Mixpanel Product Analytics Certification study plan

Define the event taxonomy for a fictional e-commerce app, naming at least 12 events with consistent object-action casing

Distinguish between tracked events and user profile properties, and write down three properties you would set once versus three you would update per session

Explain the difference between an event total, a unique-user count, and a property-level breakdown, and give one product question that each answers

Sketch a funnel for signup-to-first-purchase and identify which step you expect to have the largest drop-off and why

Build cohort definitions by signup date and by a behavioral trigger, and describe when each cohort type is more useful

Interpret a retention curve and identify the point where it flattens, explaining what a non-flattening curve implies about product-market fit

Compare segmentation by acquisition channel versus by plan tier for the same metric and note how the conclusion changes

Write three hypotheses that a product team could test using only event data already in the tracking plan

Map a web session lifecycle from page view through conversion, listing the events that fire at each stage

Explain how UTM parameters populate acquisition properties and design a naming convention that avoids channel fragmentation

Diagnose three common tracking failures: duplicate page views from SPA routing, missing properties on form submits, and bot traffic inflating totals

Describe how to attribute a conversion to a first touch versus a last touch and state which the product team should use for a given question

Compare web and mobile event schemas for the same product and list where property names must align for cross-platform reporting

Explain how mobile app lifecycle events (install, update, background, foreground) affect session counting

Describe how device and OS properties segment behavior differently than web browser properties do

Identify what breaks in attribution when a user installs the app after first visiting on web

Design a mobile onboarding funnel with platform-specific steps and predict where iOS and Android diverge

Explain how push notification events should be tracked and what property distinguishes delivery from engagement

Describe the tradeoffs of tracking offline actions and reconciling them when connectivity returns

Draft three mobile-specific metrics (for example crash-adjacent drop-off or permission grant rate) and define their calculation

Write a testable hypothesis with a primary metric, guardrail metric, and minimum detectable effect

Calculate the sample size needed for a two-variant test given a baseline conversion rate and a 10 percent relative lift target

Explain the risks of peeking at results early and how sequential testing or fixed-horizon analysis mitigates them

Distinguish statistical significance from practical significance using a scenario where a small lift is significant but not worth shipping

Interpret a results table with confidence intervals and state the ship, iterate, or abandon decision with justification

Identify three experiment validity threats: sample ratio mismatch, novelty effect, and instrumentation drift

Explain what session replay captures and how it complements quantitative funnel data

Describe privacy considerations for replay, including masking of sensitive fields and consent requirements

Use a replay-based investigation method: start from a funnel drop-off, filter replays to that step, and document observed friction patterns

Categorize observed friction into usability, performance, and expectation-mismatch buckets with at least two examples each

Connect replay findings back to the event taxonomy and propose one new event or property that would have surfaced the issue quantitatively

Assemble a final review sheet covering all five domains with the three concepts you are least confident about