# The Ecommerce Budget Builder > A free Excel ecommerce budget template: weekly on a 4-4-5 calendar, built from three years of trading history, with an assumptions register, three scenarios, every cost line, a top-down versus bottom-up reconciliation, twenty model checks and a back test. No form. Source: https://barkingcat.ai/thinking/ecommerce-budget-builder/ ## Thinking · Budget template · October 2026 A weekly ecommerce budget built from your own trading history, with every assumption on one sheet, the evidence behind it named, and the gap to the board’s number shown rather than buried. Download the workbook: [an Excel model, pre-filled with a fictional retailer so you can see it working before you add your own figures. No form, no email, no gate.](https://barkingcat.ai/assets/barking-cat-ecommerce-budget-builder.xlsx) Every ecommerce budget is a forecast, and every forecast is a punt. The useful question is which assumptions the punt rests on, and whether anyone has written them down. Most budgets do not. Finance takes last year, applies a growth rate, and the ecommerce team spends the next twelve months explaining why the year did not behave like the spreadsheet. This model builds the year the other way round. It starts from how the business actually traded, week by week, and asks what has to change for the plan to land. Then it sets that answer against the number the board has asked for, and shows the size of the gap in pounds. ## What it answers ## Four questions a board should ask of any budget ### 01 · Is the target credible? The bottom-up plan against the board target and a market-implied number, with what you would have to believe to close the gap. ### 02 · What drives profitable growth? What one point of conversion, basket value, returns or discount is worth in contribution, not only in sales. ### 03 · Where should the next pound go? Whether the upside comes from trading better or from spending more, and what each extra pound of media adds to contribution. ### 04 · Can the plan be trusted, and delivered? Twenty checks on the model itself, a stock feasibility test by period, and a back test of the method against last year. From the template’s fictional retailer, not a real business. The sample plan reads: £1.27m Gap to the board target 5.7% Conversion uplift needed to close it alone 6p Contribution per extra £1 of upside media The first two numbers are the conversation most budget rounds avoid. The third is the one they never reach: in the sample, half a million pounds of extra media buys £1.29m of sales and £32,000 of contribution. ## Before you start ## What you need Allow half a day to gather the inputs and an hour to enter them. The model is weekly on a 4-4-5 retail calendar, so weekly exports save the most time. **Write the trading notes.** A week with a site outage, a sold-out hero line or a one-off press spike should not shape next year. The model skips any week you flag, but it can only skip the weeks somebody remembers. ## Using it ## Eight steps, in order - **Settings.** Name the years, set the start date of week 1, choose how much each history year counts, and enter the board target. - **History.** Paste weekly actuals into the blue columns. Flag any week that should not shape the future with a Y, and say why. - **Market.** Category growth, online penetration and share gain, each with a source, a vintage and a check on who paid for the research. - **Calendar.** The budget year’s events. Where an event is new or has moved week, enter an uplift. - **Assumptions.** Base, Upside and Downside for every driver, each with an owner and an evidence type: historical evidence, external benchmark, management judgement or untested hypothesis. - **Fixed costs, media and stock.** One line per fixed cost, any weekly media overrides, and the stock you expect to hold. - **The checks.** Clear every FAIL before reading a single output. A WARN is a question to answer before presenting. - **The Executive Summary.** Then the P&L, the reconciliation and the scenarios. During the year, the weekly tracker. Every sheet is protected so only the blue cells can be edited. There is no password: a finance team that wants to change the model can, and a tired analyst at eleven at night cannot overwrite a formula by accident. ## Method ## How the numbers are built **Rates use a weighted rolling average.** Conversion, AOV, units per order, discount depth, returns rate and media cost per session are each the weighted average of the same week across three years, with flagged weeks left out. Week 12’s conversion rate ignores the year the hero line sold out. **Volumes use a seasonality index.** Averaging raw traffic across three years would understate a growing business, so the model measures each week’s share of its year, averages the shapes, and applies them to last year’s total and your growth assumption. **Paid traffic has diminishing returns.** Sessions are spend divided by cost per session, and cost per session moves with spend against last year, separately for search, social and display. Push spend up and traffic gets dearer. Cut it and the remaining traffic gets cheaper. **From the field.** At one business I ran, we cut paid media by 30%. Topline revenue fell by roughly 20%, and the bottom line was stronger without the spend. A budget that assumes every pound of media returns the same as the average pound would have predicted the opposite. **Discounting shows up in margin.** Cost of goods runs off the full-price value of what is sold, so a deeper promotion gives away more margin on the same sales, and the P&L says so. **Every cost names its driver.** Carriage, packaging and service per order; pick and pack per unit; returns handling per returned order; payment fees and fraud on demand. Nothing is a percentage of sales unless it genuinely behaves like one. ## Outputs ## Reading the Executive Summary The workbook opens on the answer, written as sentences that rewrite themselves as the numbers change: the bottom-up plan, the board target, the gap, and what closing it would take with each lever on its own. If no single lever is defensible in front of the board, the target is either a stretch that needs funding or a number that needs renegotiating. Below the answer, Base, Upside and Downside sit side by side, followed by what each lever is worth per unit and the evidence behind the assumption that drives it. A conversion uplift resting on an untested hypothesis is highlighted, because that is the line most budgets are quietly balanced on. ### Trading or investment A bigger plan can come from trading better or from spending more, and a board deserves to know which. The model runs each scenario’s trading assumptions on Base media, then adds the media change separately. The last column shows what each extra pound of media adds to contribution. Below zero, the extra spend buys sales at a loss. ### The decision block Choose the scenario to approve. The summary shows the media it needs against Base, the contribution change split between trading and media, the gap left to the target, and the trigger points for moving to the Downside plan or releasing the Upside budget. Write the rationale in the box provided. If it does not fit, the decision is not ready. ## Controls ## The checks Twenty tests run every time a number changes. They cover the weights, gaps in history, assumptions outside sensible bounds, returns and discounts beyond plausibility, negative media, every reconciliation between the sheets, the order of the scenarios, whether Black Friday sits in the week labelled Black Friday, stock, and the number of untested assumptions. Each one returns PASS, WARN or FAIL, with what it means and what to do. Open checks appear on the summary as actions. **Stock feasibility** sets opening stock and planned intake against the units the plan sells, by period, and values the sales at risk. It is a check on total units, not a merchandise plan: a shortfall in a hero line can hide inside a healthy total. ### The back test The model rebuilds last year from the three years before it, using the same method as the budget, and compares the result with what happened. Alongside it sits the common shortcut, a compound growth rate. Weekly accuracy matters more than the annual total: a budget that lands on the year but misses the shape of peak still misplaces stock and media. On the template’s fictional history the compound growth rate wins, because the sample data was generated with a smooth trend and a rolling average lags a trend unless the planner sets the expected change. That result has been left in deliberately. Run the test on four years of your own trading, where outages, moved events and changes of mix live, before trusting either method. ## Limits ## What it does not do - **It is not a proven forecast.** A back test rebuilds a past year. Proof needs forecasts recorded at the time and compared with what followed. - **It does not model promotional behaviour.** Event weeks carry their history. Whether a promotion created sales or merely moved them needs a holdout test, and a template cannot manufacture that evidence. - **It does not justify acquisition with lifetime value.** First-order payback is in the model. An assumed lifetime value is where acquisition budgets go to hide. - **Contribution is not fully loaded channel profit.** The cost perimeter is set out on the P&L. Shared central costs are outside it unless you add them. ## Sources ## Where this comes from The method comes from budgeting ecommerce P&Ls since 1999, and from watching the same argument repeat at budget time: finance applies a growth rate to last year, ecommerce asks for more media, and nobody writes down what has to be true for either number to land. The figures in the workbook belong to a fictional retailer and describe no real business or market benchmark. Replace them, starting with the history. If the gap on the Reconciliation sheet is larger than anyone in the room can explain, that is worth a conversation before the budget is signed. ## Contact steve@barkingcat.ai Barking Cat Advisory Limited. Registered in England and Wales, company number 17338003.