M02-01 · AI Product & Business Strategy

Product Analytics — Metrics, SQL, and Data-Driven Insight

Teaches students to extract actionable product insights from data using analytics tools and SQL. Covers product analytics platforms (Amplitude/Mixpanel/GA4), writing SQL queries against data warehouses for retention cohorts and funnel analysis, building custom dashboards, user segmentation, discovering non-obvious patterns in product data, and maintaining statistical rigor. Students learn to be the person on the product team who finds the insight nobody else noticed.

35 Hours

8 Learning objectives

Evaluate Bloom's ceiling (?)

5 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Navigate product analytics platforms (Amplitude/Mixpanel/GA4) to analyze user behavior: funnels, retention curves, feature adoption, and event sequences Apply
Write SQL queries against data warehouses (BigQuery/Snowflake/Redshift) for product analysis: retention cohorts, funnel drop-off, user segmentation, and feature usage correlation Apply
Build and maintain product dashboards (Tableau/Looker/Metabase) with 5-7 key metrics relevant to a product area, calibrated to stakeholder needs Create
Discover non-obvious insights in product data: correlations between feature usage and retention, segment-specific behavior patterns, and leading indicators of churn Analyze
Verify metric definitions before presenting data: check denominators, time periods, event tracking accuracy, and known data quality issues Apply
Use AI tools to assist with SQL query generation and data analysis while verifying output for warehouse-dialect accuracy and logical correctness Evaluate
Segment users by behavior, demographics, plan tier, acquisition channel, and feature usage to identify meaningful differences in outcomes Analyze
Present data findings with appropriate statistical caveats: confidence levels, sample sizes, correlation vs. causation distinctions, and confounding variables Apply

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

Core Competencies

What You'll Master

Product Analytics Platforms

Amplitude/Mixpanel/GA4 proficiency, event tracking, funnel analysis, retention curves, feature adoption measurement.

SQL for Product Insight

Writing analytical queries against data warehouses, cohort analysis, window functions, joins across event and user tables.

Dashboard Design

Building stakeholder-appropriate dashboards, metric selection, visualization choices, refresh cadence.

Insight Discovery

Finding non-obvious patterns, hypothesis generation from data, segment-based analysis, leading indicator identification.

Data Rigor

Metric definition verification, statistical validity, sample size awareness, appropriate caveats in data presentation.

Portfolio Artifact

What You'll Build

Product Analytics Deep Dive — Student conducts a full analytics investigation on a real or simulated product dataset: defines 5-7 key metrics with explicit calculation methodology, writes 8-10 SQL queries (retention cohorts, funnel analysis, feature correlation, user segmentation), builds a dashboard, and produces a 3-page insight report that includes at least one non-obvious finding with supporting evidence, statistical caveats, and recommended product actions.

Tools You'll Use

Industry Tools, Not Toy Projects

Amplitude / Mixpanel

Product analytics platforms for tracking user behavior, funnels, retention, and feature adoption.

Google Analytics 4

Web and app analytics platform for traffic analysis, conversion tracking, and audience insights.

BigQuery / Snowflake

Cloud data warehouses for running SQL queries against large product datasets.

Tableau / Looker / Metabase

Business intelligence and dashboard tools for building stakeholder-facing metric visualizations.

Google Sheets

Spreadsheet tool for ad hoc analysis, pivot tables, and quick data exploration.

Claude

AI assistant for SQL query generation, data analysis review, and insight synthesis.

Gong / Chorus

Call recording and analysis platforms for correlating qualitative customer insights with quantitative data.