Unit Economics and Cohort Model

Measure customer-level profitability, retention and payback.

D3 · Financial Modeling

Unit Economics and Cohort Model

Measure customer-level profitability, retention and payback.

Architecture Inputs Formula logic Worked example Controls Common errors Q&A

Model architecture

Define a unit consistently.
Build acquisition cohort.
Track retention, revenue and gross profit.
Allocate variable servicing costs.
Calculate payback and LTV ranges.

A professional model should make the decision logic visible. Inputs belong in a controlled assumption area; calculations should be formula-driven; outputs should state units, dates and scenarios; checks should be obvious and actionable.

Core inputs

CAC
Gross margin
Retention/churn
Expansion
Service cost

Evidence rule: historical figures should reconcile to approved financial statements or management data. Forecast assumptions should show an owner, date, source and rationale.

Formula logic

Relationship	Use
`CAC payback months = CAC ÷ Monthly contribution per customer`	Model formula / relationship
`Simple LTV = ARPU × Gross margin % ÷ Churn rate`	Model formula / relationship

Use the formulas as design relationships, not as substitutes for the accounting policy, contract definition or transaction facts relevant to the model.

Practical example

A ₹12,000 CAC and ₹2,000 monthly contribution yields a six-month simple payback, before overhead and time-value adjustments.

How to implement

Load the historical base and reconcile it.
Put assumptions in dedicated cells.
Build the schedule from operational drivers.
Link outputs to financial statements and dashboards.
Run base, upside and downside checks.

Control checks

Cohorts use consistent start dates
Revenue and gross margin definitions are aligned
Paid and organic CAC are separated
Retention is not averaged misleadingly
LTV assumptions are stress-tested

Finin2min crux: the model is credible only when a reviewer can trace a conclusion to evidence, assumptions and formula logic without guessing.

Common modeling errors

Using revenue instead of contribution
Combining gross and net churn
Ignoring support costs
Assuming infinite customer life
Comparing cohorts with different maturity

Practical Q&A

Should the model contain all possible detail?

No. It should contain enough detail to answer the decision question and explain material risks. Excess detail can hide the drivers.

Should a formula ever contain a hardcoded number?

Only for constants that are genuinely universal or immaterial. Business assumptions should be linked to visible input cells.

What is the minimum review standard?

Reconcile historical data, test key formulas independently, scan for hardcodes and errors, verify scenario switches, and review outputs under downside assumptions.

Source framework: ICAI Ind AS resources, notified accounting standards, Schedule III presentation principles, transaction documents and approved management data. The linked workbook templates are educational starting points, not valuation opinions.