How to Choose the Right UX Metrics for Your Product
Googles™ HEART Framework helps measure the quality of user experience
Googles™ HEART Framework helps measure the quality of user experience
The HEART framework methodology was created by Google as a way to track user-centred metrics on their own products. The framework is used to help measure the quality of the user experience (UX) of products by creating defined metrics that design teams can use to track progress towards their goals.
You can apply HEART to a specific feature or the entire product.
Using HEART, your team will identify goals, signals, and metrics for each of the five categories.
The HEART framework measures the quality of the user experience by using five metrics which form the acronym:
Happiness
Engagement
Adoption
Retention
Task Success
Measures of user attitudes, often collected via survey.
Level of involvement.
Gaining new users of a product or feature.
The rate at which existing users are returning.
Efficiency, effectiveness, and error rate.
Choose one or two categories in the HEART framework that are the focus of your product or project.
But how do you figure out which metrics to implement and track?
Having clearly defined goals will help you identify the right metrics to measure progress.
Members of your team may each have their own ideas about the goals of the project, but by using this process it aligns everyone to build where you're headed.
A common pitfall is to define your goals in terms of your existing metrics - "well, our goal is to increase the number of leads from our website."
Everyone would like that, but how will the user experience help achieve that goal? Are you interested in increasing the number of visitors to the page or increasing conversions?
Map your goals to lower-level signals.
There are generally several potentially useful signals for a particular goal. Once you have generated those favorable indicators, you may need to pause and assess which ones to focus on.
If you’re already gathering hopeful signals, you can survey the data to understand which signals seem to be the best predictors associated with your particular goal.
First ask, how easy or difficult is each signal to monitor? Is your product designed to register the relevant actions, or could it be? Second, you should select the signals you expect to be sensitive to changes in your design.
Adjust your signals into metrics that you will be tracking over time or use in your A/B testing.The specifics will depend a lot on your particular setup. But, as in the previous step, there may be several metrics that you can create from a given signal.
You'll need to analyze the data that you have already collected to decide what's the most applicable.
You will want to avoid adding “interesting stats” to your list and only use numbers that will actually help you conclude a decision. Considering asking yourself “will these numbers help me make a decision?” Stay focused on the metrics that are related to your goals to avoid going off course.
The Figma template can help you with your product goals.
