Growth & Business Optimization Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Marketing Funnel Optimization and Experimentation
Using data and controlled experimentation to improve conversion across marketing funnels. Subjects include mapping the funnel and instrumenting events and metrics for each stage, identifying bottlenecks, framing hypotheses and prioritizing experiments by impact, designing and running A B tests and controlled experiments with appropriate sample size considerations, segment and personalization strategies, post experiment analysis including effect size and significance interpretation, attribution considerations, and implementing technical or workflow changes to capture gains. Candidates should be able to describe concrete experiments, metric definitions, and how findings influenced product or marketing decisions.
Core Growth Marketing Metrics & Definitions
Understand and be able to explain key metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), conversion rate, click-through rate, retention, churn, activation rate, and how these metrics relate to each other. Know how to interpret these metrics in context (e.g., is a 2% conversion rate good or bad depending on product/channel).
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.
Analytics, Tracking, and A/B Testing
Proficiency with Google Analytics, understanding of conversion tracking setup, UTM parameters for campaign tracking. Ability to interpret analytics data: traffic sources, user behavior, conversion funnels. Understanding of attribution models and their implications for marketing decisions. A/B testing methodology: hypothesis formation, test design, sample size, statistical significance, holdout groups. Experience with marketing analytics tools and dashboards. Ability to extract insights from data and translate to actionable recommendations.
Impact Driven Mindset
Approach and habits that prioritize measurable impact over activity. Topics include defining success criteria and hypotheses, using data to compare and prioritize initiatives, selecting work with the highest expected business return, balancing short term wins and long term investments, time boxing experiments and minimum viable solutions to learn quickly, and communicating impact oriented choices to stakeholders. Candidates should be ready to show examples of how they set impact goals, measured results, and redirected effort based on outcomes.
Attribution and Revenue Measurement
Methods and frameworks for attributing conversions and revenue to marketing touch points and for measuring return on investment from campaigns. Coverage includes single touch models first interaction last interaction multi touch and data driven attribution their assumptions and limitations how to connect marketing events to sales and revenue signals designing experiments and incrementality tests handling cross device and cross channel identity and deduplication challenges addressing attribution leakage and latency using event and transactional data to calculate revenue metrics and key performance indicators and communicating business impact to stakeholders. Candidates should demonstrate understanding of trade offs between attribution models and practical approaches for producing reliable actionable revenue insights.