Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Marketing Analytics and Measurement
Covers the design, implementation, and interpretation of marketing measurement systems that connect marketing activities to business outcomes. Topics include defining and prioritizing key performance indicators across the marketing funnel from awareness and consideration through conversion, retention, and advocacy. Core metrics and diagnostic measures include click through rate, conversion rate, impressions, engagement and session metrics, bounce rate, lead volume, cost per lead, cost per acquisition, customer acquisition cost, customer lifetime value, return on advertising spend, return on investment, marketing influenced revenue, pipeline contribution, marketing qualified leads, sales accepted leads, and lead to opportunity conversion rates. Measurement frameworks and methods include last click and multi touch attribution approaches, marketing mix modeling, incrementality testing and holdout group experiments, randomized controlled experiments and split testing, and considerations for statistical significance, sample size, noise, and distinguishing correlation from causation. Also covers data and instrumentation concerns such as tagging and event tracking, data flows from advertising and marketing systems into analytics platforms and data warehouses, data quality and identity resolution, and privacy driven tracking limitations. Reporting and dashboard design topics include selecting leading versus lagging indicators, balancing granular event level dashboards with executive level summaries, setting realistic targets and benchmarks, communicating findings and recommended actions to stakeholders, and using measurement to inform channel mix, campaign optimization, and budget allocation decisions.
Business Intelligence Background
A summary of business intelligence experience including the BI platforms and tools used, types of dashboards and reports built, data volumes and sources, analytical methods, stakeholder consumption patterns, and measurable business outcomes. Candidates should explain how BI efforts influenced decisions, examples of ETL or modeling work, and any leadership or ownership of BI initiatives.