M05-07 · AI + Data & Decision Science

Revenue and Business Metrics

Teaches students to calculate, analyze, and present the business metrics that drive company decisions. Covers MRR computation with all its edge cases, unit economics (CAC, LTV, churn rate, conversion by segment), board deck data narratives, revenue forecasting, and the art of building a data culture where metrics are defined once, centrally, and trusted. Students learn to be the person the CEO trusts with the numbers.

20 Hours

7 Learning objectives

Create Bloom's ceiling (?)

4 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Calculate MRR accurately, handling trials, annual plan proration, mid-month upgrades/downgrades, manual invoices, and discrepancies between billing systems and internal databases Apply
Compute unit economics (CAC by channel, LTV by segment, churn rate by cohort, conversion rate with confidence intervals) and explain what each metric means for business decisions Apply
Build board deck data narratives that tell a coherent story: MRR growth, retention trends, feature adoption insights, and forward-looking projections with explicit assumptions Create
Construct revenue forecasts using time-series methods (Holt-Winters, Prophet) and present as ranges with documented assumptions and break conditions Apply
Evaluate requests for data using a prioritization framework: revenue impact, CEO frequency, team-unblocking potential, and long-term infrastructure value Evaluate
Design metric definition systems (Notion pages, data dictionaries) that establish single sources of truth and reduce "why don't my numbers match?" conversations Create
Analyze when a simple heuristic outperforms a complex model, articulating the tradeoff between interpretability, data requirements, and prediction accuracy at different company stages Analyze

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

Core Competencies

What You'll Master

Revenue Metrics

MRR calculation with edge cases, ARR, net revenue retention, expansion/contraction analysis, billing system reconciliation.

Unit Economics

CAC, LTV, payback period, churn rate, conversion rate by segment, and the confidence intervals around each.

Data Narrative & Forecasting

Building board-ready metric presentations, time-series forecasting with ranges, communicating what could break the forecast.

Data Culture Building

Centralized metric definitions, self-serve enablement, saying "no" to bad data requests, prioritizing analyst time for maximum organizational impact.

Portfolio Artifact

What You'll Build

Board Deck Data Narrative — Student produces a complete board-ready data package for a realistic SaaS company: MRR calculation with edge case handling (annual plans, trials, manual invoices), unit economics by segment, retention cohort analysis, a 6-month revenue forecast with best/base/worst scenarios and documented assumptions, and a 1-page metric definitions reference. Delivered as both a Google Sheets workbook (for board reference) and a slide presentation (for board meeting).

Tools You'll Use

Industry Tools, Not Toy Projects

Python (pandas, statsmodels, Prophet)

Data analysis and forecasting libraries for computing metrics, building models, and generating projections.

SQL

Warehouse queries for pulling revenue data, computing metrics, and validating calculations against source systems.

Google Sheets

Board-facing deliverable format for interactive metric exploration and scenario modeling.

Google Slides / Notion

Presentation and documentation tools for board decks and metric definition systems.

Stripe API

Revenue data source for pulling subscription, payment, and billing information programmatically.

Claude

AI assistant for forecasting code, metric definition drafting, and narrative structuring.