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A Practical Guide That Actually Works – Part 3.
Financial calculations, scenario planning, and model formatting are the core focus of this part of the series on building a practical economic model. At this stage, we move from structure to implementation — integrating formulas, input data, scenario logic, and managed assumptions. Without these elements, the model remains a static framework and cannot function as a decision-making tool.
In the first part, we established that a financial model is a management tool reflecting the core assumptions of the business — not just a set of calculations. We defined the objectives of financial modeling, reviewed common mistakes, and laid the foundation: core components, quality criteria, and the model’s role in supporting strategic decisions.
The second part focused on structuring the model: breaking it down into logical blocks like revenue, costs, investments, and taxes; defining the planning horizon and time steps; ensuring internal consistency; and aligning the model with the real economics of the business.
Now that the model architecture is in place, we move to the next stage — populating the model with calculations, scenario, and data. This is the point where the model becomes a functional tool capable of simulating business scenarios, calculating key financial indicators, and supporting management decisions.
In this part, we will cover the following steps:
Step 10. Structured financial logic and calculation dependencie
Step 11. Appropriate level of detail and granularity
Step 12. Scenario planning: The baseline is only the beginning
Step 13. Financial model format and structure: Fit for purpose
Step 14. Inputs and assumptions: Identifying the key drivers
The goal of this part is to provide a practical framework for turning a static model structure into a fully functional decision-support tool — one that remains adaptable to changing conditions and useful across a wide range of management tasks.
A financial model is not just a collection of numbers — it is a system of interrelated calculations. Errors in the relationships between key variables can render the entire forecast unreliable or misleading.
The relationship between production and sales: part of the output may be sold immediately, while the rest is allocated to inventory.
The relationship between sales volume and marketing spend: as revenue grows, advertising and PR expenses typically increase as well.
The relationship between headcount and production volume: scaling production often requires additional hiring, which impacts payroll, taxes, and benefits.
Marketing and PR expenses, for example, can be modeled in several ways:
As a percentage of actual revenue from the previous period — this creates a smooth, data-driven dependency based on recent performance.
As a percentage of forecasted revenue in the upcoming period — this assumes marketing spend is incurred in anticipation of future sales.
As cost per acquired customer, if you have reliable data on conversion rates from leads to sales.
Similarly, direct production costs can be modeled as:
Proportional to output volume, if unit costs are constant.
Non-linear, to reflect economies of scale — e.g., bulk purchasing of raw materials can reduce unit costs.
If the business is already operating, use historical actuals as the basis for calculations.
If the project is in the concept phase, use benchmark data from comparable businesses or market-based assumptions.
A financial model must reflect economic reality, not ungrounded hypotheses. Every dependency should have a clear and logical explanation — otherwise, the model’s projections become meaningless.
The depth of a financial model is a critical factor that directly impacts both the accuracy of calculations and the usability of the model. Overcomplicating the model by adding excessive detail is not always the best approach.
Based on the model’s purpose
If the model is intended for operational management, a high level of detail is necessary — e.g., tracking personnel costs at the individual level, or modeling monthly fluctuations in raw material prices.
If the model is built for investor presentations, it should remain clear, intuitive, and focused — without unnecessary complexity — to ensure the visibility of key financial indicators.
Based on available data
When precise data is unavailable, excessive granularity won’t improve accuracy and may create a false sense of control. In such cases, using aggregated or high-level assumptions is a more effective approach.
Construction projects:
You can either model costs based on average price per square meter, or develop a fully itemized budget with dozens of cost lines.
Payroll (personnel expenses):
It can be calculated as a single total for all staff, or broken down by individual employee — including salaries, bonuses, and payroll taxes.
Raw material procurement:
You can apply an average unit cost, or model supplier-specific pricing with foreign exchange adjustments.
The more detailed the model, the more precise the calculations — but also the more complex and time-consuming it becomes to maintain and update.
The key principle: only add detail where it materially impacts the financial outcomes. If additional granularity does not lead to more informed or more accurate management decisions, it is simply a waste of time.
In most cases, a single “baseline” model is not enough. Multiple scenarios are typically developed to capture a broader range of business outcomes:
Optimistic — everything goes according to plan or better.
Base case (realistic) — includes typical market fluctuations, minor delays, or cost variances.
Pessimistic — lower demand, higher procurement costs, supply chain delays, rising interest rates, and other adverse factors.
While these three core scenarios are useful, they are rarely sufficient. Real-world projects often involve a wider range of strategic and operational choices that go far beyond basic sensitivity analysis.
For example:
Production capacity can be built, acquired, or leased.
The project can be implemented in different jurisdictions, each with distinct tax implications.
IT infrastructure can be developed in-house or outsourced to third-party platforms.
Scaling can be achieved by hiring full-time staff or working with contractors and subcontractors.
A well-structured financial model must be a flexible decision-support tool, enabling users to switch between scenarios and evaluate their impact on key financial metrics.
Sample scenario questions:
How does profitability change if production is leased instead of built from scratch?
What is the impact on payback period if outsourced teams are used instead of internal staff?
How much does net income vary under an alternative tax jurisdiction?
Depending on the nature of the project, you may need not three, but 10, 15, or even 20 scenarios. For example, you might want to evaluate:
5 different financing strategies
4 possible locations
3 go-to-market approaches
The more flexible your model is in handling such variations, the more it shifts from being a simple calculation tool to becoming a true strategic decision-making platform.
A financial model should not just “run the numbers” — it should help identify the most efficient and viable path to achieving your business goals. That’s why scenario toggling must be easy, and the resulting analysis should be clear, visual, and decision-oriented.
Step 13. Financial Model Format and Structure: Fit for Purpose
There is no universal format for financial models — the optimal format depends on the objective of the model and the requirements of the end user.
If the model is intended for internal use (e.g., management accounting, performance analysis), it is typically built as a detailed Excel file or in specialized modeling software that supports in-depth parameterization.
If the model is developed for external stakeholders such as investors, clarity and structure become the priority. In such cases, the focus is on clear conclusions, visualized key metrics, and the ability to navigate between scenarios easily.
Regardless of the format, every model should be logically structured and typically includes the following components:
Inputs — key assumptions and parameters: pricing, cost structures, taxes, discount rates, and scenario variables.
Calculations — core logic: revenue projections, COGS, investment and production models, tax computations.
Outputs — standardized financial reports:
Profit and Loss Statement (P&L)
Cash Flow Statement
Balance Sheet
Financial ratios and performance indicators
Analytical tools — charts, graphs, and dashboards to visualize results and facilitate decision-making.
The more complex the business, the more detailed and flexible the model needs to be — particularly with regard to scenario analysis and sensitivity testing.
The more users interacting with the model, the simpler and more intuitive the interface should be. If the model will be used by non-financial users (e.g., executives, investors, operational managers), the emphasis should be on accessibility and clarity.
The model must also be easy to update and maintain. If underlying data changes frequently, the structure should allow for rapid updates without compromising calculation integrity.
A financial model is not just a spreadsheet — it’s a decision-making tool. Its format must align with the specific tasks it is designed to support.
Every financial model is built on a set of input data and assumptions that shape all subsequent calculations. It is essential not just to document variables, but to understand which factors genuinely drive project outcomes.
Revenue Drivers:
Sales price and its projected changes over time
Sales volumes and demand seasonality
Number of customers and conversion rates from marketing to actual sales
Cost Drivers:
Purchase prices for raw materials and components
Logistics, warehousing, and packaging expenses
Operating expenses (rent, utilities, depreciation)
Payroll costs: base salaries, bonuses, payroll taxes, potential indexation
Investment Drivers:
Cost of equipment and its depreciation
Required capital expenditures on infrastructure
Licensing, certification, and legal setup costs
Macroeconomic Assumptions:
Discount rate
Inflation and foreign exchange rate fluctuations
Potential changes in tax regulations
Each variable should be clearly defined as either static or dynamic over the course of the project.
Examples:
Will the procurement price for raw materials remain fixed or increase over time?
Will labor costs be indexed?
How would the tax structure change if the jurisdiction is altered?
In a typical financial model, 200–300 individual inputs and assumptions are documented. For more complex projects, this number can be significantly higher.
Assumptions form the foundation of reliable forecasting.
If incorrect assumptions are built into the model, all projections derived from them will be invalid.
Assumption sensitivity enables risk simulation.
For example, what is the financial impact if procurement prices increase by 15% or if the exchange rate shifts by 20%?
Model transparency.
When all parameters are clearly documented, any user of the model can understand the logic behind the projections and identify which variables are most critical.
A financial model is not just a set of figures—it’s a structured representation of interconnected variables. The more clearly the assumptions and inputs are defined, the more realistic and actionable the financial forecasts will be.