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Marketing Academy · Field Work●Growth Marketing
MiniForecast· 25 minutes

The 2.5x Bet: Forecasting a Revenue Base Under Different NRR Scenarios

Coinbase

Objective: Given a $10M ARR base, forecast 5-year outcomes at 100%, 110%, and 120% NRR with zero new-customer acquisition, and decide which marketing lever is worth funding to close the gap.

You're presenting to Coinbase's marketing leadership on whether funding a usage-triggered expansion campaign is worth it, framed entirely around what different NRR levels do to the existing subscription base over 5 years.

Use the lesson's compounding math to forecast 5-year ARR at three NRR levels, then justify funding the expansion lever with the dollar gap it closes.

Before you start

What you'll need

Free path (everything below is enough to finish)

FreeBuild the 3-scenario compounding forecast table

Free, handles the exponential formula without any paid modeling tool

Google Analytics 4(optional)
FreePull the current usage-trigger event volume to estimate realistic NRR lift from the campaign

Free event data grounds the NRR-lift assumption in real trigger volume instead of a guess

No access? Use a conservative 5-point NRR lift assumption if usage-event data isn't available

Paid upgrades (optional, faster/deeper)

Google Sheets fully covers a one-time forecast; a paid tool only pays off if you're rerunning this model every quarter.

Amplitude(optional)
FreemiumModel cohort-level NRR lift scenarios directly from historical usage-trigger campaigns

Built-in cohort forecasting saves rebuilding the compounding model by hand each quarter

The process

1 step

Step 01 of 01

How Marketing Actually Moves This Number

The lesson states a sustained 120% NRR compounds a $10M ARR base to roughly $24.9M over 5 years with zero new sales, and names usage-triggered expansion campaigns as a marketing-owned lever.

At $10M starting ARR, what's the 5-year outcome at 100% NRR (flat), 110% NRR, and 120% NRR, and what's the dollar gap between flat and 120%?

Google Sheets— Build a 5-year compounding table, ARR × NRR^years, for all three scenarios.

Procedure

  1. Set up a 5-year table for $10M ARR at 100%, 110%, and 120% NRR
  2. Compute year-5 ARR for each using ARR × NRR^5
  3. Compute the dollar gap between the 100% and 120% scenarios
Sample output
Starting ARR: $10,000,000

           Year 1     Year 3      Year 5
100% NRR  $10.0M     $10.0M      $10.0M
110% NRR  $11.0M     $13.3M      $16.1M
120% NRR  $12.0M     $17.3M      $24.9M

Year-5 gap (100% vs 120%): $14.9M

Healthy

The team sees the $14.9M gap and treats a usage-triggered expansion campaign, projected to lift NRR from 100% toward 110-120%, as directly fundable against that number.

Unhealthy

Presenting the NRR gap as an abstract percentage-point difference without translating it into the dollar figure leadership actually budgets against.

What this means

A 20-point NRR difference isn't a rounding error, it's the difference between a flat $10M business and a $24.9M one in year 5, with the exact same number of customers.

So what do I do about it?

SymptomActionEffort
Leadership is skeptical that a lifecycle/expansion campaign is worth fundingPresent the 5-year dollar gap, not the NRR percentage, in the next budget review30 min
YouYou can do this yourself, no engineering access required.

Final deliverable

A one-page 5-year forecast memo: ARR outcomes at three NRR scenarios, the dollar gap between flat and best-case, and a funding recommendation for the expansion campaign.

See a reference example
Sample output
Adyen, 5-year NRR scenario forecast (excerpt)

Starting ARR: $25M
100% NRR -> $25M in Year 5
118% NRR (enterprise benchmark) -> $56.9M in Year 5
Gap: $31.9M
Recommendation: fund the usage-triggered expansion program, the 5-year gap alone justifies a 7-figure campaign budget

Success criteria

You're done when you can:

  • Correctly compounds ARR across 5 years for all three NRR scenarios
  • Converts the NRR percentage gap into a dollar figure leadership can act on
  • Ties the forecast to a specific, marketing-owned lever named in the lesson, not a generic 'improve retention' recommendation