Startup Financial Model Assumptions: How to Build Defensible Forecasts
Updated October 6, 2026
Startup financial model assumptions are the operating inputs behind a company’s financial projections. They include variables such as customer acquisition, conversion rates, pricing, retention, hiring, salaries, cost of revenue, and payment timing. Together, these assumptions determine projected revenue, expenses, cash burn, runway, and ultimately how much funding a startup may need.
The problem is that many startup models begin with the outcome instead of the assumptions behind it. Revenue grows by 50%. Gross margin reaches 75%. Headcount increases by 20%. These numbers may produce a clean forecast, but they don’t explain how the business is expected to get there.
A stronger financial model works in the opposite direction. Revenue should emerge from assumptions about customers, conversion, pricing, and retention. Hiring should reflect the capacity required to support the operating plan. Cash burn and funding requirements should then follow from the revenue, cost, and hiring assumptions built into the model.
This is the basis of driver-based financial modeling: instead of forecasting the numbers you want to reach, model the operating assumptions that would produce them.
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01Financial projections are outputs. The assumptions behind them are what matter. Revenue growth, margins, payroll, cash burn, and funding requirements should emerge from operating assumptions rather than being entered as standalone targets.
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02Strong startup financial models are built from business drivers. Customer acquisition, conversion, pricing, retention, hiring, and cost assumptions should reflect how the company actually expects to operate and grow.
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03Assumptions need to work together across the financial model. Customer growth affects revenue and operating capacity. Hiring affects payroll and cash burn. Together, revenue, costs, and hiring determine runway and how much funding the startup may need.
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04A defensible assumption is more useful than a precise-looking forecast. Good assumptions have an identifiable operating driver, supporting evidence where available, internal consistency with the rest of the model, and a clear way to test what happens when they change.
Topics covered in this guide +
- What Are Startup Financial Model Assumptions?
- Build From Business Drivers, Not Target Growth Rates
- Revenue Assumptions Should Reflect How the Business Actually Sells
- Customer Acquisition and Retention Assumptions Need to Work Together
- Hiring Assumptions Should Follow Operating Requirements
- Cost and Margin Assumptions Need an Operating Explanation
- Cash and Funding Should Be Outputs of the Operating Plan
- How to Tell Whether an Assumption Is Defensible
- Seven Assumptions That Commonly Make Startup Models Hard to Defend
- From Assumptions to a Working Startup Financial Model
- Startup Financial Model Assumptions FAQs
What Are Startup Financial Model Assumptions?
tartup financial model assumptions are the inputs used to translate a startup’s operating plan into financial projections. They represent what the company expects to happen across customer acquisition, pricing, retention, hiring, costs, and other parts of the business that ultimately affect revenue and cash.
The important distinction is between an assumption and an output. A conversion rate is an assumption. The number of customers generated from that conversion rate is a calculated output. Revenue is then another output produced by those customers, their pricing, and their retention over time.
A simple SaaS revenue model might work like this:
Leads × Conversion Rate = New Customers
Opening Customers + New Customers − Churned Customers = Ending Customers
Average Customers × ARPU = Revenue
In this example, leads, conversion, churn, and ARPU are operating assumptions. Revenue growth is the result of those assumptions working together.
The same principle applies throughout the financial model. Hiring dates and salaries drive payroll. Usage or delivery costs influence gross margin. Revenue, payroll, and operating expenses determine cash burn. Cash burn and the minimum cash balance required to operate then help determine runway and funding needs.
This distinction matters because assumptions can be examined and challenged individually. If projected revenue appears too high, the question becomes whether the problem is customer acquisition, conversion, pricing, retention, or another underlying driver. That makes the model useful for decision-making rather than simply producing a set of financial projections.
How Startup Assumptions Flow Through the Model
Operating assumptions are inputs. The financial results they produce are outputs of the model.
| Area | Example Assumptions | What They Drive |
|---|---|---|
| Customer Acquisition | Leads, CAC, conversion rate | New customers |
| Revenue | Pricing, usage, retention | Revenue growth |
| Hiring | Roles, hiring dates, salaries | Payroll and capacity |
| Cost of Revenue | Hosting, fulfillment, transaction costs | Gross margin |
| Operating Expenses | Marketing, software, professional fees | Operating costs |
| Cash | Payment terms, collections, financing | Burn and runway |
The purpose of the model is not to make these assumptions look precise. It is to show how changes in the underlying business drivers affect the financial outcome.
Build From Business Drivers, Not Target Growth Rates
One of the easiest ways to build a startup forecast is to take the latest revenue figure and apply a growth rate. If revenue is €1 million today, assume 50% growth next year, 40% the year after, and 30% after that.
The math works. The financial model does not tell you much.
A growth rate describes the result you expect, but not what needs to happen inside the business to produce it. For a SaaS startup, 50% revenue growth could require more customers, higher pricing, better retention, expansion revenue, or some combination of all four. Each scenario has different implications for marketing spend, sales capacity, hiring, margins, and cash.
A driver-based financial model starts one level deeper. Instead of entering revenue growth directly, it identifies the operating variables that create revenue and models how those variables are expected to change.
For example:
Marketing Spend ÷ CAC = New Customers
New Customers + Existing Customers − Churned Customers = Active Customers
Active Customers × Average Revenue per Customer = Revenue
Revenue growth is then an output of the model rather than an assumption entered into it.
The same logic should extend beyond revenue. A startup expecting to triple its customer base may need additional sales, customer success, infrastructure, or delivery capacity. Those requirements affect payroll and cost of revenue, which in turn affect cash burn and runway.
This is why the assumptions in a startup financial model cannot be built independently. The revenue forecast needs to be operationally consistent with the resources required to deliver it.
Top-down assumptions can still be useful as a reasonableness check. Market size, sector growth, and comparable-company performance can help test whether the resulting forecast is plausible. But they should not replace the operating logic behind the forecast.
For most early-stage startups, the more useful question is not “What growth rate should we assume?” It is “What needs to happen in the business for that growth rate to occur?”
Target-Driven vs. Driver-Based Financial Modeling
Both approaches can produce a complete financial forecast. The difference is whether the model starts with the result or with the operating conditions required to produce it.
- Revenue Revenue grows 50%
- Customers Customers grow 40%
- Payroll Payroll grows 25%
- Gross Margin Gross margin reaches 75%
- Funding Raise €5M
- Revenue Customers × Pricing × Retention → Revenue
- Customers Acquisition Activity × Conversion → Customers
- Payroll Roles × Hiring Dates × Compensation → Payroll
- Gross Margin Revenue − Delivery Costs → Gross Margin
- Funding Burn + Cash Buffer + Milestones → Funding Need
Target-driven models start with the financial result. Driver-based models start with the operating conditions required to produce it. The resulting growth rate becomes an output of the model rather than an assumption entered into it.
Revenue Assumptions Should Reflect How the Business Actually Sells
Revenue is usually the most scrutinized part of a startup financial model, but there is no universal formula for forecasting it. The right assumptions depend on how the company acquires customers, how those customers pay, and what happens to them over time.
For a subscription business, revenue might be driven by the number of active customers, average revenue per customer, new customer acquisition, churn, and expansion. A sales-led B2B company may need to model sales capacity, pipeline generation, win rates, contract values, and sales-cycle length. A marketplace might instead depend on buyers, transaction frequency, average transaction value, and the platform’s take rate.
The important point is that the revenue model should mirror the commercial mechanics of the business.
For example, a SaaS startup might model:
Opening Customers + New Customers − Churned Customers = Ending Customers
Average Active Customers × ARPU = Revenue
A sales-led B2B startup could instead start with:
Sales Capacity × Qualified Opportunities × Win Rate = New Customers
New Customers × Average Contract Value = New Bookings
And for a marketplace:
Active Buyers × Transactions per Buyer × Average Transaction Value = GMV
GMV × Take Rate = Revenue
These models can produce very different revenue trajectories even when the companies are targeting the same headline growth rate. More importantly, they make it possible to identify what must actually improve for the forecast to be achieved.
A startup projecting faster growth may need more leads, additional sales capacity, higher conversion, better retention, increased pricing, greater customer usage, or a combination of several drivers. Each assumption should therefore have an operating explanation rather than exist simply because it produces the desired revenue curve.
This is also why historical growth rates become less useful when the business itself is changing. A new pricing model, sales team, market, product tier, or customer segment can alter the drivers behind revenue. The financial model should capture those changes directly rather than assuming that past growth continues at a different percentage.
For a deeper breakdown of revenue forecasting methods and how they vary by startup business model, see Finro’s guide to startup revenue projections.
Different Business Models Require Different Revenue Drivers
The revenue forecast should follow the commercial mechanics of the business rather than apply the same growth assumption to every startup.
Customer acquisition, conversion, pricing, churn, retention and expansion.
Sales headcount, rep productivity, pipeline generation, win rate, sales cycle and contract value.
Buyer acquisition, transaction frequency, transaction value, retention and take rate.
A 50% revenue growth target can imply very different operating requirements across these models. The assumptions should explain which business drivers have to change for that growth to occur.
Customer Acquisition and Retention Assumptions Need to Work Together
Customer growth is rarely driven by a single assumption. Increasing marketing spend does not automatically produce a proportional increase in customers, and acquiring more customers does not necessarily produce sustainable revenue if retention deteriorates as the company scales.
A useful financial model connects the full customer journey.
Marketing Spend → Leads → Conversion → New Customers → Retention → Recurring Revenue
Each step introduces an assumption. Marketing spend may determine how much acquisition activity the company can support. CAC or cost per lead affects how efficiently that spend generates demand. Conversion determines how much of that demand becomes customers. Churn and retention determine how long those customers remain and how much revenue they ultimately contribute.
These assumptions also interact. A startup might increase marketing spend aggressively while assuming CAC remains unchanged. That can be reasonable, but it requires an explanation. As companies move beyond their most responsive early customers, acquisition can become more expensive. Conversely, stronger brand awareness, referrals, better targeting, or an improving sales process can increase efficiency.
Retention deserves the same treatment. A model that adds customers every month while applying little or no churn can significantly overstate the size of the customer base and the revenue generated from it. For recurring-revenue businesses, relatively small changes in churn can compound into materially different revenue outcomes over a multi-year forecast.
The model should also account for constraints elsewhere in the business. More leads may require additional sales capacity. More customers may require additional onboarding or customer-success resources. Higher usage may increase infrastructure or service-delivery costs.
This is where a financial model becomes more than a revenue spreadsheet. Customer acquisition assumptions should connect to the resources required to acquire, serve, and retain those customers. If one part of that chain changes, the financial impact should flow through the rest of the model.
Acquisition assumptions should connect the resources invested in growth to the customers and recurring revenue they ultimately produce.
Each step introduces an assumption. Changing acquisition efficiency, conversion, or retention should therefore change the customer base and revenue generated by the model.
Hiring Assumptions Should Follow Operating Requirements
Payroll is often one of the largest expenses in a startup financial model, but headcount should not be forecast simply by increasing the previous year’s payroll by a fixed percentage. Hiring assumptions should reflect the people and capacity required to execute the operating plan.
The connection is relatively straightforward. If the revenue forecast requires more sales capacity, the model should show when additional salespeople need to be hired. If customer growth increases onboarding or support requirements, customer success headcount should respond accordingly. If the product roadmap requires additional development capacity, those engineering hires should appear in the hiring plan.
For example:
Revenue Target → Required Sales Capacity → Sales Hires
Customer Base → Support Workload → Customer Success Hires
Product Roadmap → Development Capacity → Engineering Hires
Once the required roles are identified, the financial model can translate the hiring plan into payroll using assumptions such as start dates, salaries, benefits, payroll taxes, bonuses, and other employment costs.
Roles × Hiring Dates × Fully Loaded Compensation = Payroll
Timing matters. A startup that plans to hire ten people during the year will have a very different cash requirement depending on whether those employees start in January or October. A model that applies an annual payroll growth rate can easily miss this distinction.
Hiring assumptions also need to remain consistent with the revenue forecast. A model should be able to explain how a small sales team supports a large increase in new business, how customer success capacity scales with a growing customer base, or how the product team delivers the roadmap assumed elsewhere in the forecast.
This does not mean every hire needs a separate operational formula. Early-stage companies often have overlapping roles, changing responsibilities, and hiring plans that remain uncertain. The objective is to make the material headcount assumptions consistent with the operating plan and explicit enough to understand their impact on payroll, cash burn, and runway.
Hiring Should Follow the Capacity the Business Requires
Headcount assumptions should connect the operating plan to the people required to execute it, and then translate those hires into payroll and cash requirements.
Roles × Hiring Dates × Fully Loaded Compensation = Payroll
Cost and Margin Assumptions Need an Operating Explanation
Cost assumptions should follow the same logic as revenue and hiring assumptions. Rather than increasing expenses by a fixed percentage each year, the model should identify what causes those costs to change.
A useful starting point is to separate cost of revenue from operating expenses. Cost of revenue includes the costs directly associated with delivering the product or service, such as cloud infrastructure, payment processing, third-party APIs, implementation, fulfillment, or delivery personnel. Operating expenses cover the broader resources required to run and grow the company, including sales and marketing, software, professional services, office costs, and other overhead.
The distinction becomes particularly important when forecasting gross margin.
A model that assumes gross margin increases from 50% to 75% over three years may produce attractive economics, but the improvement needs an operating explanation. It could come from lower infrastructure costs per customer, higher pricing, better supplier terms, increased automation, a shift toward higher-margin products, or greater utilization of existing delivery capacity.
Without one of those changes, margin expansion is simply another target entered into the model.
Cost assumptions also do not all behave the same way. Some costs scale directly with revenue or transactions. Others increase in steps as the business reaches certain levels of activity. Some remain relatively fixed for a period before additional capacity is required.
For example, payment processing may remain a relatively stable percentage of transaction volume, while cloud costs per customer could decline with scale. A software contract might remain fixed until the company crosses a usage threshold, while professional-service costs may be tied to specific projects rather than revenue.
Modeling these relationships separately makes it easier to understand operating leverage. If revenue grows faster than the costs required to support it, margins improve. If delivery costs, infrastructure, or other variable expenses scale at roughly the same rate as revenue, the model should not assume substantial margin expansion without a specific reason.
The objective is not to forecast every expense with a complex formula. It is to identify the costs that materially affect the economics of the business and make the assumptions behind their behavior explicit.
Different Costs Behave Differently as a Startup Scales
Cost assumptions should reflect what actually causes an expense to change. Some costs move with activity, some increase when additional capacity is required, and others are driven primarily by planned commitments.
Variable Costs
Move broadly with revenue, customers, transactions, usage, or another operating activity.
Payment Processing → Transaction Volume
Step / Capacity-Based
Remain relatively stable until the business reaches a level that requires additional capacity.
Infrastructure Capacity → Usage Threshold
Fixed / Planned Costs
Change mainly because of contracts, management decisions, projects, or other planned commitments.
Software & Professional Fees → Contracts / Plans
Higher pricing, lower unit delivery costs, better supplier terms, automation, product mix, or greater utilization can improve gross margin. Margin expansion should result from changes like these rather than being entered as a standalone target.
Cash and Funding Should Be Outputs of the Operating Plan
Cash burn and funding requirements should be consequences of the operating plan, not numbers chosen independently from it.
Once the financial model connects customer growth to revenue, hiring to payroll, and operating activity to costs, those assumptions determine how much cash the company is expected to generate or consume each month.
The basic relationship is:
Beginning Cash + Cash Inflows − Cash Outflows = Ending Cash
For most early-stage startups, however, the important question is not simply what the ending cash balance will be. It is when the company approaches its minimum acceptable cash balance and what needs to happen before that point.
This is where runway and funding requirements become part of the model.
A startup expecting faster growth may need to invest in sales, marketing, product development, or hiring before the associated revenue arrives. Customer payment terms can delay cash collection even after revenue is recognized. Annual contracts paid upfront can have the opposite effect, bringing cash into the business before all of the revenue is recognized.
These timing differences mean that profitability and cash are not interchangeable. A company can show improving revenue and margins while still requiring additional financing to execute its plan.
The funding requirement should therefore emerge from the projected cash balance, the minimum cash buffer the company wants to maintain, and the milestones it expects to reach before raising again.
Projected Cash Burn + Minimum Cash Buffer + Capital Required to Reach the Next Milestone = Funding Requirement
This also makes the fundraising assumption easier to evaluate. Instead of deciding that the company wants to raise €5 million and building the model around that amount, the model can show why €5 million is required, how long it is expected to last, and what the company expects to achieve with the capital.
That connection between operating assumptions, cash runway, and milestones is what makes the financial model useful for fundraising planning rather than simply presenting a funding target.
Funding Needs Should Emerge From the Operating Plan
Revenue, hiring, costs, and cash timing determine how much capital the business consumes, how long that capital lasts, and what the company needs to raise to reach its next milestone.
Payment terms, upfront collections, working capital, capital expenditure and financing activity can cause cash movement to differ materially from revenue and profitability shown on the income statement.
How to Tell Whether an Assumption Is Defensible
How to Tell Whether an Assumption Is Defensible
A financial model assumption does not need to be certain to be defensible. Early-stage startups rarely have enough historical data to prove exactly what customer acquisition, retention, pricing, hiring, or margins will look like several years into the future.
The more useful test is whether there is a reasonable basis for the assumption and whether the rest of the financial model is consistent with it. Four questions provide a practical way to evaluate that.
What Is Driving It?
What causes this assumption to change?
Every material assumption should have an operating explanation. If conversion improves, something in the sales process should support it. If gross margin increases, pricing, product mix, infrastructure costs, or delivery efficiency should explain the change.
What Supports It?
What evidence provides a basis for the number?
Historical performance is often the strongest starting point when relevant data exists. Earlier-stage companies may instead rely on pilots, contracts, pipeline data, pricing tests, supplier quotes, hiring plans, market benchmarks, or comparable companies.
Does It Work With the Rest of the Model?
What else needs to change if this assumption is true?
Rapid customer growth may require more sales or support capacity. Faster hiring increases payroll and cash burn. Higher transaction volume can increase infrastructure or processing costs. Assumptions need to work together as a system.
What Happens If It Is Wrong?
How much does the financial outcome depend on it?
Material assumptions should be tested rather than treated as fixed. If a small change in conversion, churn, pricing, hiring timing, or gross margin materially changes runway or funding needs, that sensitivity should be understood.
Not all support carries the same weight. A signed customer contract, observed conversion data, or an actual supplier quote generally provides a stronger basis for an assumption than a broad industry benchmark. The model should reflect that difference in confidence.
Defensible Does Not Mean Certain
A defensible assumption has an identifiable driver, a reasonable basis in evidence, consistency with the rest of the model, and a clear way to test the financial impact if the outcome differs from the forecast. A five-year startup forecast will almost certainly change. The objective is to make the assumptions transparent enough to understand why the forecast changes when the business does.
Seven Assumptions That Commonly Make Startup Models Hard to Defend
Even when the individual formulas in a financial model are correct, the forecast can become difficult to defend if the assumptions behind those formulas are disconnected from how the business is expected to operate.
Several assumptions appear repeatedly in early-stage startup models.
1. Revenue Grows at a Fixed Percentage
Assuming revenue grows by 50%, then 40%, then 30% may create a reasonable-looking curve, but it does not explain what produces that growth.
Replace the growth-rate assumption with the relevant operating drivers: customer acquisition, conversion, pricing, retention, usage, sales capacity, or another variable appropriate to the business model.
2. Customer Acquisition Efficiency Never Changes
A model may increase marketing or sales investment significantly while keeping CAC, conversion rates, or sales productivity unchanged.
That may happen, but it should not be automatic. New channels, larger customer segments, geographic expansion, increasing competition, or changes in the sales team can all affect acquisition efficiency. The model should explain why efficiency remains stable, improves, or deteriorates as the company scales.
3. Churn Is Too Low or Disappears
Small changes in retention can materially change the size of the customer base over a multi-year forecast. Assuming almost every customer remains indefinitely can therefore produce substantial revenue growth without requiring equivalent new customer acquisition.
Retention assumptions should reflect observed behavior where possible and should be tested carefully when historical cohorts are still limited.
4. Headcount Grows Independently of the Operating Plan
Increasing payroll by a fixed annual percentage ignores when and why people need to be hired.
Sales capacity should be consistent with the revenue plan. Customer success and support should reflect the customer base. Product and engineering hiring should reflect the development plan. The model does not need a formula for every employee, but material hiring assumptions should have an operating rationale.
5. Gross Margin Improves Automatically
Margin expansion is common in startup forecasts because scale is expected to create operating leverage. But scale alone does not guarantee better gross margins.
Improvement may require lower infrastructure costs, automation, better supplier terms, higher pricing, greater utilization, or a shift in product mix. If none of those drivers changes, a rising gross margin becomes difficult to explain.
6. Expenses Grow More Slowly Than the Business Without a Reason
A startup may forecast rapid revenue or customer growth while keeping operating expenses nearly flat. Sometimes this is reasonable, particularly where existing capacity can support additional volume.
But the model should identify where that operating leverage comes from. Otherwise, the forecast can simultaneously assume aggressive growth and unusually limited investment in the resources required to support it.
7. The Model Is Built Backward From a Funding or Valuation Target
One of the most problematic approaches is deciding the desired outcome first and adjusting the assumptions until the model produces it.
A startup may decide it needs a €50 million valuation, €20 million of revenue in five years, or a €5 million funding round and then reverse-engineer the forecast around that number.
Targets can be useful for planning and comparison, but they should not determine the operating assumptions. The model should show what the business is reasonably expected to achieve under its assumptions, and the resulting revenue, cash requirements, and valuation implications should follow from there.
Seven Assumptions That Commonly Make Startup Models Hard to Defend
A financial model can be mathematically correct and still produce a weak forecast. The problem is often not the formulas themselves, but assumptions that are disconnected from how the business is expected to operate.
Fixed Revenue Growth
Revenue grows by 50%, then 40%, then 30%, but the model does not explain what produces that growth.
Better Approach Build revenue from the relevant drivers, such as customer acquisition, conversion, pricing, retention, usage, or sales capacity.
Constant Acquisition Efficiency
Marketing or sales investment increases substantially while CAC, conversion rates, or sales productivity remain unchanged.
Better Approach Explain how channels, customer segments, competition, geography, or sales capacity affect acquisition efficiency as the company scales.
Unrealistically Low Churn
Customers accumulate over time with little or no churn, causing the customer base and recurring revenue to compound aggressively.
Better Approach Use observed retention where possible and test alternative churn assumptions when historical customer cohorts are still limited.
Headcount Without Capacity Logic
Payroll increases by a fixed percentage without showing when people need to be hired or what operating requirement creates the need.
Better Approach Connect material hires to sales capacity, customer support, product development, operations, and the timing of the operating plan.
Automatic Margin Expansion
Gross margin improves every year because the company is expected to scale, without identifying what changes the underlying unit economics.
Better Approach Tie margin improvement to pricing, automation, supplier terms, product mix, infrastructure efficiency, or greater utilization.
Expenses Lag Growth Without Explanation
Revenue or customers grow rapidly while operating expenses remain almost flat, creating substantial operating leverage by default.
Better Approach Identify which existing resources can support additional scale and where growth will require incremental investment or capacity.
Reverse-Engineered Outcomes
The company chooses a revenue, funding, or valuation target first and adjusts the assumptions until the financial model produces that result.
Better Approach Use targets as planning or comparison points. Let operating assumptions determine the resulting revenue, cash requirements, and valuation implications.
Most weak assumptions start with a financial outcome and work backward. A stronger model starts with the operating conditions the company expects to face and allows the financial outcome to emerge from them.
From Assumptions to a Working Startup Financial Model
A startup financial model becomes useful when the assumptions discussed throughout this guide are connected rather than modeled as separate forecasts.
Customer acquisition and retention determine the customer base. Pricing and usage translate those customers into revenue. The operating plan determines the people and resources required to support that growth. Those decisions create payroll, cost of revenue, and operating expenses, which ultimately determine cash burn, runway, and funding requirements.
The result should be an integrated model in which changing a material assumption flows through the financial statements and cash forecast.
For example, reducing the conversion rate should not only lower projected revenue. It may also change the timing of sales hiring, marketing efficiency, cash burn, runway, and the amount of capital required to reach the next milestone.
That is what makes a financial model useful for decision-making. It allows management to test how changes in the business affect the financial outcome rather than treating the forecast as a fixed plan.
The model should also evolve as the company generates new information. Actual customer acquisition, retention, pricing, hiring, and cost data can replace earlier assumptions, while the differences between forecast and actual performance can help management understand where the operating plan is changing.
There is no requirement for a startup financial model to predict the next five years precisely. Its value comes from making the relationship between operating decisions and financial outcomes explicit, measurable, and easy to update.
Need a financial model built around your actual business drivers?
Finro builds financial models for early-stage tech companies, connecting revenue, hiring, costs, cash runway and fundraising assumptions into one integrated model.
- 1 Financial projections should be outputs of operating assumptions, not standalone targets. Revenue, margins, payroll, cash burn and funding requirements should emerge from the underlying drivers of the business.
- 2 Revenue assumptions should reflect how the company actually acquires, converts and retains customers. The relevant drivers depend on the business model and may include customer acquisition, conversion, pricing, retention, sales capacity, usage or transaction volume.
- 3 Hiring, costs and margins need to remain consistent with the operating plan. Customer growth may require additional sales, support or delivery capacity, while margin improvement should result from identifiable changes in pricing, efficiency, product mix or unit economics.
- 4 A defensible assumption does not need to be certain. It should have an identifiable driver, a reasonable basis in evidence, consistency with the rest of the model and a clear way to test the financial impact if the outcome changes.
- 5 Cash runway and funding requirements should follow from the operating plan. A useful financial model shows how much capital the company needs, how long it should last and what operating or commercial milestones the business expects to reach with that capital.
