# Product Analytics

> Product Analytics

- Skill: `skillmedev/product-analytics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add skillmedev/product-analytics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/skillmedev/product-analytics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: SkillMedev (https://skillmd.com/u/skillmedev)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/skillmedev/product-analytics

---


# Product Analytics

You cannot improve what you do not measure, but most teams measure the wrong
things badly. A messy event taxonomy is technical debt that misleads every
decision built on it. This skill sets up analytics that actually inform product
work.

## Start With Questions, Not Events

Before instrumenting anything, write the decisions analytics must inform:

- Where do users drop off before first value?
- Which features correlate with retention?
- What does an activated user do that a churned one did not?

Instrument backward from these questions. Tracking everything "just in case"
produces noise nobody queries.

## Event Taxonomy

A disciplined naming convention is the foundation. Get it wrong and you spend a
year cleaning data.

- Use a consistent structure: object-action (e.g. `project_created`,
  `invite_sent`). Pick one convention and enforce it.
- Define properties deliberately: each event carries the context you will
  segment by (plan, source, role).
- Maintain a tracking plan - a single source of truth listing every event, its
  properties, and its owner. This is the contract between product, eng, and
  data.
- Size it: an early-stage tracking plan should be roughly 20-50 events, not
  hundreds. If the plan exceeds ~100 events before product-market fit, it is
  tracking implementation details, not decisions.
- Govern changes: new events go through review so the taxonomy does not rot.

## The North Star and Inputs

- Pick one north-star metric that captures delivered value (e.g. weekly active
  teams, queries run, documents shipped) - not a vanity count.
- Decompose it into input metrics you can actually move (acquisition,
  activation, engagement, retention).

## Core Analyses

### Funnels
Map the path to first value and measure conversion at each step. The biggest
drop-off is your highest-leverage fix. Watch time-to-convert, not just rate:
for a self-serve product, most activation that will ever happen happens in the
first session or first day - a median time-to-first-value beyond a day is
itself the finding.

### Cohort Retention
Group users by signup period and track retention over time. A healthy product
shows a retention curve that flattens (a "smile" for the best products); a
curve decaying to zero means no product-market fit yet.

How to read the curve:

- Judge where it flattens, not the month-1 number - the asymptote is the
  product's real retained base.
- Consumer apps: roughly 40% day-1 / 20% day-7 / 10% day-30 is a good curve;
  well under that (say ~25/10/5) means the leak comes before the habit forms.
  A consumer curve flattening above ~20% is a strong signal.
- B2B SaaS: retention is measured in weeks/months, not days; expect the curve
  to flatten within the first 4-8 weeks, and monthly logo retention below ~95%
  (churn above ~5%/month) is a red flag for anything sold to businesses.
- Compare newer cohorts against older ones - cohorts flattening higher over
  time is the clearest evidence the product is improving.

### Activation
Define the activation event - the action that predicts long-term retention
(the "aha moment"). Find it by comparing what retained users did early versus
churned users: take users still active at week 4+, contrast their first 1-7
days of behavior against churned users' first 1-7 days, and look for the
early action with the biggest retention gap. Then optimize the funnel to it.

### Feature Adoption
Track breadth (how many use a feature) and depth (how often). Correlate
adoption with retention to prioritize the roadmap.

## Avoid Vanity Metrics

- Total signups, total pageviews, cumulative anything - they only go up and
  inform nothing.
- Prefer rates and cohorts over totals; prefer leading indicators over lagging
  ones.

## Tooling and Hygiene

- Choose one analytics tool as the source of truth; pipe to a warehouse for
  deep analysis.
- Validate instrumentation regularly - silent tracking bugs corrupt every
  downstream decision.
- Document metric definitions so "active user" means the same thing to everyone.

## Quality bar

- Every event in the tracking plan traces back to a named decision question;
  none exist "just in case".
- The taxonomy follows one object-action convention with zero exceptions, and
  the plan lists properties and an owner per event.
- The north star measures delivered value (a rate or active count, never
  cumulative) and decomposes into movable input metrics.
- The retention read states where the curve flattens and against which
  benchmark, not just a month-1 percentage.
- The activation event is backed by a retained-vs-churned comparison, not
  picked by intuition.

## Deliverable

Produce an analytics plan: the decision questions, a north-star metric with
input-metric tree, an event tracking plan with naming convention, and the
priority analyses (activation funnel, cohort retention) to build first.