What data is needed to build a financial model

One of the first questions a client asks is: "What do I need to prepare?" It's the right question, and the answer largely determines the quality of the future model. Below is a systematic list of data, grouped by category. Not every item is required for every project, but understanding the full picture will help you gather information faster and avoid missing anything.

Project description

Before diving into numbers, we need to understand the essence of the business. At this stage, a brief description is enough:

• What the business is. What the company does, what product or service it provides.
• Current stage. A greenfield project, an operating business, or an expansion of an existing one.
• Purpose of the model. Who it's for — a bank, an investor, internal management, or a grant application.
• Planning horizon. Typically 3-5 years for an investment project, 1-2 years for a management model.

This is not a formality. The project description determines the model's structure, level of detail, and the set of metrics that need to be calculated.

Geography and business format

Business location affects dozens of model parameters: price levels, rental rates, salaries, tax regimes, and demand seasonality.

• City and area. Rent in a major downtown district versus an outer suburb can differ tenfold. Salaries can vary by a factor of 2-3.
• Premises format. Owned building, lease, or new construction. This determines the CAPEX structure and fixed costs.
• Area. Total and usable floor space — for calculating rent, utilities, and capacity utilization.
• Operating hours. Hours and days of operation, number of shifts — for calculating revenue and payroll.

Sales plan or capacity plan

This is the central data block that the entire model depends on. Depending on the industry, it may look quite different:

• For services: number of clients per day/month, average service fee, repeat visits.
• For retail: number of buyers, average transaction value, traffic-to-purchase conversion rate.
• For manufacturing: production volume in physical units, equipment capacity, ramp-up to design capacity.
• For food service: number of seats, table turnover, average check, delivery share.

If there is no precise sales plan — that's normal. But at least some reference points are needed: target utilization, industry benchmarks, or market research results.

Perfectly prepared data doesn't exist. The purpose of a model is not to plug ready-made numbers into formulas, but to build a logic framework into which refined data can be entered as it becomes available. You can start with hypotheses.

Pricing and average ticket

Pricing is one of the most sensitive parameters in any model. A small price change can radically affect the bottom line.

• Price list or pricing matrix. For each product or service — base price, range, volume-based pricing.
• Average ticket. If the product range is wide, a weighted average by category is sufficient.
• Price dynamics. Are price adjustments planned? How often? Are they tied to inflation, exchange rates, or raw material costs?
• Discounts and promotions. Average discount amount, share of discounted sales.

If the business is new and prices haven't been set yet, market benchmarks are useful: competitor pricing, category price ranges, and potential customer survey results.

Staffing

Payroll is usually one of the largest expense items. The model requires:

• Staffing plan. A list of positions with the number of people in each role.
• Base salaries. Fixed compensation for each position.
• Variable compensation. Bonuses, sales commissions, KPI-based incentives — how they are calculated and what they depend on.
• Hiring timeline. When each employee starts. Especially important for projects with a phased launch.
• Payroll taxes and benefits. These depend on the jurisdiction and compensation levels. Include statutory contributions (social security, health insurance, pension) as well as any applicable tax incentives for tech companies, SMEs, or other qualifying categories.

Rent, utilities, and other fixed costs

Fixed costs are expenses the business incurs regardless of sales volume. These must be known with maximum precision, as they determine the breakeven point.

• Rent. Rate per square foot/meter, total area, escalation terms, security deposit.
• Utilities. Electricity, water, heating, internet, phone. For manufacturing — equipment power consumption.
• Insurance. Property, liability, employee health insurance.
• Accounting and legal services. Outsourced or in-house.
• Marketing and advertising. Monthly budget, breakdown by channel.
• Software. Subscriptions, licenses, CRM, ERP.

Raw materials, supplies, and variable costs

Variable costs are directly tied to sales volume. Their structure varies significantly by industry:

• For manufacturing: bill of materials (BOM) per unit, supplier prices, defect rate, inbound logistics.
• For food service: recipe costing (food cost), ingredient usage, storage losses.
• For retail: purchase prices, markup, logistics to warehouse and to customer.
• For services: consumable materials, payment processing fees, contractor payouts.

The more accurately the unit cost of a product or service is calculated, the more reliable the model. A one-percentage-point error in margin at high volumes translates into tens of thousands of dollars in discrepancy.

Launch investment

For a new project or expansion, a list of startup costs is needed:

• Renovation and construction. Estimates, contractor proposals.
• Equipment. Itemized list with prices, delivery and installation timelines.
• Furniture, fixtures, and signage. Everything needed before opening.
• Licenses and permits. Cost and timeline to obtain.
• Working capital for launch. Funds for the first months of operation while the business hasn't reached breakeven: rent, salaries, initial inventory purchases.

A common mistake is forgetting about working capital. The entrepreneur budgets only for capital expenditures, then discovers there isn't enough cash for operational expenses in the first months. The model must explicitly show the working capital requirement.

The most common cause of cash flow gaps at launch is underestimating working capital. A business can be profitable "on paper," but if there isn't enough cash in the first months for rent and salaries, it won't survive to breakeven. The model must show this clearly.

Loans, leasing, grants, and subsidies

Funding sources are an important part of the model, especially for bank-facing versions:

• Equity. How much the owner contributes, in what form (cash, property, intellectual property).
• Loans. Amount, interest rate, term, repayment schedule (annuity or amortizing), any grace period.
• Leasing. Leased asset, total cost of financing, term, down payment.
• Grants and subsidies. Amount, eligibility conditions, permitted use, timelines.
• Equity investment. Amount, investor's share, entry and exit terms.

If the exact financing terms haven't been determined yet, the model uses market rates and standard terms. This allows you to assess how much debt the business can service, so you can approach a bank with a clear understanding of your capacity.

Taxes

The tax burden can vary significantly depending on the chosen tax regime and jurisdiction:

• Tax regime. Standard corporate taxation, simplified regimes, or special frameworks (e.g., IFRS-based reporting, local GAAP) — each regime affects cash flow differently.
• VAT / Sales tax. Is the company a VAT/sales tax payer? What rate applies to its products? Is there input VAT to offset?
• Property tax. Relevant when real estate or expensive equipment is on the balance sheet.
• Industry-specific taxes. Excise duties, natural resource taxes, environmental fees — these depend on the industry and location.
• Incentives. Regional incentives, special economic zone status, tech company incentives, SME benefits.

Choosing a tax regime is itself an important decision, and a financial model helps make it. We often build two scenarios — under standard taxation and under a simplified regime — and compare the total tax burden across all factors.

What to do when precise data isn't available yet

This is the most common scenario. An entrepreneur comes with an idea but has no precise figures — no confirmed sales plan, no finalized lease terms, no final price list.

And that's perfectly normal. A financial model is a tool for working with uncertainty, not a record of already known facts. Here's how we approach these situations:

1. We use industry benchmarks. The average check for a coffee shop in a major city, typical first-year utilization for a dental clinic, standard food cost for casual dining — these are well-known figures that serve as a starting point.
2. We build from capacity. If the sales plan is unknown, we calculate from maximum throughput and assume a gradual ramp-up to target utilization.
3. We use ranges. Instead of a single price point, we run three scenarios: pessimistic, base, and optimistic. The model shows results for each.
4. We iterate. The first version of the model is built on assumptions. As real data comes in — quotes, lease agreements, early sales — assumptions are replaced with facts.

Why missing data doesn't always prevent starting a model

Paradoxically, it's often the model itself that helps identify which data is truly needed. Until you build the calculation structure, you don't know which parameters are critical and which are secondary.

For example, an entrepreneur might spend a week collecting data on office supply costs, even though that expense is 0.1% of the budget. Meanwhile, they might not think about customer acquisition cost, which drives 30% of all expenses.

The model sets priorities. Through sensitivity analysis, it shows which parameters have the greatest impact on results, allowing you to focus your efforts on gathering exactly that data.

That's why our recommendation is: don't wait until all data is collected. Start with what you have. Fill out the brief with the numbers you know, and mark the rest as assumptions or ranges. We'll figure out what's missing and ask targeted questions.

In practice, 80% of the necessary information can be provided by the client in a single meeting. The remaining 20% is refined during the engagement, once it's clear which parameters are critical for the specific project.