HEART vs AARRR: Which Metrics Framework Should You Use?

Two Lenses on Product Health

Every product team needs a metrics framework. Without one, you measure what is easy instead of what matters, you drown in dashboards, and you cannot answer "is the product getting better?" with confidence.

HEART and AARRR are the two most influential metrics frameworks in product management, and they answer different questions. HEART asks: is the user experience good? AARRR asks: is the business growing? Both are valuable. The right choice depends on your product's maturity, your team's goals, and who needs to see the numbers.

Quick Comparison

HEART: Deep Dive

Google's HEART framework was published in 2010 by researchers in the Search Quality team. It was designed to solve a specific problem: existing web analytics metrics (page views, bounce rate, time on site) did not capture whether users were actually having a good experience.

HEART provides five dimensions that cover the full spectrum of user experience quality. For a detailed breakdown of each dimension with implementation examples, see the HEART framework guide.

The Five Dimensions

Happiness. Subjective user satisfaction. Measured through surveys (NPS, CSAT, SUS), in-app ratings, or sentiment analysis. Happiness is the only HEART dimension that requires asking users directly. It captures what event tracking cannot: does the user feel good about the product? A product can have high engagement but low happiness if users are stuck using it because of switching costs. Happiness separates "people use this" from "people like this."

Engagement. How frequently and deeply users interact with the product. Measured through session frequency, time in app, feature usage depth, and actions per session. High engagement means users are finding value repeatedly. Low engagement suggests the product is not sticky enough. The distinction between passive engagement (opening the app) and active engagement (completing meaningful actions) matters. Track the actions that correlate with value delivery, not just presence.

Adoption. New users (or existing users of new features) taking a specific action for the first time. Measured through new user activation rates, feature adoption percentages, and first-use completion. Adoption tells you whether new things you build are actually getting used. A feature that ships to 100,000 users but is adopted by 2% is a different story than one adopted by 40%. Adoption is the metric that keeps product teams honest about the value of their work.

Retention. Users returning to the product over time. Measured through N-day retention, cohort retention curves, and churn rate. Retention is the single most important metric for long-term product health. Both HEART and AARRR include it, which signals how fundamental it is. A product with 80% week-4 retention will outperform one with 40% retention regardless of how much the second product spends on acquisition.

Task Success. How efficiently users complete core workflows. Measured through task completion rate, time on task, and error rate. Task Success is the most UX-specific dimension. It captures whether the product works well, not just whether people use it. A checkout flow with a 94% completion rate is meaningfully better than one with 78%, even if both generate revenue. Task Success is where UX investment translates to measurable improvement.

The GSM Process

HEART is designed to be used with the Goals-Signals-Metrics (GSM) process:

1. Goals. For each HEART dimension, define what success looks like for your product. "Users find what they need quickly" (Task Success). "Users come back weekly" (Retention). Goals should be specific enough to act on but broad enough to last a quarter
2. Signals. Identify observable user behaviors that indicate progress toward each goal. "Users complete search within 10 seconds" (Task Success). "Users log in 3+ times per week" (Retention). Signals are the bridge between abstract goals and concrete numbers
3. Metrics. Select a specific, measurable number for each signal. "P50 search completion time" (Task Success). "Week 4 retention rate" (Retention). Metrics should be unambiguous, trackable, and actionable

The GSM process prevents the common mistake of picking metrics before defining goals. Without it, teams end up tracking whatever is easy to measure instead of what matters. This is the difference between "we track 47 metrics" and "we track 5 metrics that tell us whether the product is improving."

Strengths of HEART

• Captures UX quality. The only widely adopted framework that measures subjective user satisfaction (Happiness) and workflow efficiency (Task Success) alongside behavioral metrics. Growth frameworks miss these dimensions entirely. A product can grow while delivering a mediocre experience. HEART catches that
• Feature-level applicability. HEART works at the product level, the feature level, or the workflow level. You can run HEART analysis on a new checkout flow, a single feature release, or the entire product. This flexibility makes it useful for quarterly planning and individual sprint goals. You can have a product-level HEART dashboard and a feature-level HEART scorecard running simultaneously
• Design team alignment. HEART gives UX designers and researchers a shared vocabulary with product managers. Designers can point to Task Success and Happiness metrics to justify UX improvements that do not directly move growth numbers. Without HEART, the argument for UX investment is often "trust us, it matters." With HEART, it is "Task Success improved 12%, which will flow through to retention"
• Prevents vanity metrics. The GSM process forces teams to tie every metric to a user-experience goal. This makes it harder to optimize for numbers that look good in dashboards but do not represent real user value. Vanity metrics (total signups, page views, time on site) get filtered out because they fail the "does this connect to a user experience goal?" test
• Complementary to growth frameworks. HEART is not anti-growth. It adds a UX lens to whatever growth framework you already use. If AARRR shows low retention, HEART's Task Success and Happiness metrics help explain why retention is low. This makes HEART a powerful diagnostic companion to growth-focused measurement

Weaknesses of HEART

• No revenue connection. HEART does not explicitly measure business outcomes. Better Happiness and Task Success should lead to better Retention and Revenue, but the link is indirect. Executives who think in business outcomes find HEART metrics hard to act on. When the CEO asks "how is the product doing?" and you answer with Task Success rates, the response is usually "but what about revenue?"
• Survey dependency. Happiness requires surveying users, which adds implementation overhead and introduces response bias. Survey-based metrics update slowly (monthly or quarterly) compared to event-based metrics (real-time). The users who respond to surveys are a biased sample. Getting statistically meaningful Happiness data requires careful survey design and distribution
• Implementation effort. Task Success requires instrumenting task start, completion, and error events for each core workflow. This is more engineering effort than tracking page views and clicks. Teams with limited instrumentation capacity may struggle to implement HEART fully. You cannot measure Task Success for a workflow if you do not track the workflow's start and end events
• Less intuitive for non-product teams. "Task Success rate for onboarding improved from 72% to 81%" means everything to a product team and nothing to a sales leader. HEART metrics require translation for cross-functional communication. This overhead is real. If you spend more time explaining the metrics than acting on them, the framework is adding friction instead of removing it

AARRR: Deep Dive

Dave McClure's AARRR framework (pirate metrics) was introduced in 2007 and became the default metrics model for startups. Its power is simplicity: five stages that map the entire customer lifecycle from first visit to advocacy. For a full breakdown of each metric with benchmarks, see the AARRR pirate metrics guide.

The Five Stages

Acquisition. How users find your product. Measured through traffic by channel, cost per acquisition, and conversion rate from visitor to sign-up. Acquisition metrics tell you which channels are worth investing in and which are leaking budget. The key question is not "how many visitors?" but "how many visitors from channels that convert?"

Activation. The user's first experience of value. Measured through onboarding completion rate, time-to-first-key-action, and activation rate. Activation is the most actionable stage for most products because small improvements compound across every user who signs up. If 1,000 users sign up per month and you improve activation from 30% to 40%, that is 100 more activated users per month with zero additional acquisition spend.

Retention. Users coming back after their first session. Measured through N-day retention, cohort curves, and weekly/monthly active user ratios. Retention separates products with genuine value from products with good marketing. If Retention is low, fixing Acquisition is a waste of money. Every dollar spent acquiring a user who churns in week 1 is a dollar spent on a temporary vanity metric.

Revenue. Users paying for the product. Measured through conversion rate (free to paid), ARPU, LTV, and expansion revenue. Revenue connects product usage to business outcomes. For a deeper look at the metrics that matter most for B2B SaaS, see the complete guide to product metrics. Revenue is where the product team's work translates to the financial metrics that fund the company.

Referral. Users recommending the product to others. Measured through referral rate, viral coefficient, and NPS. Referral is the cheapest acquisition channel and a strong signal of product-market fit. If users voluntarily tell others about your product, you are building something worth using. Referral is also the hardest stage to engineer. You cannot force word-of-mouth. You can only build a product good enough that people want to share it.

Strengths of AARRR

• Maps to business outcomes. Every stage connects to revenue. Acquisition feeds Activation, which feeds Retention, which feeds Revenue, which funds more Acquisition. Executives understand this model intuitively because it mirrors how businesses work. No translation layer needed. A board deck built on AARRR metrics tells a story that investors follow immediately
• Identifies bottlenecks. The funnel structure makes it immediately clear where users are dropping off. If Acquisition is strong but Activation is weak, you know exactly where to focus. This diagnostic power makes AARRR the best framework for prioritization at the macro level. Instead of debating which initiative matters most, you can point to the leakiest stage and focus there
• Simple to implement. AARRR metrics map to standard analytics events that most tools track by default: page views (Acquisition), key actions (Activation), return visits (Retention), payment events (Revenue), invite actions (Referral). A basic implementation takes hours, not weeks. Any product analytics tool can track the core AARRR metrics without custom engineering. Use the RICE Calculator to prioritize which specific improvements to make within each stage
• Universal language. AARRR is understood across product, marketing, sales, and leadership. When a PM says "our Activation rate dropped 5% this month," everyone in the room understands the implication. This shared vocabulary reduces the translation overhead that HEART metrics require. It also makes cross-functional alignment easier because everyone is looking at the same funnel
• Growth-oriented. AARRR is designed to grow a product. It helps teams focus on the biggest growth lever at any given stage. Early-stage products typically optimize Activation and Retention. Growth-stage products optimize Revenue and Referral. The framework scales with the company. A 5-person startup and a 500-person company can both use AARRR, just with different metric definitions and targets

Weaknesses of AARRR

• Ignores experience quality. AARRR tells you how many users activate, retain, and pay. It does not tell you whether they enjoy using the product. A product can have decent Retention numbers while users grudgingly tolerate it because switching costs are high. HEART's Happiness and Task Success dimensions capture what AARRR misses. This blind spot becomes dangerous when competitors offer a better experience and your retention drops suddenly
• Funnel oversimplification. Real user journeys are not linear. Users loop back between stages, skip stages, and reactivate after churning. AARRR's sequential model is useful as a mental model but does not capture the messy reality of user behavior. A user who churns in month 3, comes back in month 6, and upgrades in month 7 does not fit neatly into the funnel
• Acquisition bias. Teams using AARRR often over-invest in Acquisition because it is the first stage and the easiest to measure. The framework does not inherently prevent this. Disciplined teams start from Retention and work backward, but the funnel's top-down structure encourages the opposite. Spending on Acquisition feels productive even when the real problem is Retention
• Referral measurement challenges. Referral is the hardest AARRR stage to measure accurately. Viral loops, word-of-mouth, and organic sharing are partially invisible to analytics. Many teams track invite-send rate as a proxy, but this understates the actual referral impact. A user who tells a colleague about your product over lunch does not show up in any dashboard
• No feature-level granularity. AARRR works at the product level. It does not easily decompose to "how is this specific feature performing?" HEART's ability to measure individual features and workflows is more useful for sprint-level decisions. When a designer asks "did our redesign improve the experience?" AARRR has no answer. HEART does

When to Use Each Framework

Use HEART When

• Your product has achieved product-market fit and the priority is deepening engagement and improving experience quality
• You have a UX research team or dedicated designer who needs quantitative metrics to justify design investments
• You are optimizing a specific feature or workflow (onboarding flow, search experience, checkout process) and need granular UX metrics
• Your team's goal is reducing friction rather than increasing top-of-funnel volume
• You need to measure user satisfaction as a leading indicator of retention and NPS

Use AARRR When

• You are pre-product-market fit and need to identify which stage of the funnel is broken
• Your team includes growth marketers who need metrics that span acquisition through revenue
• You need metrics that executives and investors understand without translation
• Your primary challenge is growth rate: more users, better conversion, higher revenue
• You want a framework that is fast to implement with standard analytics tools

Use Both When

• Your product organization is large enough to have separate UX and growth functions
• You want AARRR to identify the macro bottleneck (which funnel stage) and HEART to diagnose the micro cause (which UX problem within that stage)
• You report to multiple stakeholders: AARRR for leadership, HEART for the product and design team
• Your product is mature enough that both growth efficiency and experience quality are strategic priorities

Implementation Comparison

AARRR Implementation (Week 1)

1. Define one metric per stage. For example: Acquisition = organic sign-ups per week; Activation = percentage completing onboarding within 24 hours; Retention = week-4 retention rate; Revenue = free-to-paid conversion rate; Referral = percentage of users who send an invite
2. Instrument events in your analytics tool. Most analytics platforms (Amplitude, PostHog, Mixpanel) track these events natively or with minimal configuration
3. Build a single dashboard showing all five metrics with week-over-week trends. Keep it on one screen. If the dashboard requires scrolling, it has too many metrics
4. Review weekly. Identify the stage with the biggest drop-off. Allocate sprint capacity to that stage
5. As you mature, add secondary metrics per stage. But start with one metric per stage. Simplicity is the point

HEART Implementation (Weeks 1-3)

1. Run the GSM process for each relevant dimension. You do not need all five. Most teams start with 2-3 dimensions (typically Engagement, Retention, and Task Success). Add Happiness and Adoption when you have the instrumentation capacity
2. Instrument event-based metrics for Engagement, Adoption, Retention, and Task Success. Task Success requires tracking workflow start, completion, and error events for each core workflow. This is the most engineering-intensive step
3. Set up a survey mechanism for Happiness. In-app NPS (monthly or quarterly) is the most common approach. Use NPS benchmarks for your industry to set targets
4. Build a dashboard per product area (not one global dashboard). HEART is most useful when applied to specific features or workflows
5. Review monthly or quarterly. Tie insights to specific feature and design initiatives. HEART metrics move slower than AARRR metrics, so weekly review is often premature

Using Both Together

1. Start with AARRR to identify your macro bottleneck (e.g., Activation is 25%, well below the 40% benchmark for your category)
2. Apply HEART to the bottleneck stage. For Activation, measure Task Success (what percentage of users complete each onboarding step?) and Happiness (do users who complete onboarding report satisfaction?)
3. Use HEART findings to prioritize specific UX improvements within the bottleneck stage
4. Measure the impact of improvements via both frameworks: did HEART's Task Success improve? Did AARRR's Activation rate improve?
5. Once the bottleneck is resolved, move HEART analysis to the next bottleneck stage

Real-World Application Examples

Early-Stage SaaS (15 employees, pre-PMF)

Use AARRR only. The team needs to find product-market fit, which means identifying where users drop off and fixing the leakiest stage. HEART's nuance is valuable but premature. The priority is: are users signing up (Acquisition)? Are they experiencing value (Activation)? Are they coming back (Retention)?

Growth-Stage B2B SaaS (150 employees, $20M ARR)

Use both. AARRR tracks the growth funnel and reports to leadership. HEART (applied to the onboarding flow, the core workflow, and the expansion trigger) helps the product team improve the experience that drives retention and expansion revenue. The growth team owns AARRR. The product team owns HEART.

Enterprise Product (500+ employees, multiple products)

Use HEART as the primary product team framework. At this scale, the growth funnel is managed by dedicated marketing and sales teams with their own metrics (MQLs, pipeline, win rate). The product team's job is to make the product better, which is what HEART measures. Use AARRR for product-led growth motions (free tier, self-serve onboarding) where the product team owns the full funnel.

The Verdict

If you can only pick one framework, pick AARRR. It is simpler to implement, easier to communicate, and directly connects product metrics to business outcomes. Most product teams, especially at startups and growth-stage companies, will get more value from a clear funnel model than from a UX-focused measurement system.

Add HEART when your product is mature enough that experience quality is a strategic differentiator. When your Retention is stable and your Revenue model is proven, HEART's Happiness and Task Success dimensions reveal the next layer of improvement opportunities that AARRR cannot see.

The best product teams eventually use both: AARRR as the operating system for growth, and HEART as the diagnostic tool for experience quality. For more on selecting and implementing the right metrics, see the Product Analytics Handbook and the complete guide to product metrics.