The Ultimate Product Metrics Cheat Sheet

Quick Answer (TL;DR)

Product metrics are the quantitative measures that tell you whether your product is succeeding or failing. This cheat sheet organizes 100+ essential metrics into six categories following the pirate metrics (AARRR) framework: Acquisition, Activation, Engagement, Retention, Revenue, and Referral. For each metric you will find the definition, formula, industry benchmarks, and guidance on when to use it. Bookmark this page and return to it whenever you need to choose the right metrics for your product goals.

Why Product Metrics Matter

Every product decision should be informed by data. Without metrics, you are navigating blind --- relying on intuition, stakeholder opinions, or anecdotal evidence. The best product teams build a metrics hierarchy that connects daily operational metrics to strategic business outcomes.

A strong metrics practice helps you:

Prioritize features based on measurable impact rather than gut feeling

Detect problems early before they compound into churn or revenue loss

Communicate progress to leadership with objective evidence

Align teams around shared definitions of success

Run experiments with clear success criteria

"If you cannot measure it, you cannot improve it." --- Peter Drucker

The challenge is not a lack of metrics --- it is knowing which ones matter for your product at your stage. A pre-product-market-fit startup should obsess over activation and retention. A mature SaaS company may focus on expansion revenue and net dollar retention. This cheat sheet gives you the full toolkit so you can select the right instruments.

How to Use This Cheat Sheet

Identify your current priority. Are you focused on growth, monetization, or retention?

Select 3-5 primary metrics that directly measure progress toward your goal.

Add 2-3 guardrail metrics to ensure you are not optimizing one area at the expense of another.

Define benchmarks using the ranges provided here as starting points, then calibrate to your own historical data.

Review quarterly. As your product matures, your metrics should evolve.

Acquisition Metrics

Acquisition metrics measure how effectively you attract new users or customers to your product.

#	Metric	Definition	Formula	Benchmark Range	When to Use
1	Website Traffic	Total visits to your website	Sum of all sessions	Varies by industry	Always; foundational awareness metric
2	Unique Visitors	Distinct individuals visiting your site	Count of unique cookie/user IDs	Varies by industry	When measuring reach vs. frequency
3	Traffic by Source	Breakdown of visits by channel (organic, paid, referral, direct, social)	Sessions per channel / Total sessions	Organic 40-60%, Paid 10-30%	When optimizing channel mix
4	Cost Per Acquisition (CPA)	Average cost to acquire one customer	Total marketing spend / New customers acquired	SaaS: $50-$500; Consumer: $1-$50	When evaluating marketing efficiency
5	Customer Acquisition Cost (CAC)	Fully loaded cost to acquire a customer including sales and marketing	(Sales + Marketing costs) / New customers	SaaS: $200-$2,000+	When calculating unit economics
6	CAC Payback Period	Months to recover acquisition cost	CAC / (Monthly revenue per customer x Gross margin)	SaaS: 12-18 months	When assessing capital efficiency
7	Click-Through Rate (CTR)	Percentage of impressions that result in a click	Clicks / Impressions x 100	Search ads: 2-5%; Display: 0.5-1%	When optimizing ad or email campaigns
8	Cost Per Click (CPC)	Average cost for each click on an ad	Total ad spend / Total clicks	Google Ads: $1-$5; B2B: $3-$10	When managing paid acquisition budgets
9	Cost Per Lead (CPL)	Cost to generate one qualified lead	Marketing spend / Leads generated	B2B SaaS: $30-$200	When evaluating top-of-funnel efficiency
10	Lead-to-Customer Rate	Percentage of leads that become paying customers	New customers / Total leads x 100	B2B: 2-5%; B2C: 1-3%	When assessing sales funnel effectiveness
11	Organic Traffic Growth	Month-over-month growth in organic search visits	(Current month organic - Previous month) / Previous month x 100	5-15% MoM for growing companies	When measuring SEO effectiveness
12	Signup Rate	Percentage of visitors who create an account	Signups / Unique visitors x 100	SaaS: 2-5%; Free tools: 10-20%	When optimizing landing pages
13	Install Rate	Percentage of app store visitors who install	Installs / Store page views x 100	iOS: 30-40%; Android: 20-30%	For mobile apps; ASO optimization
14	Viral Coefficient (K-factor)	Number of new users each existing user brings	Invites sent per user x Conversion rate of invites	>1.0 means viral growth	When evaluating organic growth loops
15	Impression Share	Percentage of available impressions your ads capture	Your impressions / Total eligible impressions x 100	60-80% for branded terms	When assessing paid search competitiveness
16	Marketing Qualified Leads (MQLs)	Leads that meet marketing qualification criteria	Count of leads passing scoring threshold	Varies by business	When aligning marketing and sales
17	Sales Qualified Leads (SQLs)	Leads that sales has accepted as worth pursuing	Count of leads accepted by sales	MQL-to-SQL: 20-40%	When measuring pipeline quality

Key Takeaways for Acquisition

CAC and CAC Payback Period are your most important acquisition metrics for SaaS. If CAC payback exceeds 18 months, your growth may not be sustainable.

Blended CAC (all channels combined) hides channel-level inefficiencies. Always measure CAC by channel.

Organic acquisition (SEO, word of mouth, virality) compounds over time while paid acquisition costs tend to increase.

Activation Metrics

Activation metrics measure how effectively new users experience the core value of your product --- the "aha moment."

#	Metric	Definition	Formula	Benchmark Range	When to Use
18	Activation Rate	Percentage of signups who complete a key action	Users completing key action / Total signups x 100	SaaS: 20-40%	Always; primary onboarding metric
19	Time to Value (TTV)	Time from signup to first value realization	Median time from signup to key action	Minutes to hours ideally	When optimizing onboarding speed
20	Time to First Key Action	Time until a user performs the core product action	Median time from signup to first key action	Under 5 minutes ideal	When reducing onboarding friction
21	Onboarding Completion Rate	Percentage of users who finish the onboarding flow	Users completing onboarding / Users starting onboarding x 100	40-60%	When evaluating onboarding design
22	Setup Completion Rate	Percentage of users who complete account setup	Users with complete setup / Total new users x 100	50-70%	For products requiring configuration
23	Free Trial Conversion Rate	Percentage of trial users who become paid	Paid conversions / Trial starts x 100	SaaS: 15-25% (opt-in); 50-60% (opt-out)	For freemium/trial models
24	Aha Moment Completion	Percentage reaching the moment of value realization	Users reaching aha moment / Total new users x 100	30-50%	When defining and optimizing the critical first experience
25	First Session Duration	Length of a user's first session	Median first session length	Mobile: 3-5 min; SaaS: 10-20 min	When assessing initial engagement quality
26	Feature Discovery Rate	Percentage of users who encounter a specific feature	Users who view feature / Total active users x 100	20-50% for core features	When evaluating feature visibility
27	Signup-to-Paid Conversion	Percentage of free signups that eventually pay	Paying users / Total signups x 100	Freemium: 2-5%; Trial: 15-25%	When measuring monetization of acquisition
28	Onboarding Drop-off Rate	Percentage of users who abandon onboarding at each step	Users dropping at step N / Users starting step N x 100	<20% per step is good	When identifying onboarding bottlenecks
29	Welcome Email Open Rate	Percentage of welcome emails opened	Opens / Emails sent x 100	50-60%	When optimizing email onboarding sequences
30	Product Qualified Lead (PQL) Rate	Percentage of users whose behavior signals purchase intent	PQLs / Total free users x 100	5-15%	For product-led growth companies

Key Takeaways for Activation

Activation is the most underinvested lever in most products. Improving activation by 25% can have a larger impact on revenue than the same improvement in acquisition.

Define your aha moment empirically: analyze what actions early users take that correlate with long-term retention, then optimize for that action. A/B testing is the most reliable way to validate these optimizations.

Time to value is critical. Every additional step in onboarding reduces completion by 10-20%.

Engagement Metrics

Engagement metrics measure how deeply and frequently users interact with your product after activation.

#	Metric	Definition	Formula	Benchmark Range	When to Use
31	Daily Active Users (DAU)	Unique users active in a single day	Count of unique users per day	Varies by product	For daily-use products (social, messaging)
32	Weekly Active Users (WAU)	Unique users active in a 7-day window	Count of unique users per week	Varies by product	For weekly-use products (project management)
33	Monthly Active Users (MAU)	Unique users active in a 30-day window	Count of unique users per month	Varies by product	Universal engagement baseline
34	DAU/MAU Ratio (Stickiness)	Proportion of monthly users who use the product daily	DAU / MAU x 100	SaaS: 10-20%; Social: 30-50%+	When measuring habitual use
35	DAU/WAU Ratio	Proportion of weekly users who use the product daily	DAU / WAU x 100	40-60% is strong	When measuring weekly engagement intensity
36	Session Duration	Average time spent per session	Total time in sessions / Number of sessions	SaaS: 5-15 min; Gaming: 20-40 min	When measuring depth of engagement
37	Sessions Per User	Average number of sessions per user per period	Total sessions / Active users	3-5 per week for B2B SaaS	When measuring frequency of use
38	Pages/Screens Per Session	Average number of pages viewed per session	Total page views / Total sessions	3-5 pages	When measuring exploration depth
39	Feature Adoption Rate	Percentage of users who use a specific feature	Users of feature / Total active users x 100	Core: 50%+; Secondary: 20-40%	When evaluating feature success
40	Feature Usage Frequency	How often a feature is used per user per period	Feature uses / Users of feature	Varies by feature	When measuring feature stickiness
41	Power User Percentage	Percentage of users who exceed a high-usage threshold	Power users / Total active users x 100	10-20%	When identifying your most valuable user segment
42	Core Action Frequency	How often users perform the product's primary action	Core actions / Active users per period	Daily for daily products	When tracking habitual engagement
43	Bounce Rate	Percentage of single-page visits	Single-page sessions / Total sessions x 100	25-40% for SaaS; 40-60% for blogs	When evaluating landing page effectiveness
44	Scroll Depth	How far down a page users scroll	Median scroll percentage	50-70% of page	When optimizing content layout
45	User Activity Score	Composite score of user engagement behaviors	Weighted sum of actions normalized to 0-100	Define per product	When creating engagement segments
46	Content Consumption Rate	Percentage of available content consumed	Content items consumed / Total available x 100	10-30%	For content-heavy products
47	Collaboration Rate	Percentage of users who interact with other users	Users who collaborate / Total active users x 100	30-50% for collaboration tools	For multi-user products
48	Notification Interaction Rate	Percentage of notifications acted upon	Notification actions / Notifications sent x 100	Push: 5-15%; In-app: 15-30%	When optimizing notification strategy
49	Search Usage Rate	Percentage of sessions that include a search	Sessions with search / Total sessions x 100	10-30%	When evaluating information architecture
50	API Call Volume	Number of API calls made by users/integrations	Sum of API calls per period	Varies by product	For platform/API products

Key Takeaways for Engagement

DAU/MAU ratio is the gold standard for stickiness. Facebook targets 50%+. Most B2B SaaS products range 10-20%.

Do not confuse time in app with value. Users spending 30 minutes may be struggling, not engaged. Pair session duration with task completion metrics.

Power users often represent 80% of value creation. Study their behavior to identify features and patterns you can encourage in other users.

Retention Metrics

Retention metrics measure whether users continue to find value over time. Retention is the single most important category for long-term product success.

#	Metric	Definition	Formula	Benchmark Range	When to Use
51	Day 1 Retention	Percentage of users who return the day after signup	Users active on Day 1 / Users who signed up x 100	Mobile: 25-40%; SaaS: 40-60%	When evaluating first-day experience
52	Day 7 Retention	Percentage of users active 7 days after signup	Users active on Day 7 / Cohort size x 100	Mobile: 10-20%; SaaS: 30-50%	When measuring early retention
53	Day 30 Retention	Percentage of users active 30 days after signup	Users active on Day 30 / Cohort size x 100	Mobile: 5-10%; SaaS: 20-35%	When measuring medium-term retention
54	Week-over-Week Retention	Percentage of users retained from one week to the next	Users active this week who were active last week / Last week active users x 100	60-80%	When tracking weekly retention trends
55	Monthly Retention Rate	Percentage of users retained month over month	Active users this month who were active last month / Last month active users x 100	SaaS: 80-95%	Primary SaaS retention metric
56	Cohort Retention Curve	Retention plotted over time for each signup cohort	Retention at period N for each cohort	Flattens above 20-30% for healthy products	When analyzing retention over the full lifecycle
57	Customer Churn Rate	Percentage of customers lost in a period	Customers lost / Customers at start of period x 100	SaaS: 3-7% annually; 0.5-1% monthly	Always; primary health metric
58	Revenue Churn Rate	Percentage of revenue lost from existing customers	Revenue lost / Starting MRR x 100	SaaS: <1% monthly is excellent	When measuring revenue impact of churn
59	Net Revenue Retention (NRR)	Revenue retained plus expansion from existing customers	(Starting MRR - Churn - Contraction + Expansion) / Starting MRR x 100	Best-in-class: 120-140%	The single most important SaaS metric
60	Gross Revenue Retention (GRR)	Revenue retained excluding expansion	(Starting MRR - Churn - Contraction) / Starting MRR x 100	>85% is good; >90% is excellent	When isolating retention from expansion
61	Logo Retention Rate	Percentage of customer accounts retained	(Customers at start - Churned) / Customers at start x 100	>90% annually for B2B SaaS	When tracking customer count health
62	Resurrection Rate	Percentage of churned users who return	Resurrected users / Churned users x 100	5-15%	When evaluating win-back campaigns
63	Contraction Rate	Percentage of revenue lost to downgrades	Downgrade revenue / Starting MRR x 100	<2% monthly	When monitoring plan downgrades
64	Expansion Rate	Percentage of revenue gained from upsells/cross-sells	Expansion revenue / Starting MRR x 100	>5% monthly for top SaaS	When measuring growth from existing customers
65	Time to Churn	Average duration before a customer churns	Median days from signup to churn	Varies widely	When identifying churn windows for intervention
66	Retention by Cohort	Retention segmented by signup date	Retention rate per cohort over time	Improving cohorts = product-market fit progress	When evaluating product improvements over time
67	Reactivation Rate	Percentage of dormant users who become active again	Reactivated users / Dormant users x 100	5-10%	When measuring re-engagement campaign success

Key Takeaways for Retention

Net Revenue Retention above 100% means you are growing even without acquiring new customers. This is the hallmark of elite SaaS companies.

Cohort analysis is non-negotiable. Aggregate retention numbers hide whether your product is improving or degrading over time.

Retention compounds. Improving retention by 5% can increase lifetime value by 25-95% (Bain & Company).

A customer health score aggregates multiple signals (usage, support tickets, NPS, payment history) into a single leading indicator that predicts churn before it happens.

Fix retention before scaling acquisition. Pouring users into a leaky bucket is expensive and demoralizing.

Revenue Metrics

Revenue metrics measure the financial performance and sustainability of your product.

#	Metric	Definition	Formula	Benchmark Range	When to Use
68	Monthly Recurring Revenue (MRR)	Predictable revenue earned each month	Sum of all active subscription revenue per month	Depends on stage	Always; foundational SaaS revenue metric
69	Annual Recurring Revenue (ARR)	Annualized recurring revenue	MRR x 12	Depends on stage	For annual planning and investor communication
70	Average Revenue Per User (ARPU)	Average revenue generated per active user	Total revenue / Active users	SaaS: $50-$500/mo for B2B	When comparing monetization across segments
71	Average Revenue Per Account (ARPA)	Average revenue per customer account	Total revenue / Number of accounts	Varies by market	When accounts have multiple users
72	Lifetime Value (LTV)	Total revenue expected from a customer over their lifetime	ARPU x Gross margin x (1 / Churn rate)	3-5x CAC minimum	When evaluating acquisition spend limits
73	LTV:CAC Ratio	Relationship between customer value and acquisition cost	LTV / CAC	3:1 to 5:1 ideal	When assessing unit economics sustainability
74	MRR Growth Rate	Month-over-month growth in MRR	(Current MRR - Previous MRR) / Previous MRR x 100	10-20% MoM early stage; 5-10% growth stage	When tracking revenue momentum
75	New MRR	Revenue from newly acquired customers	Sum of first-month revenue from new customers	Varies	When measuring acquisition revenue contribution
76	Expansion MRR	Additional revenue from existing customers (upsells, cross-sells)	Sum of revenue increases from existing customers	>30% of new MRR for best-in-class	When measuring growth from existing base
77	Churned MRR	Revenue lost from cancellations	Sum of revenue from churned customers	<2% of total MRR monthly	When quantifying revenue impact of churn
78	Quick Ratio (SaaS)	Ratio of revenue growth to revenue loss	(New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)	>4 is excellent; >2 is healthy	When assessing overall revenue health
79	Gross Margin	Revenue remaining after cost of goods sold	(Revenue - COGS) / Revenue x 100	SaaS: 70-85%	When evaluating profitability potential
80	Revenue Per Employee	Revenue efficiency metric	Total revenue / Number of employees	SaaS: $150K-$300K per employee	When benchmarking operational efficiency
81	Average Contract Value (ACV)	Average annualized value of a customer contract	Total contract value / Number of contracts	SMB: $5K-$25K; Enterprise: $50K-$500K+	When evaluating deal sizes
82	Average Selling Price (ASP)	Average price at which your product is sold	Total revenue from new deals / Number of new deals	Varies by segment	When tracking pricing trends
83	Monthly Burn Rate	Net cash spent per month	Monthly expenses - Monthly revenue	Depends on funding stage	For startups managing runway
84	Runway	Months of operation remaining at current burn	Cash on hand / Monthly burn rate	12-18 months minimum	When planning fundraising timing
85	Rule of 40	Combined growth rate and profit margin	Revenue growth rate + Profit margin	>40% is excellent	When benchmarking overall company health

Key Takeaways for Revenue

LTV:CAC ratio below 3:1 means you are spending too much to acquire customers, or your customers are not generating enough value. Both are existential issues.

SaaS Quick Ratio is one of the most underused metrics. It tells you whether you are adding revenue faster than you are losing it. A ratio below 2 means you are on a treadmill.

Expansion MRR is the secret weapon of the best SaaS companies. Companies like Twilio and Snowflake generate more than 130% net dollar retention because existing customers spend more over time.

Referral Metrics

Referral metrics measure how effectively your existing users drive new user acquisition through word of mouth and formal referral programs.

#	Metric	Definition	Formula	Benchmark Range	When to Use
86	Net Promoter Score (NPS)	Likelihood that customers recommend your product	% Promoters - % Detractors	SaaS: 30-50 is good; 50+ is excellent	When measuring overall customer satisfaction
87	Viral Coefficient (K-factor)	Number of new users each user generates	Invites per user x Invite conversion rate	>1.0 = viral; 0.3-0.7 typical	When evaluating organic growth potential
88	Referral Rate	Percentage of users who make a referral	Users who refer / Total active users x 100	2-5% for most products	When measuring referral program participation
89	Referral Conversion Rate	Percentage of referred users who sign up	Referred signups / Total referral clicks x 100	10-25%	When optimizing referral program effectiveness
90	Invites Sent Per User	Average referral invitations per active user	Total invites sent / Active users	1-3 for healthy programs	When measuring referral program reach
91	Customer Satisfaction (CSAT)	Satisfaction rating for a specific interaction	Sum of satisfied responses / Total responses x 100	75-85%	When measuring specific touchpoint quality
92	Customer Effort Score (CES)	Ease of completing a task or resolving an issue	Average score on 1-7 scale	>5.5 is good	When measuring support or UX friction
93	Review Rating	Average rating on third-party review sites	Average star/point rating	>4.0 out of 5	For B2B products (G2, Capterra)
94	Social Shares	Number of times your product/content is shared	Count of shares across platforms	Varies widely	When measuring organic brand amplification
95	Word of Mouth Coefficient	Percentage of new users acquired through WOM	WOM-attributed signups / Total signups x 100	20-40% for PLG companies	When measuring organic growth channels
96	Referral Revenue	Revenue generated from referred customers	Sum of revenue from referred customers	10-30% of total revenue	When calculating referral program ROI
97	Time to First Referral	Average time before a user makes their first referral	Median days from signup to first referral	30-90 days	When optimizing referral program timing

Key Takeaways for Referral

NPS is a lagging indicator. It tells you what happened, not what will happen. Pair it with behavioral data.

Viral coefficient above 1.0 is extremely rare and usually not sustainable. Do not chase virality; instead focus on making your product so valuable that users naturally recommend it.

Referred customers typically have 16% higher LTV and 37% higher retention than non-referred customers (Wharton School research).

Bonus: Operational and Product Health Metrics

#	Metric	Definition	Formula	Benchmark Range	When to Use
98	System Uptime	Percentage of time the product is available	Uptime minutes / Total minutes x 100	99.9% (three nines) minimum	Always; reliability baseline
99	Page Load Time	Time to fully render a page	Median load time in seconds	<2 seconds	When optimizing performance
100	Error Rate	Percentage of requests that result in errors	Error responses / Total requests x 100	<0.1%	When monitoring product reliability
101	Support Ticket Volume	Number of support tickets per period	Count of tickets per week/month	Varies	When measuring product usability
102	First Response Time	Time to first support response	Median time from ticket creation to first response	<1 hour for high-priority	When evaluating support quality
103	Time to Resolution	Average time to resolve support tickets	Median time from ticket creation to resolution	<24 hours for most issues	When measuring support effectiveness
104	Sprint Velocity	Amount of work completed per sprint	Story points completed per sprint	Stable velocity is the goal	When planning development capacity
105	Deployment Frequency	How often code is deployed to production	Deployments per day/week	Elite: multiple per day; High: weekly	When measuring engineering efficiency
106	Lead Time for Changes	Time from code commit to production deployment	Median time from commit to deploy	Elite: <1 hour; High: <1 week	When optimizing delivery pipeline
107	Mean Time to Recovery (MTTR)	Average time to recover from a failure	Total downtime / Number of incidents	<1 hour	When measuring operational resilience
108	Change Failure Rate	Percentage of deployments causing a failure	Failed deployments / Total deployments x 100	<15%	When measuring deployment reliability

Building Your Metrics Dashboard

Step 1: Choose Your North Star Metric

Select one metric that best captures the core value your product delivers. This becomes the top of your metrics hierarchy. See our complete guide to finding your North Star Metric for a step-by-step process with examples from Spotify, Airbnb, and Slack.

Step 2: Select Input Metrics

Identify 3-5 metrics that are leading indicators of your North Star. These are the metrics your teams can directly influence.

Step 3: Add Guardrail Metrics

Choose 2-3 metrics that ensure you are not creating negative side effects. For example, if your North Star is engagement, a guardrail might be customer satisfaction to ensure you are not increasing engagement through dark patterns.

Step 4: Define Counter Metrics

For each optimization effort, identify what could go wrong. If you are optimizing for speed, your counter metric might be error rate.

Step 5: Set Review Cadence

Daily: Operational metrics (uptime, error rate, active users)

Weekly: Engagement and activation metrics

Monthly: Retention, revenue, and growth metrics

Quarterly: Strategic metrics (NPS, LTV, market share)

Common Mistakes When Working with Metrics

Vanity metrics obsession. Total signups, page views, and app downloads look impressive but do not indicate product health. Always prefer metrics that measure value delivery.

Too many metrics. Tracking 50 metrics means you are tracking none effectively. Focus on 5-8 primary metrics at any given time.

Ignoring segmentation. Aggregate metrics hide crucial differences between user segments, plans, geographies, and cohorts.

Confusing correlation with causation. A metric moving after a feature launch does not prove the feature caused the movement. Use controlled experiments.

Setting and forgetting. Metrics should evolve as your product and market mature. Review your metrics framework quarterly.

Measuring outputs instead of outcomes. Features shipped is not a product metric. Impact delivered is.

Not defining metrics precisely. "Active user" means different things to different teams. Document exact definitions and ensure alignment.

Ignoring leading indicators. Revenue churn is a lagging indicator. By the time you see it, the damage is done. Track leading indicators like engagement drops and support ticket increases --- or build a customer health score to aggregate these signals into an early warning system.

Tools and Resources

Analytics Platforms

Amplitude --- Product analytics with cohort analysis and behavioral insights

Mixpanel --- Event-based analytics with powerful segmentation

Heap --- Auto-capture analytics that retroactively analyzes user behavior

PostHog --- Open-source product analytics suite

Google Analytics --- Web traffic and basic product analytics

Business Intelligence

Looker --- Data modeling and visualization

Metabase --- Open-source BI tool

Tableau --- Enterprise-grade data visualization

Mode --- SQL-based analytics

Subscription and Revenue Analytics

ChartMogul --- Subscription analytics and revenue recognition

ProfitWell --- Free subscription metrics and benchmarking

Baremetrics --- Real-time SaaS metrics dashboard

Customer Feedback

Delighted --- NPS and CSAT surveys

Hotjar --- Heatmaps, session recordings, and feedback

UserTesting --- Qualitative user research

Final Thoughts

Metrics are only as valuable as the decisions they inform. The goal is not to track everything --- it is to track the right things, understand what they are telling you, and act on those insights. Start with the metrics most relevant to your current stage and priorities, define them precisely, and build a culture where data informs every product decision.

Return to this cheat sheet whenever you need to evaluate a new metric, set up a dashboard, or ensure you are measuring what matters. The best product teams are not the ones with the most data --- they are the ones who ask the best questions and measure the answers.

The Ultimate Product Metrics Cheat Sheet: 100+ Metrics Every PM Should Know