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Revenue Model

Forecast your business growth with confidence ๐Ÿ”ฎ

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Written by Baremetrics
Updated this week

What is the Revenue Model?

The Revenue Model allows you to project your MRR based on your current metrics. This powerful forecasting tool combines historical data with your growth assumptions across different scenarios, helping you model revenue trajectories and make data-driven strategic decisions.


Table of Contents


What do I do with these projections?

  • Set Targets: Establish realistic MRR, churn, and expansion goals based on the projected figures.

  • Stress-Test Scenarios: Use Base, Upside, and Downside scenarios to understand the impact of different growth trajectories on revenue.

  • Guide Decision-Making: Align your sales, marketing, and customer success budgets with expected growth areas.

  • Support Forecasting: Integrate the Revenue Model outputs into your financial forecasts to anticipate future revenues and profitability.

  • Communicate: Share insights with internal and external stakeholders to build confidence in your financial plan.


Getting Started

Accessing the Revenue Model

Navigate to Revenue > Revenue Model in your Baremetrics dashboard.


Setting Your Scenario

Use the scenario dropdown in the top-right corner to choose:

  • Base Case: Your most realistic projection

  • Upside Case: Optimistic growth scenario

  • Downside Case: Conservative estimate

Each scenario can have different growth assumptions, letting you prepare for multiple business outcomes.

How the Revenue Model Works

The Revenue Model combines your actual Baremetrics data with forecasting logic to project future performance.

Actuals (White columns): Your real historical data from Baremetrics metrics serves as the foundation.

Forecasts (Blue columns): Future projections are automatically generated by:

  1. Taking trailing averages of your actual data

  2. Applying your specified growth rates to these averages

  3. Manual overrides where you have known expectations

The Revenue Model is organized into sections that represent different ways your MRR changes each month:

New Customers

Forecast growth from customer acquisition:

  • New Customers: Number of new sign-ups per month

  • New ARPU: Average Revenue Per User from new customers

  • New Customer MRR: Automatically calculated (New Customers ร— New ARPU)

Reactivations

Model revenue from customers returning after churning:

  • Reactivations: Count of previously churned customers who return

  • Reactivation ARPU: Average revenue per reactivated customer

  • Reactivation MRR: Automatically calculated (Reactivations ร— Reactivation ARPU)

Expansions

Project additional revenue from existing customers:

  • Expansions: Total expansion revenue (upgrades, add-ons, increased usage)

Churn

Forecast revenue loss from cancellations:

  • Customer Churn: Number of customers expected to cancel

  • Churn ARPU: Average revenue per churned customer

  • Churn MRR: Automatically calculated (Customer Churn ร— Churn ARPU)

Contractions

Model revenue decreases from existing customers:

  • Contractions: Revenue lost from downgrades or reduced usage


Setting Growth Rates

Click the โœˆ๏ธ icon next to any metric to configure how it grows over time:

Period (Trailing Average Months):

  • How many months of historical data to average

  • Example - if 3 is input then the system takes the average of your last 3 months and applies growth to that value

  • This smooths out month-to-month fluctuations

Growth Percentage:

  • Monthly growth rate applied to the trailing average

  • For example: If your 3-month average for New Customers is 100 and you set 5% growth, next month projects 105 customers


Understanding Your Results

The Revenue Model shows how different factors contribute to your overall MRR growth:

  • Positive Contributors: New Customer MRR + Reactivation MRR + Expansions

  • Negative Contributors: Churn MRR + Contractions

  • Monthly Change: The net effect on your MRR each month


Troubleshooting

Projections seem unrealistic?

  • Check that your growth rates match recent performance

  • Verify your trailing period includes representative months

  • Consider if external factors make historical data less relevant

Scenarios don't make sense?

  • Ensure each scenario has distinct, logical assumptions

  • Base Case should be most likely, not most conservative

  • Upside/Downside should reflect realistic best/worst outcomes


Need Help Getting Set Up?

Our Success team is happy to walk you through the setup or help refine your model. You can schedule a time here

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