Somewhere near the start of every serious conversation, a brand asks the only question that matters: can we hit our number? And across this industry, the standard answer is a confident yes, delivered before anyone has opened the account, priced against a retainer, and quietly forgotten by Q3. We think that answer is the original sin of Amazon services, responsible for most of the disappointment that follows. So we built a different answer. It's a model, it takes real work before a penny changes hands, and it ends with an invitation we mean literally: here are all of our assumptions, change any of them, break it if you can.
This piece opens the method up, because we'd rather the whole market raised its standard than keep the discipline as a trade secret. A simplified interactive version lives on our site; here's the full thinking behind it.
Principle one: every number comes from your account
The model starts where every honest forecast has to: in your own Seller Central, Ads console and Brand Analytics data. Real conversion rates, real traffic, real repeat behaviour, real fee stacks per product. Nothing benchmarked from "industry averages", because industry averages are how projections get built out of other people's businesses. And where a number genuinely can't be known yet, your exact landed costs, a fee awaiting confirmation, it goes into the model as a visible assumption, not a buried guess. In our workbooks those cells are literally coloured yellow: this is assumed, replace it with your actuals, and watch everything recalculate. The yellow cell rule is small and it changes the entire relationship, because there's nowhere for a bad number to hide.
| Input (a yellow cell) | What usually breaks it | The stress test |
|---|---|---|
| Average selling price | Discounts and deal weeks quietly lower it | Rerun the year at the promoted price |
| Landed cost per unit | Freight spikes and supplier increases | Add 10% and see if the year survives |
| FBA fee per unit | Size-tier changes and annual fee updates | Check the tier, then rerun at the new rate |
| Monthly growth rate | Optimism | Halve it; the plan should still be worth running |
| TACoS trajectory | Competition arriving on your terms | Hold TACoS flat instead of falling and re-read the profit line |
Principle two: month by month, or it's a hockey stick
Annual targets hide their own impossibility. £500k "next year" sounds plausible right up until you distribute it across twelve months against seasonality, stock arrival dates, review accumulation and the compounding lag of rank, at which point many targets reveal themselves as requiring a miracle in Q4. So the model builds monthly: what traffic, at what conversion, at what price, with what TACoS against each product's breakeven, has to be true in March for the March number to happen. Month-by-month construction is how you find the difference between a target that's ambitious and one that's arithmetic fiction, before the year starts rather than after it ends.
Principle three: three scenarios, honestly labelled
Every model we ship carries three cases, and the labels are commitments, not moods. The conservative case is what we're prepared to commit to, stress-tested against every cost assumption. The base case is what we plan against, the version resourcing and stock decisions are built on. The stretch case is what proper investment unlocks, priced so the cost of ambition is visible next to its reward. One number is a pitch. Three numbers with stated assumptions is a decision-making tool, and the gap between them tells you exactly how much of the plan is method and how much is hope.
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Get the free audit at rankhouse.co.uk →Principle four: levers, not vibes
Inside the model, growth is never a curve someone drew. It's the output of named levers, each with its own cell: pricing and price architecture, conversion work on the tile and the page, review velocity, subscription capture, ad efficiency against per-product breakevens, stock availability. Pull any lever and the whole model recalculates, which converts strategy debates into arithmetic: "what if we fund the higher Subscribe & Save tier?" stops being an opinion exchange and becomes a cell change with a visible consequence. It also makes accountability automatic, because when a month misses, the model shows which lever underperformed the assumption, and the conversation is about mechanics rather than blame.
What "break it if you can" actually buys you
When we hand a model over, the invitation is genuine: change the yellow cells, stress the costs, halve the conversion assumption, see what survives. Two outcomes, both good. Either the model holds under your interrogation, in which case the plan has earned real confidence before a pound is spent, or you find a weakness, in which case we've located the plan's true risk while it's still cheap to fix. Compare that with the industry standard, where the first stress test the plan ever meets is reality, in November, with the year's budget already gone.
The Qualkem account ran on exactly this discipline: a true per-product P&L first, targets modelled against it, levers worked weekly, and the results, profit growing three to four times faster than revenue, are what the method looks like compounding. Not magic. Arithmetic, maintained.
If someone's currently promising you a number, ask to see the month-by-month model underneath it, with the assumptions exposed and the scenarios labelled. If it doesn't exist, you've learned what the promise weighs. And if you'd like to see what your targets look like inside a real model, that's precisely what the free audit at rankhouse ends with: your goals, stress-tested against your own account's data, with every assumption in a yellow cell where it belongs. Break it if you can. If you can't, we'll build it together.
Questions we get asked about this
What exactly is a 'yellow cell' in practice?
A visible confession: any number the model assumes rather than knows, coloured yellow in the workbook so it can't hide, labelled with what it is and what actual should replace it. Landed costs awaiting your confirmation, a fee pending re-measure, a conversion assumption on an untested price point, all yellow until replaced, and the model recalculates the moment truth arrives. The rule sounds cosmetic and changes the entire relationship, because it makes the forecast auditable: you can see precisely which conclusions rest on facts from your account and which rest on assumptions we've declared, and there is nowhere for a convenient guess to hide inside a formula. Models without the convention aren't necessarily dishonest. They're just unfalsifiable, which for planning purposes is worse.
Why three scenarios instead of one forecast?
Because one number is a pitch and three numbers with stated assumptions are a decision tool. The labels carry commitments: the conservative case is what we're prepared to commit to, stress-tested against every cost assumption; the base case is what we plan against, the version stock and resourcing decisions are built on; the stretch case prices what proper investment unlocks, so ambition's cost sits visibly next to its reward. The gap between the cases is itself information, how much of the plan is method versus hope, and the discipline prevents both classic failures: promising the stretch case to win the work, and sandbagging the conservative case to guarantee applause. You see all three, with the levers that separate them.
What happens when a month misses the model?
The most useful conversation in the engagement, because the model makes it mechanical rather than emotional: a miss decomposes into which lever underperformed its assumption, traffic, conversion, price realisation, TACoS, stock availability, and the response follows from the mechanism. A conversion shortfall points at the tile and the page; a TACoS breach points at targeting or the auction; a stock gap points at the cover plan. Sometimes the finding is that an assumption was wrong, and the yellow cell gets corrected and the year re-forecast honestly rather than heroically. What the model deletes is the standard industry ritual: a narrative explanation, a hopeful next month, and a December surprise. Misses become diagnosis, and diagnosis becomes the next week's work.
Can I really break the model, and what if I do?
Genuinely, and finding a break is a good outcome for both of us. Change the yellow cells, halve the conversion assumption, stress the costs, delay the stock, and watch what survives. If the conservative case holds under your worst honest inputs, the plan has earned confidence before a pound is spent, which is the entire point of doing this before engagement rather than after. If you find a weakness, an assumption the plan quietly leans on, a scenario where the economics fold, we've located the true risk while it costs a conversation to fix rather than a quarter to survive. The alternative, industry-standard sequence runs the first stress test in live trading with your budget as the instrument. We'd rather break spreadsheets.
Do you really refuse to promise numbers before the model exists?
Yes, and it costs us exactly the clients we'd rather not have. A number promised before anyone has opened your account is a projection built from someone else's business, and the promiser knows it; the confident first-call yes is this industry's original sin, priced into a retainer and quietly forgotten by autumn. What we'll say before the model exists is what we can defend: how the method works, what comparable situations have produced, and what the free audit will establish from your own data. The commitment comes after the arithmetic, in the conservative case, where it means something. If a competitor's confidence arrives faster than their spreadsheet, you've learned what the confidence weighs, and it's the cheapest lesson in the process.
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About the author
Daniel Whalley is the founder of rankhouse, a boutique specialist agency for Amazon-focused growth in FMCG, health, wellness and beauty brands. He has spent 10 years inside Amazon accounts, generating £100M+ for the brands he works with, and manages £500k+ a month in ad spend across the UK, Europe and the US. He writes from inside the accounts he runs, not from the sidelines. Connect on LinkedIn → · amazon@rankhouse.co.uk