TL;DR: Most MRR growth forecasts are either wild guesses or complicated models that nobody understands. The right approach is building a simple model with five inputs: new customer acquisition rate, average new customer MRR, expansion rate, contraction rate, and churn rate. Plug in your historical averages, project forward month by month, and you get a realistic forecast that actually matches how your business works. The model takes an hour to build and gives you 80% of the accuracy of sophisticated forecasting systems.
We’ve reviewed dozens of financial models from SaaS companies seeking funding or trying to understand their business. Half of them are just revenue trending up at some assumed growth rate. The other half are sprawling Excel nightmares with 200 inputs that break when you change anything.
Neither approach works. The trend-based models ignore business mechanics and blow up when conditions change. The complex models are impossible to maintain and nobody trusts them because nobody understands what’s happening inside.
The right model sits in between. It captures how your business actually works (customers come in, some expand, some churn) without requiring a PhD in financial modeling to operate.
Here’s what a simple MRR model looks like and why it works better than either extreme.
Your MRR growth comes from five fundamental drivers. Model these accurately and everything else follows:
New customers per month. How many new customers are you adding? This comes from your sales pipeline, marketing funnel, or historical acquisition patterns. For a company adding 15 new customers monthly, that’s your input.
Average MRR per new customer. What do new customers typically pay? If some pay $500, some pay $2,000, use the average. For our example, let’s say $800.
Monthly expansion rate. What percentage of your existing customer base expands each month through upsells, additional seats, or price increases? Historical data shows this. Maybe it’s 4% of starting MRR monthly.
Monthly contraction rate. What percentage downgrades, reduces seats, or negotiates price cuts? Maybe it’s 1% of starting MRR monthly.
Monthly churn rate. What percentage of customers cancel completely? Maybe it’s 2% of starting MRR monthly.
That’s it. Five numbers. If you can’t confidently state these five numbers for your business, stop reading and go calculate them from historical data. They’re the foundation of everything.
Here’s how the model works, using realistic numbers:
Starting point (January): $100K MRR, 125 customers
Month 1 (February) calculation:
– Starting MRR: $100K
– New customer MRR: 15 customers × $800 = +$12K
– Expansion MRR: $100K × 4% = +$4K
– Contraction MRR: $100K × 1% = -$1K
– Churn MRR: $100K × 2% = -$2K
– Ending MRR: $100K + $12K + $4K – $1K – $2K = $113K
– Ending customers: 125 + 15 – (125 × 2%) = 137 customers
Month 2 (March) calculation:
– Starting MRR: $113K
– New customer MRR: 15 × $800 = +$12K
– Expansion MRR: $113K × 4% = +$4.5K
– Contraction MRR: $113K × 1% = -$1.1K
– Churn MRR: $113K × 2% = -$2.3K
– Ending MRR: $113K + $12K + $4.5K – $1.1K – $2.3K = $126.1K
– Ending customers: 137 + 15 – (137 × 2%) = 149 customers
Continue this month by month for 12, 24, or 36 months. Each month’s ending MRR becomes next month’s starting MRR. The expansion, contraction, and churn rates apply to the growing base, so they compound.
In this example, the company grows from $100K to $126K in two months (26% growth) even though they’re only adding $12K in new customer MRR monthly. That’s because expansion (+4% monthly) more than offsets contraction and churn (-3% combined), creating net revenue retention of 101%.
The model captures the fundamental mechanics of SaaS growth: you add customers, they expand or contract over time, some leave. Everything else is details.
More importantly, you can actually maintain this model. Update your five inputs monthly based on actual performance. If new customer acquisition accelerates from 15 to 20 monthly, plug in 20. If churn spikes from 2% to 3%, reflect that immediately.
The model lets you test scenarios easily:
What if we double sales capacity and add 30 customers monthly instead of 15? Change one number, see the impact.
What if we improve onboarding and reduce churn from 2% to 1.5%? Change one number, see the dramatic long-term effect.
What if expansion slows from 4% to 3% because we’re running out of upsell opportunities? Model it.
We’ve seen companies with 50-tab Excel models that take a full-time FP&A person to maintain get more value from this simple five-input model because they can actually use it for decision making instead of just creating it once for fundraising.
The basic model works for most companies, but you can add layers of sophistication where your business requires it:
Segment by customer size. Run separate models for SMB, mid-market, and enterprise if these segments behave differently. Each has its own new customer acquisition, expansion, and churn rates. Sum them to get total MRR.
Model acquisition by channel. If you acquire customers through paid search, content, and direct sales with different volumes and unit economics, model each channel separately. This reveals which channels drive growth most efficiently.
Add sales capacity constraints. Instead of assuming you can always add 15 customers monthly, tie new customer acquisition to rep productivity. If you have 3 reps each closing 5 customers monthly, and you plan to hire 2 more reps who ramp over 3 months, model that growth curve.
Include cohort aging effects. Newer customers might churn at 3% monthly while customers over 12 months old churn at 1%. Split your customer base into cohorts and apply different retention rates.
Layer in pricing changes. If you’re raising prices in Q3, model the expansion impact on existing customers and higher starting MRR for new customers.
The key is adding complexity only where it meaningfully improves accuracy. A company with 90% of revenue from one customer segment doesn’t need segmentation. A company with equal revenue from three distinct segments does.
Build the model using historical data, then test it by forecasting the last 12 months and comparing to what actually happened. If your model predicted $850K MRR in December and you ended at $820K, you’re 3.5% off—that’s good. If you predicted $850K and ended at $650K, something’s wrong with your assumptions.
Common reasons models miss reality:
Your expansion rate is wrong. You assumed 4% monthly expansion but actual historical average was 2.5%. Fix the input.
Your churn is seasonal. You assumed constant 2% monthly churn but Q4 actually sees 3.5% churn due to contract renewals and budget resets. Model seasonal patterns.
Your new customer acquisition isn’t steady. You assumed 15 per month but actually you do 8-10 most months with spikes to 25 in Q4. Model the pattern, don’t average it.
Your average MRR per new customer has been declining. You assumed $800 but you’re actually moving downmarket and new customers average $600. Update the assumption.
Test, adjust, test again until the model reliably projects recent history within 5-10%. Then you can trust it to project the future.
Once you have a working model, use it to answer the questions that actually matter:
How much do we need to grow new acquisition to hit our revenue target? Plug in different new customer numbers until the output matches your goal. This reveals whether you need 10% more sales capacity or 3x more sales capacity.
Is it better to invest in reducing churn or increasing expansion? Model both scenarios. Reducing churn from 2% to 1.5% might increase ending MRR more than increasing expansion from 4% to 5%. Now you know where to invest product resources.
How long until we hit $1M MRR at current growth rates? Run the model forward until you hit the milestone. This grounds planning in reality rather than arbitrary timelines.
What happens if our growth rate slows by 30%? Reduce new customer acquisition by 30% and see the impact. This stress-tests your cash runway and helps with contingency planning.
What’s our path to break-even if we can’t raise the next round? Model reduced spending on acquisition (fewer new customers) while maintaining product investment to improve retention. Find the scenario where revenue growth covers burn rate.
We’ve used this model in board meetings to settle debates about where to invest. Sales wants more budget to hire reps. Product wants budget to reduce churn. Model both investments with realistic assumptions about impact. Whichever increases MRR faster wins the budget.
The model is simple but easy to mess up if you’re not careful:
Using bookings instead of MRR. If you sign a $100K annual contract, that’s not $100K MRR, it’s $8,333 MRR. Keep everything in monthly recurring terms.
Forgetting that expansion, contraction, and churn compound. These rates apply to the growing base each month. A 4% expansion rate means growing amounts of expansion dollar as MRR grows, not a fixed dollar amount.
Modeling gross metrics instead of net. If 4% of customers expand but 1% contract, don’t model 4% expansion. Model 3% net expansion. Or track expansion and contraction separately (better).
Not updating assumptions regularly. If market conditions change, your historical averages become less predictive. Update the model monthly with rolling 6-month averages of your key rates.
Assuming perfection. Don’t model 0.5% churn if you’ve never achieved below 2%. Be realistic about what’s achievable, not aspirational.
Not accounting for sales rep ramp time. New reps don’t produce immediately. Model a 3-month ramp where they close 30%, 60%, then 100% of quota.
Companies that model MRR well update their assumptions monthly and track forecast versus actual religiously. When actual MRR comes in different than forecast, they investigate why and refine their model.
They use the model in leadership meetings to align around growth strategy. Everyone understands the five inputs and how they drive outcomes. When sales says they need three more reps, finance can model the impact and show it increases MRR 22% over 12 months for $450K investment. Now you have a real ROI discussion.
They build scenarios regularly. Best case (everything improves 20%), base case (current trends), worst case (deterioration across metrics). This shows a range of outcomes and helps with risk management.
They don’t treat the model as truth, they treat it as a tool for understanding drivers. When reality diverges from the model, that’s information about what’s changing in the business.
Most importantly, they keep it simple enough that non-finance people can understand and use it. The sales leader can plug in different acquisition numbers. The product leader can adjust churn rates. The CEO can test strategic scenarios without needing a data analyst.
This is what separates useful models from decorative models. Useful models get used weekly to make decisions. Decorative models get built for fundraising and then forgotten.
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Q: How far forward should we forecast with this model?
For operational planning, 12 months is useful. For fundraising, 24-36 months is expected but gets increasingly uncertain. Beyond 36 months, too many variables change for the model to be meaningful. The right approach is building a detailed 12-month forecast you trust, extending it to 24-36 months with clear scenarios, and being honest with investors that the outer years are directional not precise. Update the model quarterly as you get more data.
Q: What if we don’t have enough historical data to calculate reliable rates?
If you’re pre-revenue or very early, use industry benchmarks as placeholders: 3-5% monthly churn for SMB SaaS, 1-2% for mid-market, 3-4% monthly expansion, 1% contraction. Update these aggressively as you gather actual data. Even 3 months of real data is better than pure benchmarks. If you have 6 months of data, use rolling 6-month averages. If you have 12+ months, use rolling 12-month averages to smooth seasonality.
Q: Should we model pessimistic, realistic, or optimistic assumptions?
Build three versions: base case using historical averages (most likely), upside case showing 20% improvement across key metrics (what’s possible if things go well), and downside case showing 20% degradation (what happens if we hit headwinds). Present all three to boards and investors. Use base case for internal planning because it’s most likely to be accurate. The scenarios help everyone understand the range of outcomes and what drives them, which is more valuable than pretending you know exactly what will happen.