How do I plan Black Friday ad budget?

Usually from margin and timing, not from last year's ROAS. Work out break-even ROAS at the sale price, start spending before slower buyers decide, and schedule increases instead of making them on the day. Then judge the week on total sales in Shopify, not on what each platform claims.

Black Friday is the one week when every ad report looks brilliant. Buyers who waited for the sale finally buy, and every platform that touched them claims the order. Build next year's plan on those claims and you will overpay with great confidence.

What one store's data shows

One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts.

Start with timing. On the Journeys sheet, one-touch journeys carry 79.5% of revenue and buy within 0.5 days. In that store's export, journeys of 2 to 3 touches take 12.5 days, and 4 to 9 touches take 16.9 days. If your export looks like this, money spent on the sale days mostly reaches people who had already decided. Spend meant to win a slower buyer has to start about two weeks earlier.

Then the line every euro must clear. The Break-even sheet does the sum for a 40% margin: 1 divided by 0.40 gives 2.5x. That is arithmetic on a margin, not a ROAS anyone measured. A Black Friday discount comes out of that margin, so the line moves up.

For illustration, a channel returning 3x clears break-even easily in October and loses money at the same 3x on Black Friday. Nothing about the channel changed. The price did.

What the file cannot show is a Black Friday. The export treats 1 January 2024 to 21 August 2026 as one block, with no spend and no weekly figures. So it cannot say how that store's peak behaved or what any channel caused in it. It is one store, not a benchmark.

Why does last year's ROAS mislead?

The usual answer is to take last year's Black Friday ROAS, add a growth target and push more money into the week itself. Four things break that plan.

• It counts buyers who waited. People who meant to buy anyway hold off for the sale, then click an ad on the day. Each platform counts the orders it touched, so the ROAS mixes sales the ads caused with sales they caught.
• The margin moved. The discount raised the line, as the table shows. Last year's target was set at last year's discount, on last year's costs.
• Late money buys late buyers. A budget that doubles on Friday mostly reaches people who were already in the queue. The build-up weeks win the slower buyers, if your journeys look like the ones above.
• Sudden jumps reset the machines. Meta's own example: raising a budget from $100 to $1000 may send ad sets back into the learning phase. Google's geo experiment guide says any budget change greater than 20% needs a new learning period. Increases planned in steps, or scheduled ahead, avoid both.

Google's advice cuts against panic buying too. Its help says Smart Bidding already manages seasonal events. Its seasonality adjustments suit short events of 1 to 7 days, and only major changes in conversion rate. A planned sale is exactly what they are for, and a nervous Friday afternoon is not.

What can the plan not tell you?

• Which channel caused the week. Every platform will claim its share of the same orders. Only a holdout settles that, and it needs clean weeks before the peak to set its baseline.
• Whether the sale added sales or moved them. A discount can pull December orders into November. Compare the weeks after Cyber Monday with the same weeks last year before you call the week a win.
• Whether this year matches last year. Black Friday moves each year, and GA4 has removed its match-day-of-week option for the previous year. Set both date ranges by hand so Friday meets Friday.

What to do this week

1. Set this year's break-even at the sale price. In Shopify admin, open Analytics, then Reports, filter the Category to Profit Margin and open Gross profit by product. Pass: your best sellers show a margin you can recompute at the planned discount. Fail: they show no profit, which Shopify says happens when no cost was recorded at the time of sale, so fill in costs first.
2. Find last November's lag in GA4. Click Advertising, open the Key events dropdown, choose Key event attribution paths and set last November as the date range. Pass: the Path length filter lets you read Days to key event for paths of two or more touchpoints. Fail: the report is empty, so check that purchase counts as a key event before November.
3. Ask Google which campaigns will run short. In Google Ads, open the Insights page at account level and find Budget pacing insights. Pass: no main campaign reads Projected to be limited by budget. Fail: one does, which Google says means it may miss 5% or more of future weekly traffic, so plan its increase now.

Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework

Sources, 1 October 2026: Significant edits and learning phase (Meta Business Help Center); Implement campaigns for geo experiments (Google Ads Help); About seasonality adjustments (Google Ads Help); About seasonal budget adjustments (Google Ads Help); Change and compare date ranges in reports (Google Analytics Help); Profit reports (Shopify Help Center); Key events attribution paths report (Google Analytics Help); About budget pacing insights (Google Ads Help)

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