Go-to-Market · core
Product Analytics
Activation, retention, funnels — measuring whether the product works.
Mental model
Good analytics looks like a small tree: one north-star metric that captures the value users get, two or three input metrics that drive it, and one guardrail you must not regress on. If a PM cannot make a tradeoff from this tree alone, the tree is wrong.
How to study Product Analytics
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use PostHog — Product analytics docs to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Product Analytics with Landing Pages. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Define a north-star metric and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Define a north-star metric
Pick a product. Define its north-star metric (one number that captures customer value), 2-3 input metrics that drive it, and a guardrail metric you must not regress.
Expected evidence: A tree: north-star → inputs → guardrail, each with a one-sentence rationale.
Open the interactive drill →Review prompts
- What makes a metric a guardrail rather than an input metric, and why must the tree have one?
Build evidence
Synthesize: Application Engineering
Turn backend, client, UX, real-time, interactive, analytics, and distribution skills into one complete product. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks
Prerequisites
None assigned yet.