Product Marketing Analytics: Growth and Retention with Data

Provide clarity and purpose for the product team, drive data-driven strategy, and fuel a continuous feedback loop between data, decisions, and users to boost growth and retention.

Explore how Spotify defines a north star metric—time spent listening—and uses supporting metrics like songs per day, month-on-month retention, and playlist additions to optimize recommendations.

Develop a metrics framework aligned to the casino mission, linking results to strategy, and define a north star metric 'user engagement score' with metrics like dead spins and hit rate.

Analyze how session length with auto spin on and off reveals different play styles—engaged, unengaged, and bots—and drive cohort-based decisions, hypotheses, and experiments for engagement and retention.

Explore end-to-end product analytics and how cross-functional alignment across marketing, product, and sales reveals the full user journey, from onboarding and acquisition to retention and monetization.

Apply the pirates funnel framework to measure acquisition, activation, retention, revenue, and referral, estimating lifetime value and customer acquisition cost to drive growth.

Analyze a registration flow to identify metrics at each onboarding step and explain why, applying the pirate metric framework to inform product growth and retention.

Learn how to measure onboarding and the user journey with metrics at each step—from landing page visits to sign up, KYC, and deposits—aligned to the pirate metric funnel.

Explore DAU and MAU to measure traffic, view time series charts, and analyze daily and monthly usage.

Learn how the DAU/MAU ratio measures product stickiness by comparing daily and monthly active users, revealing usage patterns, campaign impacts, and month-on-month retention signals.

Explore how onboarding forms and app funnels create drop-offs from installs to launches to signups, and learn to measure conversion rates to identify optimization opportunities.

Measure activation as a signal of product value using day zero activation rate and the first spin, to forecast retention and monetization.

Explore how time to first activation measures how quickly users experience core value, using a slot game example to compare mean and median and set activation benchmarks.

Accelerate the aha moment by optimizing onboarding to reduce signup friction, offering quick demos, and clarifying product value to boost early engagement and retention.

Learn to calculate arpu by selecting a consistent time period, accounting for paying users and weekly versus monthly subscriptions, and dividing total revenue by total users for accurate metrics.

Analyze ARPPU and ARPU calculations to measure revenue per paying user and per user, guiding campaigns, pricing, and product features that boost free-to-paid conversions and retention.

Explore ARPDAU, the average daily revenue per user, and learn to track it across days to spot trends and optimize monetization and product strategies.

Explore lifetime value and customer acquisition cost to assess long-term profitability, optimize referral-driven growth, and balance marketing spend for sustainable ROI.

Explore the difference between rpu and ltv, showing how rpu is a short-term, fixed-period lagging metric while ltv projects long-term revenue and accounts for churn and retention.

Explore measuring user retention by comparing initial signups to returning users across days and months (D0, D1, M0, M1) and interpret campaign versus organic effects on drop-off and loyalty.

Visualize retention through monthly curves and extend the funnel by showing activation-to-retention links; identify product market fit when retention plateaus after initial drop-off with a loyal core.

Analyze event-based retention to reveal why players return by tracking actions like spins, deposits, bonuses, and pay-table views. Use these patterns to guide product strategy and roadmap for growth.

Discover how product analytics uses revenue retention to shape slot-game design by analyzing bonus triggers, returning users, betting behavior, and the monetary value of retained revenue.

Learn how reactivation fuels retention by identifying churn drivers, enticing return with rewards, personalized notifications, and new content to re-engage users.

Prioritize product value and quality to improve retention, balancing acquisition with long-term user experience; Duolingo's streaks and leveling systems illustrate building habits that sustain growth.

Explore monetization and pricing starting with the free model, and learn to measure engagement, activation, DAU/MAU, and contributions in open source and community-driven products.

Explore one time payments and subscriptions as monetization models, compare revenue peaks, risk, and investor considerations, and learn how pricing, market validation, and frequent releases shape long-term growth.

Explore the freemium pricing model, blending free access with paid tiers, in-app purchases, and ads to monetize product value while tailoring plans for different user segments.

Explore usage-based monetization and pay-as-you-go pricing across apps, gyms, and AI tools, highlighting fairness, credits, subscriptions, freemium blends, and pricing decisions based on data and tests.

Explore an A/B pricing test to identify the optimal price in Canada by comparing 7.50, 6, and 9, and assess conversion, churn, arpu, and ltv for balanced adoption and monetization.

Explore product channel fit by analyzing how App Store, Google Play, direct sales, ads, and partnerships reach users and influence revenue and retention.

Analyze channel data from casinos and marketing channels like Instagram, referrals, and content SEO to compare ARPU, retention, ROI, and LTV, guiding product and marketing to optimize channel fit.

Track channel-based metrics to optimize growth: revenue per channel, ARPU, retention, churn, and CAC. Drill down on demographics and conversion rates to optimize ROI and LTV with partner collaboration.

Analyze feature usage data to understand adoption and revenue across game modes, and combine this with qualitative insights, sentiment, and device context to guide product iterations.

Explore feature pairing by analyzing how main game usage and bonus buys interact, using cohorts, session length, and seven-day retention to guide product decisions.

Apply sentiment analysis to interpret user feedback with NLP, tagging content as positive, neutral, or negative, and surface actionable product insights for data-driven growth and retention.

Use tools such as FullStory, Hotjar, and Google Analytics to detect rage clicks, dead clicks, and drop-offs, then translate granular events into heatmaps and prioritized UX improvements.

Explore emotion detection, analyzing facial expressions, body language, and voice tone to inform real-time content recommendations and improve engagement, with motion and location analysis raising privacy and ethics considerations.

Explore how device type, operating system, and screen resolution influence user experience. Identify mobile dominance, desktop engagement, and specific resolutions with zero engagement to guide user interface adjustments.

Apply the course lessons to day-to-day product management and entrepreneurship, and invite feedback, ratings, and ideas for future topics.