Funnel Analysis and Conversion Tracking Questions

Product analytics practice focused on analyzing user journeys and measuring how well a product or website converts visitors into desired outcomes. Core skills include defining macro and micro conversions, mapping multi step user journeys, designing and instrumenting event level tracking, building and interpreting conversion funnels, calculating step by step conversion rates and drop off, and quantifying funnel leakage. Candidates should be able to segment funnels by cohort, acquisition source, channel, device, geography, or user persona; perform retention and cohort analysis; reason about time based attribution and multi path journeys; and estimate the impact of optimization levers. Practical competencies include implementing tracking, validating data quality, identifying common pitfalls such as missing events or incorrect attribution windows, and using split testing and iterative analysis to validate hypotheses. Candidates should also be able to diagnose root causes of drop off, create mental models of user behavior, run diagnostic analyses and experiments, and recommend prioritized interventions and product or experience changes with expected outcomes and measurement plans.

EasyTechnical

56 practiced

Define macro and micro conversions in the context of funnel analysis. Provide three concrete examples of macro and micro conversions for both an e-commerce product and a freemium SaaS product. Explain why distinguishing between macro and micro conversions matters when prioritizing optimization work and how you would communicate the difference and trade-offs to business stakeholders.

MediumTechnical

68 practiced

When designing experiments that may increase conversions but also increase returns or refunds, what guardrail metrics would you define and why? Provide at least five guardrails (quantitative), their acceptable thresholds or monitoring approach, and action playbooks for when guardrails are violated post-deployment.

MediumTechnical

63 practiced

You need to produce a 7-day retention cohort table by acquisition_channel where cohorts are defined by user first_seen_date. Using user and events tables outlined below, write a SQL query that computes cohort size and retention percentages for days 0 through 14 after signup. Explain assumptions about timezone and users with multiple acquisition channels.

Schemas:

sql

users(user_id STRING, first_seen_date DATE, acquisition_channel STRING)
events(user_id STRING, event_name STRING, occurred_at DATE)

EasyTechnical

97 practiced

Explain the difference between drop-off and churn in product analytics. Provide three quantitative definitions or SQL-like pseudocode for each term (for example: time-based, activity-based, cohort-based definitions), and explain in which business scenarios each definition is most appropriate.

HardTechnical

51 practiced

Outline a plan to build an algorithmic multi-touch attribution using a Markov chain model to quantify removal effect of each marketing channel. Describe data preparation (user-level paths), transition matrix construction, computation of absorbing probabilities, how to compute removal effects, and considerations for sparse channels.

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