Using Excel for Business Analysis
Danielle Stein Fairhurst · 2012 · 316 pages
This is a financial modelling how-to for corporate analysts, built entirely around Excel 2007/2010 mechanics: cell referencing, WACC, depreciation schedules, data tables. It has nothing to do with picking stocks or timing markets, and readers expecting an investing book will be lost inside chapter one.
The one idea
The book's real argument is that a financial model is not a spreadsheet. A spreadsheet is any collection of numbers and formulas; a model is a structured tool built to answer a specific business question, with inputs, calculations, and outputs kept deliberately separate so someone other than the builder can trust and audit it. Fairhurst spends the first three chapters on this distinction because, in her experience, bad model design (not bad Excel skill) is what makes models fail.
From there the book is a toolbox: the aggregation and lookup functions (SUMIF, VLOOKUP, INDEX/MATCH), the specific formulas for escalation, depreciation (SLN, DB, DDB, SYD), weighted average cost of capital, break-even points, and payback periods, then the display and stress-testing layer on top (conditional formatting, data tables, Scenario Manager, form controls). None of it is proprietary insight. All of it is standard corporate finance and Excel mechanics, documented with worked numbers.
The organizing principle repeated throughout is 'document your assumptions, link don't hard-code, enter data once.' It is a discipline book for people who build models other people rely on (analysts, consultants, FP&A staff), not a strategy book for people trying to beat a market.
What it actually teaches
The book's method is really a checklist for building an auditable model, not a sequence for making a decision, so here are its core components in the order the book presents them.
SEPARATE INPUTS, CALCULATIONS, AND RESULTS
Keep hard-coded inputs, working calculations, and final outputs in distinct sections or worksheets, colour-coded so a user always knows which cells are safe to change. The book calls this one of its 'golden rules' of model design, alongside using one consistent formula per row or column and referencing left-to-right, top-to-bottom to avoid circular logic.
BUILD THE CORE CALCULATIONS WITH THE RIGHT FUNCTION
The book walks through the actual formulas for common finance problems: WACC as (Debt / (Debt+Equity) x Cost of Debt x (1 - Tax Rate)) + (Equity / (Debt+Equity) x Cost of Equity); break-even units as fixed cost / (price - variable cost); payback period estimated with a close-match HLOOKUP or LOOKUP against cumulative cash flow; depreciation via SLN, DB, DDB, or SYD depending on whether the asset should write off evenly or faster in early years.
STRESS-TEST WITH SCENARIOS AND SENSITIVITY TABLES
Once the model works, run best-, base-, and worst-case scenarios (the book recommends three as a minimum and roughly a dozen as a practical ceiling) using one of three Excel mechanisms: manual drop-downs (data validation or combo boxes), Scenario Manager, or one- and two-variable data tables. The book is candid that Scenario Manager is 'quite limited in its functionality and not particularly helpful in large and complex financial models,' and that data tables cap out at two inputs and one output.
PRESENT THE OUTPUT, NOT THE MECHANICS
The final chapter argues most modellers over-invest in build quality and under-invest in the presentation layer: charts, sparklines, colour scales, and a clean summary page, because the audience for a model (senior managers, clients) cares about the result and the scenario range, not the formulas underneath.
What it looks like Monday morning
A reader who builds financial models for a living (budgets, project business cases, valuation workups) walks away with a concrete checklist: separate the input cells with a distinct colour, replace any hard-coded number that appears twice with a link, add a data validation drop-down for scenario switching instead of saving three copies of the file, and use SUMIF($A$4:$A$6,$B$1,B4:B6)-style formulas to drive scenario-dependent inputs.
For anyone hoping this is a trading or investing book, Monday morning brings nothing. There is no security selection, no position sizing, no market analysis. The only market-adjacent content is generic project evaluation math (NPV, IRR, WACC, payback) applied to internal capital projects, not to buying stocks.
| Method | Excel function | Year 1 charge | Behavior |
|---|---|---|---|
| Straight-line | SLN | $4,500 | Same charge every year; does not stop automatically at full depreciation |
| Fixed declining balance | DB | $4,500 (rate applied to book value) | Faster write-off early, book value never reaches zero |
| Double declining balance | DDB | $10,000 (20% of cost) | Doubles the straight-line rate, switches to straight-line once it would give a smaller charge |
| Sum-of-the-years-digits | SYD | Varies by year fraction | Accelerated but still linear total; charge decreases every year by a fixed amount |
All four methods depreciate the same $45,000 base but allocate it differently across the 10 years, which changes reported profit and tax timing in each period.
Scenario Manager is quite limited in its functionality and is not particularly helpful in large and complex financial models.Danielle Stein Fairhurst, Using Excel for Business Analysis
Where it fails
- It is not an investing book at all. Every worked example is corporate: birdcage manufacturing break-even, a property development feasibility model, a personal mortgage budget, a staff cost forecast. There is no coverage of portfolio construction, valuation of public equities, technical analysis, or trading of any kind. A reader who picked this up expecting market content bought the wrong book.
- The whole book is version-locked to Excel 2007/2010. Instructions are given step by step through specific ribbon menus ("click the What-if Analysis icon in the Data Tools section"), ADD-INS dialogs, and Excel-2003 addenda hosted on a companion website. Excel has shipped several UI overhauls and new native functions (XLOOKUP, dynamic arrays, LET, Power Query) since 2012, and none of that is here. Someone using a current version of Excel will find some menu paths simply don't exist anymore.
- The worked numbers are illustrative, not tested against real outcomes. Every example (the WACC of 9.78 percent, the birdcage break-even of 2,944.41 units, the property development margins of 24 to 31 percent) is a constructed teaching case with round, convenient inputs, not a real company's data. There is no discussion of what happens when actual data is messy, missing, or contradicts the model's assumptions, which is the normal condition in real corporate finance work.
- It assumes an audience that already has a spreadsheet job. The book presupposes the reader is already building models for an employer or client, understands basic accounting terms (P&L, balance sheet, fixed assets) going in, and has Excel installed with admin rights to enable the Developer tab and macros. It is not written for someone learning either finance or Excel from zero; it is a mid-career reference for practicing analysts.
- Some content is padding relative to what a modern reader needs. Long step-by-step click sequences (18 numbered steps to build a two-variable data table, separate instructions for Mac 2011 and Excel 2003 throughout) take up space that could have been compressed into a handful of principles. The book is thorough to the point of being a manual, which slows down anyone who just wants the formula.
Who it is for
Buy it if
You are an analyst, FP&A professional, or consultant who builds Excel models for internal business decisions (budgets, project evaluations, scenario planning) and want a structured reference on model design discipline and the standard finance formulas (WACC, depreciation, break-even, payback) with worked Excel steps.
Skip it if
You came here for anything related to investing, trading, or portfolio management. This is a corporate financial modelling manual with zero market content, and it is also tied to a decade-old version of Excel, so anyone wanting current best practice on lookups or dynamic arrays should look elsewhere too.
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