product-analytics

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Product Analytics

Product analytics is the discipline of measuring how users interact with a product, understanding which behaviors drive value, and making decisions grounded in data rather than intuition. The goal is not to collect every number possible but to instrument the right behaviors, define metrics that map to business outcomes, and maintain the rigor to act on findings correctly.

When to use this skill

Trigger this skill when the user:

• Needs to define or audit a metrics framework for a product
• Wants to build or analyze a conversion funnel
• Asks about cohort analysis, retention curves, or churn investigation
• Needs to measure feature adoption after a launch
• Wants to design an event taxonomy or instrumentation plan
• Is analyzing A/B test results or interpreting statistical significance
• Asks about north star metrics, input metrics, or AARRR framework
• Needs to build a product dashboard or choose which metrics to show by audience

Do NOT trigger this skill for:

• Pure data engineering tasks such as pipeline architecture or warehouse schema design

(those are infrastructure concerns, not product analytics methodology)

• Business intelligence reporting where the goal is financial or operational reporting,

not product behavior analysis

Key principles

1. Instrument before you need data - Tracking is a prerequisite, not an afterthought.

Add instrumentation when a feature ships, not when a stakeholder asks "do we track that?" Retrofitting events means losing the baseline period and the ability to compare pre/post.

1. Define metrics before building features - Before writing a line of code, agree on

what success looks like and how it will be measured. A feature without a success metric cannot be evaluated and cannot be killed. Write the metric definition into the spec.

1. Segment everything - Aggregate numbers hide the truth. Always break down metrics

by user segment (new vs. returning, plan tier, acquisition channel, geography) before drawing conclusions. An overall retention rate that looks healthy can mask a collapsing new-user cohort.

1. Retention is the ultimate metric - Acquisition and activation are table stakes.

Retention - whether users come back and get repeated value - is the only signal that proves product-market fit. A product with strong retention can fix acquisition; a product with broken retention cannot be saved by growth spend.

1. Correlation requires investigation, not celebration - Two metrics moving together

is a hypothesis, not a conclusion. Before attributing causation, check for confounders, test the relationship with a controlled experiment, and document the evidence. Acting on spurious correlations wastes engineering capacity and can harm users.

Core concepts

Event taxonomy

Events are the atoms of product analytics. An event represents a discrete user action (or system action) at a point in time. A well-designed taxonomy makes querying intuitive and avoids the "event graveyard" where hundreds of poorly named events accumulate with no documentation.

Naming convention: object_action in snake_case. The object is the thing being acted on; the action is what happened.

user_signed_up
dashboard_viewed
report_exported
onboarding_step_completed
subscription_upgraded

Every event should carry a consistent set of properties:

• user_id - anonymous or authenticated identifier
• session_id - groups events within a single session
• timestamp - ISO 8601, always UTC
• platform - web, ios, android, api
• event_version - allows schema evolution without breaking queries

Entity-specific properties are added per event:

report_exported:
  report_type: "weekly_summary"
  format: "csv"
  row_count: 1450

Funnel analysis

A funnel is an ordered sequence of steps a user must complete to reach a goal. Funnel analysis reveals where users drop off and quantifies the conversion loss at each step.

Key measurements:

• Step conversion rate - users who completed step N+1 / users who completed step N
• Overall conversion rate - users who completed the final step / users who entered step 1
• Time-to-convert - median and 90th percentile time between first and last step
• Drop-off point - the step with the steepest conversion decline

Funnels should be analyzed with a defined window (e.g., within 7 days, within a single session) to avoid counting users who convert months later by coincidence.

Common funnels by product type:

| Product type | Acquisition funnel | Activation funnel | |---|---|---| | SaaS | Landing page -> Sign up -> Verify email -> First login | Login -> Create first item -> Invite team member | | E-commerce | Product page -> Add to cart -> Checkout start -> Purchase | First purchase -> Second purchase within 30 days | | Marketplace | Search -> Listing view -> Contact/Bid -> Transaction | First transaction -> Second transaction |

Cohort analysis

A cohort is a group of users who share a defining characteristic at a specific point in time - most commonly the week or month they first signed up. Cohort analysis tracks how that group's behavior evolves over time.

Retention cohort table:

         Week 0  Week 1  Week 2  Week 3  Week 4
Jan W1:  100%    42%     31%     27%     25%
Jan W2:  100%    38%     29%     26%     24%
Jan W3:  100%    45%     34%     30%     28%

Reading across a row shows how a specific cohort retains over time. Reading down a column shows whether a given time-since-signup period is improving or degrading across cohorts. Improvement down a column - newer cohorts retaining better than older ones - is the strongest early signal that product improvements are working.

Behavioral cohorts group users by an action rather than signup date (e.g., users who completed onboarding vs. those who skipped it). Comparing behavioral cohorts quantifies the impact of a specific behavior on downstream retention.

Retention curves

A retention curve plots the percentage of a cohort that remains active over successive time periods. The shape of the curve matters as much as the final number.

Curve shapes:

• Flat decay to zero - all users eventually churn; the product has no habit-forming

loop. Fundamental product problem.

• Decaying to a stable floor - some users churn, but a core group stays. The floor

percentage is the product's "true retention." The goal is to raise the floor.

• Smile curve (recovery) - users churn, then some return. Common in seasonal or

lifecycle products. Worth understanding the re-activation trigger.

D1 / D7 / D30 benchmarks by category (mobile apps):

| Category | D1 | D7 | D30 | |---|---|---|---| | Social / Messaging | 40%+ | 20%+ | 10%+ | | Utilities | 25%+ | 10%+ | 5%+ | | Games | 35%+ | 15%+ | 7%+ | | Productivity (SaaS) | 60%+ | 40%+ | 25%+ |

Metric hierarchy

A healthy metrics framework has three tiers. Conflating them creates confusion about what the team is optimizing for.

North star metric - The single number that best captures the value delivered to users and predicts long-term business success. It is a lagging indicator that changes slowly. Examples: weekly active users completing a core action, number of projects with 3+ collaborators, monthly transactions processed.

Rules for a good north star:

1. It measures delivered value, not activity (DAUs alone is not a north star)
2. One team cannot game it without genuinely helping users
3. It is understandable by every person in the company
4. It moves on a relevant timescale (not too fast to be noisy, not too slow to provide signal)

Input metrics (leading indicators) - The behaviors that causally drive the north star. These are actionable by product and engineering teams within a quarter. Examples: new user activation rate, core action complet