Growth & Business Optimization Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Experimentation and Product Validation
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
Growth Prioritization Frameworks
Core frameworks and techniques used to prioritize growth projects and experiments. Includes qualitative matrices such as impact and effort mapping and value versus complexity, and quantitative scoring models such as RICE scoring spelled out as reach times impact times confidence divided by effort. Candidates should understand how to estimate reach, impact magnitude, confidence or uncertainty, and required effort; consider sample size and statistical confidence when prioritizing experiments; assess strategic alignment with company goals and resource constraints; and communicate tradeoffs clearly. Interview preparation includes practicing ranking and scoring hypothetical initiatives, explaining assumptions and sensitivity to inputs, and justifying prioritization decisions under time or resource constraints.
Feature Success and A/B Testing
How you'd measure success of a specific feature launch. Setting up experiments or A/B tests. Understanding statistical significance and sample sizes at a basic level. Interpreting results and deciding when to ship, iterate, or kill a feature.
Feature Success Measurement
Focuses on measuring the impact of a single feature or product change. Key skills include defining a primary success metric, selecting secondary and guardrail metrics to detect negative side effects, planning measurement windows that account for ramp up and stabilization, segmenting users to detect differential impacts, designing experiments or observational analyses, and creating dashboards and reports for monitoring. Also covers rollout strategies, conversion and funnel metrics related to the feature, and criteria for declaring success or rollback.
Funnel Analysis and Conversion Optimization
Skills for mapping multi stage user journeys and diagnosing conversion drop offs to identify the highest leverage opportunities. Candidates should be able to define funnel stages, estimate and calculate conversion rates and absolute impact, segment users to reveal differing behaviors, and use cohort analysis and event level data to generate hypotheses. This includes designing instrumentation to capture relevant signals, selecting appropriate success metrics and leading indicators, accounting for attribution and confounders, and proposing randomized experiments with power and sample size considerations to validate improvements. The final step is linking funnel changes to downstream business outcomes such as activation retention or monetization.
Experiment Design and Execution
Covers end to end design and execution of experiments and A B tests, including identifying high value hypotheses, defining treatment variants and control, ensuring valid randomization, defining primary and guardrail metrics, calculating sample size and statistical power, instrumenting events, running analyses and interpreting results, and deciding on rollout or rollback. Also includes building testing infrastructure, establishing organizational best practices for experimentation, communicating learnings, and discussing both successful and failed tests and their impact on product decisions.
A and B Test Design
Designing and running A and B tests and split tests to evaluate product and feature changes. Candidates should be able to form clear null and alternative hypotheses, select appropriate primary metrics and guardrail metrics that reflect both product goals and user safety, choose randomization and assignment strategies, and calculate sample size and test duration using power analysis and minimum detectable effect reasoning. They should understand applied statistical analysis concepts including p values confidence intervals one tailed and two tailed tests sequential monitoring and stopping rules and corrections for multiple comparisons. Practical abilities include diagnosing inconclusive or noisy experiments detecting and mitigating common biases such as peeking selection bias novelty effects seasonality instrumentation errors and network interference and deciding when experiments are appropriate versus alternative evaluation methods. Senior candidates should reason about trade offs between speed and statistical rigor plan safe rollouts and ramping define rollback plans and communicate uncertainty and business implications to technical and non technical stakeholders. For developer facing products candidates should also consider constraints such as small populations cross team effects ethical concerns and special instrumentation needs.
Growth Trade Offs and Tensions
Focuses on growth specific tensions and trade offs candidates have navigated, such as user acquisition versus retention, growth velocity versus unit economics, viral loops versus organic brand building, or paid growth versus organic channels. Candidates should describe concrete examples from their work, the metrics and experiments used to evaluate options, how they balanced short term growth and long term health, and how they aligned stakeholders around the chosen approach. This topic evaluates understanding of growth levers, metrics driven decision making, experimentation, trade off reasoning under uncertainty, and communication of trade off rationale.