Unit Economics for Fintech Products
This use case explains how to model unit economics and cohort behaviour for fintech and digital financial products in Model Reef.
You will:
Represent users, customers or accounts as economic units.
Model acquisition, activation, churn and monetisation over time.
Link unit level behaviour to revenue, cost and contribution margin.
Use cohorts and scenarios to evaluate product and growth strategies.
Model Reef is not an event level analytics warehouse. It uses aggregated cohort and driver based assumptions rather than raw user events.
When to use this pattern
Use this pattern when:
You operate a fintech or digital financial product with recurring or transactional revenue.
You want to understand CAC, LTV, payback and unit profitability.
Product and growth decisions depend on user behaviour and retention.
You want finance and product teams to share a common quantitative model.
It can be combined with:
Loan Book Growth Forecasting (for credit products).
Multi Product Financial Model.
Build a Pricing Model.
Build a Unit Economics Model.
Architecture overview
Unit economics modelling uses:
Unit and cohort structure
Segments by product, channel, geography or risk band.
Monthly or quarterly acquisition cohorts.
Behaviour drivers
Acquisition and conversion rates.
Activation and engagement.
Churn, downgrade and upgrade behaviour.
Cross sell and upsell patterns.
Economics per unit
Revenue per active user, account or contract.
Direct servicing cost per unit.
Variable marketing and support cost.
Contribution margin per unit and per cohort.
Scenario logic
Different growth and retention strategies.
Pricing and product changes.
Channel mix and CAC changes.
Step 1: Define units, segments and cohorts
Decide what a unit is in your context, for example:
Active customer or account.
Funded loan or card.
Active card or wallet.
Subscription or contract.
In the branch tree or in variable naming, distinguish:
Products (for example BNPL, SME credit, payments).
Channels (direct, partner, marketplace).
Cohorts (for example month of acquisition).
You can model cohorts explicitly via separate variables or implicitly via retention curves applied to aggregate units.
Step 2: Build acquisition and funnel drivers
In the Data Library, create drivers such as:
Marketing Spend by product and channel.
Cost per Lead, click or application.
Conversion rates from lead to activated user.
Number of New Units (customers, accounts, loans) per period.
Where marketing is a major cost, model CAC as:
CAC = Marketing Spend divided by Number of New Units.
Create Revenue or Driver variables that store unit counts, for example:
Units - Active Customers - Product A.
Units - New Customers - Product A.
These will underpin revenue and cost calculations.
Step 3: Model retention, churn and cohort behaviour
Define retention or churn curves per segment, such as:
Monthly Retention Rate for each cohort.
Churn Rate that changes over time as cohorts age.
Upgrade and downgrade flows if you have multiple product tiers.
You can approximate cohort behaviour by:
Using a generic retention curve applied to all units, or
Maintaining separate retention profiles for major cohorts or segments.
From this, compute:
Active Units per period based on new units and retention.
Average Life Time or implied lifetime from the retention curve.
These series will drive recurring revenue and ongoing cost.
Step 4: Attach revenue and cost to each unit
Create Revenue variables such as:
Revenue - Product A - Subscription Fees.
Revenue - Product A - Transaction Fees.
Revenue - Product A - Interest or Spread (for credit products).
Attach drivers for:
ARPU (average revenue per user) or revenue per active unit.
Take rates on transaction volumes.
Spread over funding cost for lending products.
Revenue can then be modelled as:
Revenue = Active Units × Revenue per Unit, or
Revenue = Volume × Take Rate where volume itself is driven by units.
Create Opex variables for unit related costs, such as:
Direct servicing cost per unit.
Customer support cost per unit or per active user.
Variable marketing and incentives.
Contribution margin is then:
Contribution per Unit = Revenue per Unit minus Direct Cost per Unit.
Total Contribution = Active Units × Contribution per Unit.
Step 5: Compute CAC, LTV and payback
With unit revenue, cost and acquisition drivers in place, you can compute:
CAC (Customer Acquisition Cost) as above.
LTV (Life Time Value) using discounted or undiscounted cashflows per unit.
Payback Period as the time it takes for cumulative contribution per cohort to exceed CAC.
You can implement these as:
Custom report lines or KPI cards using formulas.
Cohort specific calculations if you want more detail.
For credit products, combine unit economics with Loan Book Growth Forecasting and Credit Loss and Provision Modelling to capture risk adjusted economics.
Step 6: Use scenarios for product, pricing and growth strategies
Clone the base model into scenario models to explore:
Higher or lower growth and acquisition spend.
Different pricing and fee structures.
Improved or worsened retention and engagement.
Channel mix shifts with different CAC and unit economics.
Credit loss and funding effects where products involve balance sheet risk.
In each scenario, adjust:
Acquisition, conversion and retention drivers.
Revenue per unit and cost per unit assumptions.
Product mix and cross sell or upsell behaviour.
Discount rates if you use DCF style LTV.
Compare scenarios using:
CAC, LTV and LTV to CAC by segment.
Contribution margin and unit profitability.
Portfolio level P&L, cash and valuation.
Check your work
Input assumptions are anchored in observed data or trials where possible.
LTV and payback are realistic compared to historical cohorts.
Product and growth teams agree on the definitions and metrics used.
Scenario comparisons reflect plausible strategic options.
Troubleshooting
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