HEART framework to analyze UX metrics

White man holding a pencil while he is drawing a paper wireframe.
Source: Freepik

Do you know what the HEART framework is? For companies that work with development of digital products, for example, this model can bring several benefits, such as: more control over projects and improved user experience.

On this article, we’re going to understand more about:

1- What is HEART framework?
2- How to apply in a project?
3- The benefits to use it.

Developed by Google Ventures in partnership with Digital Telepathy, the HEART framework is a model that involves a set of metrics focused on User Experience.

The word HEART is formed by the acronym:

  • Happiness
  • Engagement
  • Adoption
  • Retention
  • Task Success

HEART helps on the phases of development of a product, since launch until retention of a costumer (the heart of your company).

Visually, this framework is made in the form of a table. In the horizontal column, you must include the HEART metrics. On the vertical side of the table, 3 columns should appear: Goals, Signals and Metrics, as in the table below:

Table with HEART framework data.
HEART framework table

Let’s understand more about each one of those columns: Goals, Signals, and Metrics?

  • Goals: You have to identify the objective of your service or project or even for your feature that your team released. Very aligned with your user’s needs and your business.
  • Signals: How the success or the fail can be identified on the behavior or on the user’s attitudes. Can be complementary metrics that indicates a specific scenario.
  • Metrics: When the signals can be very specific and be traceable or accompanied by a dashboard.

Below you can see some examples of metrics and also how to fill your HEART framework.

Example of types of metric used.
Example of types of metrics used — Image by UX Cheat

The HEART framework can be simple and easy to understand. While the framework may be designed for large projects; there’s nothing to stop it being implemented on smaller projects either — it’s just that the methods used for data collection are likely to vary.

Hope you have enjoyed reading this far, and that my tips will help you to improve your application and metrics analysis in your day-to-day work.

Follow me on LinkedIn and feel free to reach me out as we talk about research when we have a coffee! ☕

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