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.
A/B Testing and Optimization Methodology
Discuss your experience designing and running A/B tests on content elements: headlines, formats, messaging, calls-to-action, visual design, content length, etc. Share specific examples of tests you've run with results and how you implemented learnings. Discuss statistical significance and proper experimental design. Show how you prioritize testing opportunities and build a testing roadmap.
Conversion Measurement and Attribution
Covers measurement frameworks, attribution modeling, and performance analysis for conversion programs. Topics include understanding and selecting attribution models such as first touch, last touch, and multi touch, measurement of key conversion metrics including click through rate, conversion rate, cost per acquisition, return on ad spend, and customer lifetime value, aligning metrics to business goals, multi channel contribution analysis, and implications of attribution choice on optimization decisions. Also includes designing experiments and analytics to measure incremental impact, handling tracking and instrumentation challenges, and communicating performance tradeoffs to stakeholders.
Hypothesis and Test Planning
End to end practice of generating clear testable hypotheses and designing experiments to validate them. Candidates should be able to structure hypotheses using if change then expected outcome because reasoning ground hypotheses in data or qualitative research and distinguish hypotheses from guesses. They should translate hypotheses into experimental variants and choose the appropriate experiment type such as A and B tests multivariate designs or staged rollouts. Core skills include defining primary and guardrail metrics that map to business goals selecting target segments and control groups calculating sample size and duration driven by statistical power and minimum detectable effect and specifying analysis plans and stopping rules. Candidates should be able to pre register plans where appropriate estimate implementation effort and expected impact specify decision rules for scaling or abandoning variants and describe iteration and follow up analyses while avoiding common pitfalls such as peeking and selection bias.