Driver-Based Model Structure

Corporate Financial Modelling

Corporate financial modelling is the discipline of building financial models for operating companies — as distinct from a single asset, project, or development. Nearly every corporate model type is built on the same foundation, a fully integrated three-statement structure, and then specializes that foundation toward a specific purpose: a budget model constrains it to a fixed annual period, a driver-based model rebuilds it from operational units rather than percentage growth, a consolidation model extends it across multiple legal entities and currencies, a management reporting model extracts and re-presents its outputs as KPIs, and a transaction model (a merger model, an LBO) repurposes it to answer a specific capital-structure or ownership-change question. This page is the hub for the Knowledge Centre's corporate financial modelling content: the shared three-statement foundation, how each model type specializes it, and where each mechanic is covered in full technical depth elsewhere on this platform.

Forecast Driver

A forecast driver is a labelled input cell, most commonly a growth rate, a margin percentage, a unit count, or a price, that a forecast formula references rather than embeds directly. It is the structural unit that makes a forecast auditable and sensitizable, because changing the driver cell changes every downstream calculation that depends on it, consistently and traceably. A forecast driver is structurally distinct from a hardcode, a value typed directly into a calculation cell with no traceable source, even where the two produce an identical output in a given period.

Hardcode

A hardcode is a typed value, a number, date, or rate, entered directly into a formula cell rather than derived from a reference to an assumptions tab or another calculated cell. It is one of the most common and most consequential structural risks in Excel financial models, because a hardcoded value does not update when the model's stated assumptions change, silently disconnecting the model's output from its own inputs.

Revenue Forecasting Methods

Revenue can be forecast using several structurally different methods, and the choice of method has a direct effect on how defensible and auditable the resulting forecast is. This guide sets out the four principal methods used in institutional financial models — top-down forecasting from market size and share, bottom-up forecasting from unit economics, trend and growth-rate extrapolation from historical results, and cohort-based forecasting for subscription and other recurring-revenue businesses — with guidance on when each method is appropriate and how the methods can be combined within a single forecast.

Cost Forecasting Methods

Costs cannot be forecast reliably using a single blanket method, because different cost lines behave differently as a business scales. This guide sets out the classification step that should precede any cost forecast — separating fixed from variable costs — followed by the three principal construction methods used in institutional financial models: the percent-of-revenue method for costs that scale proportionally with revenue, driver-based opex build-up for costs tied to a specific operational driver other than revenue, and cost of goods sold construction for the direct costs attributable to production. It is the companion guide to Revenue Forecasting Methods, covering the cost side of the same forecast.

Assumption Design Best Practices

How a forecast's assumptions are designed determines whether the forecast can actually be audited, sensitized, and defended in front of a reviewer, independent of whether the assumed values themselves are reasonable. This guide sets out five construction disciplines for assumption design: separating input cells from calculation formulas, labelling every assumption clearly with its unit, consolidating assumptions onto a dedicated tab, entering each driver once at a single point rather than repeating it, and structuring input cells so they can be sensitized cleanly without breaking the calculations that depend on them.

Budget Model Structure

A budget model is built on the same three-statement mechanics as any other corporate forecast, but its defining discipline is governance rather than formulas: a fixed period, an assumption freeze once the budget is approved, and a variance-tracking structure that compares actuals against that unchanging baseline throughout the period. This guide covers how to structure a budget model correctly — top-down and bottom-up build methods and when each is appropriate, the assumption freeze and formal change-control process that distinguishes a budget from a forecast, and how the variance schedule should be built so that a variance is explained by its driver, not just its size.

Scenario Planning for Forecasting

Building a base, upside, and downside case is a planning and governance process, distinct from the Excel mechanics used to implement a scenario switch. This guide covers that process: how to define a coherent set of driver changes for each case, how to govern which assumptions are allowed to move between cases and by how much, how to document the rationale behind each case so it can be defended to a reviewer, and how the process relates to the underlying switch-cell mechanism that makes the resulting cases operable inside the model.

Sensitivity Analysis

Sensitivity analysis is the quantitative assessment of how much a financial model's output changes when a single input variable is changed by a defined amount, while all other variables are held at their base case values. It measures the responsiveness — or sensitivity — of outputs to individual assumption changes. Sensitivity analysis is distinct from scenario analysis, which changes multiple assumptions simultaneously to reflect a coherent alternative state. Sensitivity analysis isolates the effect of individual variables; scenario analysis tests the combined effect of assumption sets.