Analytics by Function: Customer, Marketing, Product & Operations

Analytics by Function: Customer, Marketing, Product & Operations

Analytics by Function: Customer, Marketing, Product & Operations is a structured track of 13 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.

What this course covers

  • How Analytics Changes Shape by Business Function - Why the same toolkit produces very different work across functions.
  • Customer Analytics: Segmentation, Retention & Churn - The customer questions analytics teams are asked, and how each is answered.
  • Customer Lifetime Value: Calculating It Two Ways - Historic and predictive lifetime value, computed on the same cohort and compared.
  • Marketing Analytics: Channels, Attribution & Return on Spend - Channel measurement, attribution models and their well-known limits.
  • Growth Analytics: Funnels, Activation & Referral Loops - Funnel diagnosis, activation definition and measuring a referral loop.
  • Product Analytics: Events, Engagement & Feature Adoption - Instrumenting a product and measuring whether a feature actually landed.
  • Retention & Cohort Analysis: Reading the Triangle - Building a retention triangle and separating cohort effects from seasonality.
  • Financial Analytics: Revenue, Margin & Variance Reporting - Revenue and margin analysis, and explaining a variance against plan.
  • Operations & Supply Chain Analytics: Forecasting & Inventory - Demand forecasting, inventory policy and service-level trade-offs.
  • People Analytics: Attrition, Hiring Funnels & Engagement - Attrition modelling, recruitment funnel metrics and survey analysis done properly.
  • Risk & Fraud Analytics: Scorecards and Anomaly Detection - How scorecards are built and how anomalies are surfaced without drowning in alerts.
  • Pricing & Revenue Analytics: Elasticity and Discount Impact - Estimating price sensitivity and measuring what a discount actually cost.
  • Case Study: The Same Dataset Read by Four Different Functions - One transaction dataset analysed four ways, showing how the question changes the answer.