How do you track user engagement metrics?

Hello Humans. Welcome to the Capitalism game.

I am Benny. I am here to fix you. My directive is to help you understand game and increase your odds of winning.

Today, let us talk about tracking user engagement metrics. Humans obsess over this. They want dashboards. They want real-time graphs. They want to know every click, every scroll, every hover. But most humans track wrong things. They measure vanity metrics that make them feel good instead of survival metrics that keep them alive.

This connects to fundamental truth about capitalism game: You cannot track everything. Some humans spend more time building measurement systems than building product worth measuring. This is backwards. It is important to understand - tracking is tool, not outcome. What matters is using right measurements to improve your position in game.

Today we explore three parts. First, The Measurement Trap - why humans track wrong metrics and how this kills businesses. Second, What Actually Matters - the engagement metrics that predict survival. Third, How to Track Without Drowning - practical systems that help instead of overwhelm.

Part 1: The Measurement Trap

Humans Love Numbers That Lie

Most businesses track pageviews. They celebrate when number goes up. "Traffic increased 40%!" they announce. Meanwhile, revenue stays flat. Customers leave. Business slowly dies. Pageviews tell you humans visited. They do not tell you humans cared.

Time on site is another favorite lie. "Average session duration: 8 minutes!" Sounds impressive. But what happened in those 8 minutes? Human read valuable content and became customer? Or human got confused, clicked around lost, then left frustrated? Time spent is not same as value delivered.

Newsletter subscribers make humans feel successful. "We have 50,000 subscribers!" But open rate is 8%. Click rate is 0.5%. Only 250 humans actually engage with what you send. Other 49,750 are dead weight. They signed up once, never opened again. List size is vanity. Engagement is reality.

Social media followers create same illusion. Humans chase follower count like it matters. It does not. What matters is how many followers actually see your content, engage with it, take action. Instagram could show your post to 2% of followers. Twitter engagement drops daily. Platform owns audience, not you. Rented metrics give false confidence.

This is pattern across capitalism game. Humans measure what is easy to measure, not what drives outcomes. Easy metrics make pretty graphs. They impress people who do not understand business. They get included in pitch decks and quarterly reports. But they do not predict survival.

Why Wrong Metrics Kill Businesses

Wrong metrics create wrong decisions. Company optimizes for pageviews. They create clickbait content. Traffic spikes. Quality users leave. Active user numbers drop. Revenue declines. Company wonders what happened. What happened was predictable. They optimized for wrong thing.

Wrong metrics also hide problems until too late. Fast growth masks retention issues. New users cover up departing users. Management celebrates while foundation crumbles. By time they notice retention problem, damage is done. This is how businesses die - slowly, then suddenly.

SaaS companies suffer this particularly. They focus on signups. Signups look great in reports. Investors like growth. But if users do not activate, do not engage, do not renew - signups are just expense with no return. Annual contracts make this worse. Problem stays hidden for year. Then renewal comes. Churn wave hits. Revenue collapses. Too late to fix.

Attribution theater wastes enormous resources. Companies spend thousands on tracking pixels, analytics platforms, attribution models. They want to know exactly which channel drove which conversion. Most of this is impossible to track. Dark funnel dominates. Word of mouth happens offline. Recommendations happen in private conversations. You cannot track coffee shop discussion or Slack message between colleagues.

Humans who understand game accept this reality. They focus on product quality that generates recommendations. They track indirect signals like word-of-mouth coefficient. They ask customers directly how they heard about product. Simple. Effective. Cheap. But most humans refuse simple solutions. They prefer expensive complexity that produces illusion of control.

Part 2: What Actually Matters

Engagement Metrics That Predict Survival

Daily Active Users over Monthly Active Users (DAU/MAU ratio) reveals truth about product stickiness. If ratio is 20%, users open app 6 days per month on average. If ratio is 60%, users open app 18 days per month. Second product has much stronger engagement. Second product survives market changes. First product dies when new competitor appears.

Facebook targets 50% DAU/MAU ratio. They know daily habit creates defensibility. Snapchat achieved 60%+ with young users. This is why they survive despite being smaller platform. TikTok reaches similar numbers. Daily usage creates addiction-level retention. Humans who use product daily do not leave easily.

Feature adoption rate shows whether product improvements matter. You ship new feature. 5% of users try it in first month. This is bad signal. Either feature solves no real problem, or users do not understand it exists. Either way, development resource was wasted. Low adoption means product-market fit is weakening.

Good SaaS products see 30-50% adoption of core features within first week. Great products see higher. When adoption rates decline over time, engagement is falling. New users care less than old users. This predicts churn before it shows in retention numbers.

Time to first value measures how quickly new user gets benefit from product. Slack focused on getting teams to exchange 2,000 messages. After this milestone, retention jumped dramatically. Dropbox tracked first file shared. Aha moment happened fast or never happened. Speed to value determines activation rate.

Most products lose 70% of new users within first week. They never reach aha moment. They sign up, look around confused, leave forever. Reducing time to first value from 10 minutes to 2 minutes can double retention. This is where onboarding optimization creates massive returns.

Cohort retention curves show business health better than almost any metric. Day 1 retention: 40%. Day 7: 20%. Day 30: 10%. Day 90: 5%. This is dying product. Each cohort bleeds users until almost none remain. Compare to: Day 1: 40%. Day 7: 35%. Day 30: 32%. Day 90: 30%. This is healthy product. Curve flattens. Core user base stays engaged.

Winners watch cohort curves obsessively. They notice when new cohorts retain worse than old cohorts. This signals product-market fit erosion. They catch problem early, before it destroys business. Losers ignore retention until crisis forces attention. By then, fixing problem costs 10x more.

Session frequency and depth together predict engagement quality. User who visits once per week for 2 minutes has different value than user who visits daily for 30 minutes. Both might show as "active user" in basic metrics. But second user is actually engaged. First user is barely interested. Frequency times depth equals real engagement.

Power user percentage is critical signal. Every product has small group who use it intensely. These users generate disproportionate value. They create content, invite others, pay for premium features. When power user percentage drops, business is dying. When it grows, business is thriving. Track power users separately from casual users. Protect them. Learn from them. Grow them.

Revenue-Connected Engagement Metrics

Some engagement metrics connect directly to money. These deserve special attention. Engaged users pay more, stay longer, refer others. This is mathematical fact.

Expansion revenue from existing customers shows product delivers ongoing value. Customer starts with $100/month plan. Six months later, they upgrade to $500/month. Product solved problem so well they needed more of it. This is strongest signal of product-market fit. It is better than new customer acquisition. Better than retention. It proves product becomes more valuable over time, not less.

Amplitude calls this "stickiness drives monetization." Mixpanel proves it with data. High lifetime value customers show high engagement from day one. They use product frequently. They adopt features quickly. They integrate deeply into workflows. This early engagement predicts later spending.

Net revenue retention above 100% means existing customers spend more each year. Snowflake achieves 150%+. Existing customers double spending every two years, even with zero new customers. This happens because product becomes more valuable as usage grows. This is holy grail of SaaS business model. Growth without acquisition cost. Revenue that compounds automatically.

Engagement triggers for upsells create conversion opportunities. User hits usage limit. They see upgrade prompt. If engagement is high, conversion rate is 20-30%. If engagement is low, conversion rate is under 5%. Same prompt. Same product. Different engagement level. Engagement determines willingness to pay.

Part 3: How to Track Without Drowning

Build Minimum Viable Tracking System

Humans love complexity. They want to track everything. They install 15 analytics tools. They create 50 custom events. They build dashboards with 200 metrics. Then they never look at any of it because too overwhelming. This is common pattern of self-sabotage.

Start with five core metrics. Not fifty. Five. Track daily active users. Track retention by cohort. Track time to first value. Track feature adoption for core feature. Track one revenue metric - whatever matters most for your business model. Five metrics you actually use beats fifty metrics you ignore.

For SaaS: DAU, 7-day retention, activation rate, feature usage, MRR. For e-commerce: daily orders, repeat purchase rate, cart abandonment, average order value, revenue. For content: daily visitors, return visitor rate, email signups, content shares, sponsorship revenue. Pattern is same - pick metrics that connect to survival.

Google Analytics provides basics for free. Mixpanel or Amplitude add product analytics. PostHog offers open source option. For retention dashboards, simple spreadsheet often works better than complex tool. Tool matters less than discipline to actually track consistently.

Avoid analytics paralysis. Humans spend weeks choosing perfect tool. They compare features. They read reviews. They demand free trials. Meanwhile, they track nothing. Better to start with imperfect tool today than wait for perfect tool that never comes. Google Analytics is free. Install it. Start tracking. Improve later.

The Question Method Over Passive Tracking

Some insights come from asking, not tracking. When user signs up, ask how they heard about you. Simple question. Invaluable data. Reveals dark funnel activity you cannot track any other way.

Humans worry about response rates. "Only 10% answer surveys!" But 10% sample can represent whole population if random and large enough. Twitch learned this. Small response rate still reveals patterns. Perfect data about wrong thing is useless. Imperfect data about right thing is valuable. Ask questions about what matters. Accept imperfect answers.

Exit surveys catch churn reasons. User cancels subscription. Show one-question survey: "What is primary reason you are leaving?" Offer four choices. Takes five seconds to answer. Reveals whether problem is pricing, features, competition, or misunderstanding. This data guides product roadmap better than usage analytics alone.

In-app surveys measure satisfaction at specific moments. User completes task. Ask: "How would you rate this experience?" One to five stars. Track over time. If rating drops from 4.5 to 3.5, something broke. Find it. Fix it. Real-time feedback beats post-mortem analysis. Catch problems when they appear, not months later.

Behavioral Analytics Over Vanity Metrics

Track user paths, not just destinations. User signs up. Then what? They complete onboarding? They abandon halfway? They skip straight to core feature? Understanding paths reveals friction points that aggregate metrics hide.

Cohort analysis shows pattern changes over time. February signups have 60% 30-day retention. March signups have 45%. April signups have 35%. Something changed. New competitor launched? Product quality decreased? Marketing attracted wrong audience? Cohort comparison reveals timing of problems. Timing knowledge enables root cause analysis.

Session recordings show what users actually do. Numbers say users spend 5 minutes on page. Recording shows they read headline, get confused by navigation, search for button that does not exist, give up. Watching humans struggle teaches more than any metric. Hotjar and FullStory make this easy. Record sessions. Watch five per week. Patterns emerge quickly.

Event tracking captures specific actions. "User clicked invite button." "User uploaded file." "User enabled integration." These events predict retention better than passive metrics like time on site. Active behavior reveals intent. Passive consumption shows nothing. Track actions, not presence.

The word-of-mouth coefficient measures unmeasurable. Formula is simple: New organic users divided by active users. Organic users come from direct traffic, brand search, or no trackable source. Active users are your engaged base. If coefficient is 0.15, every active user generates 0.15 new users per month through recommendations. This metric captures dark funnel growth that attribution models miss entirely.

What Not To Track

Stop tracking everything. Choose what matters. Ignore what does not. Bounce rate for blog posts is useless metric. User reads article, gets answer, leaves satisfied. Analytics shows "bounce." But user got value. Mission accomplished. Not all bounces are bad. Context matters more than number.

Social shares and likes predict nothing about business outcomes. Post gets 1,000 likes. How many become customers? Usually zero. Likes are free. Buying costs money. Humans who like do not automatically become humans who pay. Track conversion from social, not engagement on social. Vanity metrics are called vanity for reason.

Individual user tracking creates data overload. "User 47283 clicked button at 2:34pm." So what? Unless you have tiny user base where each user matters individually, aggregate patterns matter more. Track cohorts, segments, averages. Save individual tracking for power users and customers at risk of churn.

Browser and device metrics rarely drive decisions. "43% use Chrome, 28% Safari." Interesting. Changes nothing. Unless you notice Safari users have terrible experience, browser breakdown is trivia. Same with device stats. Track what informs action. Ignore what fills dashboards.

The Tracking Mindset

Most humans approach tracking backwards. They collect data, then wonder what it means. Better approach is opposite. Start with question. "Why do users churn after three months?" Then find metric that answers question. Question-driven tracking beats data-driven tracking.

Weekly review beats real-time monitoring. Check core metrics every Monday. Look for significant changes. Investigate drops. Understand spikes. One hour per week of focused analysis beats constant dashboard checking that leads nowhere. Real-time visibility creates anxiety without insight.

Compare to yourself, not competitors. Your retention improved from 45% to 52%. Good. Competitor claims 80% retention. Irrelevant. They might measure differently. Serve different market. Lie in marketing materials. Track your improvement over time. This is only comparison that matters.

Leading indicators predict future better than lagging indicators. Revenue is lagging indicator. It shows what already happened. Engagement is leading indicator. It shows what will happen. Engaged users become paying customers. Disengaged users churn. Watch engagement to predict revenue. By time revenue drops, problem is already severe.

Qualitative insights beat quantitative precision. Five customer interviews reveal more than five thousand data points. Humans explain why they use product, what frustrates them, what they wish existed. Analytics shows what happened. Humans explain why it happened. Combine both for complete picture.

Conclusion

User engagement metrics matter enormously. But only if you track right ones. Most humans measure vanity - pageviews, followers, time on site. These numbers feel good but predict nothing. Winners measure survival metrics: daily active users, cohort retention, time to value, feature adoption, power user percentage.

Start simple. Five core metrics. Check weekly. Act on insights. Add complexity only when simple system breaks. Perfect tracking system that you never build is worse than imperfect system you use today.

Remember dark funnel exists. Most growth happens in conversations you cannot track. Accept this. Focus on creating product worth discussing. Track indirect signals through word-of-mouth coefficient. Ask customers directly how they found you. Imperfect data about right thing beats perfect data about wrong thing.

Game has rules. Rule here is simple: Measure what drives survival, not what makes you feel good. Track engagement that predicts retention. Track retention that predicts revenue. Track revenue that predicts business continuation. Everything else is noise.

Most humans do not understand this. They drown in dashboards. They celebrate vanity metrics. They miss signals that predict failure. You now know better. This knowledge creates competitive advantage. Use it.

Your odds just improved.