionality is at a product-level.

While Attractiveness focuses only on users who had visibility of the feature, Adoption widens the lens to include all active users, whether they saw the feature or not.
This broader scope helps you understand the overall relevance of the feature within your product.

Adoption can be low for valid reasons: limited rollouts, premium-only access, or niche utility. So a low number isn’t always a problem.

But here’s the catch: low adoption is only acceptable if you can explain it.
If visibility isn’t restricted and the feature is meant for a wide audience, yet adoption remains low, that’s a red flag. You may be facing an issue of discoverability, unclear value, poor integration, or all of the above. And that means it’s time to dig deeper.

Example

• Formula
  users who interacted with the feature / active users
• Result
  3.000 / 50.000 = 6%
• Interpretation
  6% of your entire active user base used this feature. This data tells you how adopted the feature is at a product-level.

Here it’s no longer about getting the user in, it’s about getting what they came for. An effective feature delivers value without friction. If someone enters, interacts and leaves without achieving anything, you have failed because the user has not been able to capture the value you are offering.

Effectiveness can be affected by bad content, bad information architecture, bad usability… Or simply that the value you offer is not what the user expected to find.

Here you must define what “get value” means. In this case following the example of saving favorite products, we decided that the goal is to get users “to review their list of favorites”.

Example

• Formula
  users who saved and then accessed their list / users who interacted with the feature
• Result
  1500 / 3.000 = 50%
• Interpretation
  50% of those who interacted with the feature obtained its real value. the rest may have used it out of curiosity, error or simply did not understand how to take advantage of it.

Once someone adopts the functionality, you need to understand the frequency and depth of use. Is it something that becomes habit? Or is it a “one-time visit”?

Engagement tells you whether a feature becomes part of recurring behavior. Here we talk about repetition, frequency and depth of use.

But hey, not all features need high engagement, think for example about the user profile configuration.

Example

• Results
  Users who saved more than once: 500
  Users who saved more 5 times: 200
  Users who accessed their list more than once: 100
• Interpretation
  Repeated usege is low. Perhaps it needs better visibility, stronger reminders, or accumulated value (like recommendations based on favorites).

Attractiveness vs. Effectiveness

This cross tells you whether your feature is winning attention, delivering value, or failing at both. It helps identify if your problem is in the messaging or in the experience itself.

1. High attractiveness, high effectiveness:
   The ideal scenario. Users are drawn to the feature and find value once they interact with it. Invest in scaling, refining, or promoting this functionality further.
   E.g.: Instagram Stories
2. High attractiveness, low effectiveness
   You’re selling the right idea, but the experience is broken or underdelivers. Users are curious, but disappointed. Fix the flow, simplify the journey, or realign the promise with the outcome.
   E.g.: Clubhouse
3. Low attractiveness, high effectiveness
   Hidden gem. The value is there, but users aren’t noticing or trying it. Likely a discoverability or positioning problem. Improve visibility, entry points, or messaging.
   E.g.: Google Calendar Goals
4. Low attractiveness, low effectiveness
   Dead weight. Neither appealing nor useful. Unless it’s highly strategic, consider removing it to simplify your product and refocus your team’s attention.
   E.g.: Facebook Poke

Engagement vs. Adoption

This matrix reveals whether a feature is broadly used, habit-forming, or simply irrelevant. It helps you decide whether to improve, promote, or retire a functionality.

1. High adoption, high engagement
   Core feature. Many users are using it, and they come back often. This is likely essential to your product experience. Double down and protect its performance.
   E.g.: Slack Channels
2. High adoption, low engagement
   Useful, but not habit-forming. Could be a utility-type feature (like settings or profile edit) that users access occasionally. Optimize if needed, but don’t overinvest.
   E.g.: Account Settings
3. Low adoption, high engagement
   Niche but loved. Few users discover it, but those who do, use it intensely. Strong signal of potential. Improve onboarding or discoverability and test traction in broader segments.
   E.g.: Notion templates
4. Low adoption, low engagement
   Clear signal to cut. Not used and not missed. Unless there’s strong strategic rationale, remove it to reduce complexity and focus on what actually matters.
   E.g.: Google+

Extra: Amplitude offers a great tool to build an engagement matrix quickly.

Remember, every frontend feature you ship should answer the five golden questions:

1. Do users see it? → Reach
2. Do they want to interact? → Attractiveness
3. Is it relevant to my user base? → Adoption
4. Are they getting value from it? → Effectiveness
5. Are they coming back to it? → Engagement

These five questions are the foundation of product insight. If you can’t answer them, you’re not really tracking, you’re just logging data.

Forget about vanity dashboards and bloated event lists. Start here.
Instrument these five metrics for every relevant feature. Once you have this baseline, you’ll know where the problem is and what to do next.

Only then should you move on to funnels, cohorts, LTV and all the rest.

Because you can’t improve what you don’t understand.

Product design isn’t just about aesthetics or usability, it’s also about business. Deciding what to remove is just as important as deciding what to ship. Tracking every part of your UI at this level of depth allows you to build products that are more focused, more coherent, and ultimately more profitable.

This kind of tracking also helps you:

• Prioritize with real data, not assumptions
• Spot where user flows are breaking
• Back up decisions with evidence when talking to stakeholders
• Optimize resources (especially critical in startups)
• Connect design to actual outcomes

This tracking also connects your design decisions directly to business outcomes, turning UX into strategic impact.

Great design without data is just a beautiful opinion. And strategy without real usage is just another presentation.

Tracking every part of your UI isn’t micromanagement. It’s precision. It’s professionalism. It’s design that drives outcomes.

Because if you don’t truly understand what’s happening in your interface, how can you expect to make it better?