Product Field Guide

Heart Metrics

Quantifying customer love

Heart Metrics

Product managers can be myopic about building. I gravitate toward development because the creation process is enjoyable; making a vision come to life feels productive and satisfying. But over-indexing on building is a fool’s errand if customers don’t use and enjoy the product.

Google invented a framework for user-centric design that broadly applies to any product. Five dimensions, aptly acronymized as HEART, measure customer love:

  • H for Happiness
  • E for Engagement
  • A for Adoption
  • R for Retention
  • T for Task Success

Each metric will shape how one builds, markets, sells, and supports a product or feature. External or internal solutions can leverage this framework; some metrics will be more critical for a given initiative. A product manager should consider each during product design, as the dimensions will require a degree of interactivity and built-in analytics to measure effectively.

I will present them logically rather than ordering them to satisfy an acronym.


Adoption is concerned with gaining new users to a product or feature. A product is worthless without users, so adoption is a critical step. Consider this a sales and marketing problem—how can you acquire customers?

Depending on the product or feature, we could measure adoption based on new signups, accounts created, purchases made, or the percentage of the population that upgraded to a new version.

Let’s consider the Product Field Guide email newsletter: the number of subscribers is a decent measure of adoption.

Task Success

Task Success is an objective measure of product effectiveness. Think of this as an engineering problem—does the thing work as expected?

At the most basic, we might measure the error rate of users or how efficiently they accomplish their goals. Does the feature reduce the user’s cycle time to complete a process? Were they able to create a profile, upload a photo, process a payment, or submit a form?

For a newsletter, we could measure delivery rate—how often a sent message arrives in an inbox without going to spam. We might also consider the signup flow and how many clicks it takes for a prospective reader to subscribe.


Engagement is the degree of user interaction with the product. This is where the magic happens—the telling indicator of a feature falling or flying. Think of this as a product design problem. Customers will engage if we have product-market fit and add value.

We might measure the number of visits over time (i.e., per hour, day, or week), the number of tasks completed, the time spent in-app, or the frequency of clicks/taps/hovers on certain portions of the experience. Aim to assess the level of user involvement.

For a newsletter, engagement could be the number of email opens and click rates. We might look at how many comments we receive on a post or how often individual users open a message.


Retention is how often existing users return to the product. A working solution or healthy acquisition rate isn’t enough if customers don’t use the product consistently. Consider this an account management problem—how can we continue adding value to users over time?

There are two parallel paths to measure retention—finance and usage. Financial measures like renewal rate, churn, and recurring revenue indicate that the product keeps providing value or customers wouldn’t continue to pay for it. But usage metrics—like active users over time—are often better assessments of product efficacy.

For a newsletter, unsubscribes (churn) or open rate over time can represent subscriber retention.


Happiness is concerned with user sentiment. This is squishy territory, as customer attitudes around a product are difficult to quantify. Whereas the other dimensions are measured passively—like clickstream logging or financial reports—happiness is often collected actively through surveys.

Typical metrics include customer satisfaction (CSAT) or net promoter score (NPS), calculated by aggregating user ratings. Natural language processing can systematically parse free-text fields like user reviews or comments to gauge user sentiment. While this data is precious to receive, it’s costly to collect—regarding employee and customer time—so we should consider it carefully.

For a newsletter, the number of post likes or a qualitative assessment of comments can measure user happiness.

This article was last updated on 26 Feb 2024.

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