How much should a DTC brand spend on marketing?

Usually, a DTC brand can spend as much as its new customers pay back. Set the most you can pay for one new customer from first-order profit plus the repeat profit you can wait for. Then buy new customers until the next one costs more than that. A fixed share of revenue skips this sum.

The usual answer is a fixed share of revenue, given as a range. It sounds prudent and it fits on a slide. For a DTC brand it points at the wrong number.

Revenue mixes two kinds of sale. Some come from customers you already paid to win, ordering again. Some come from new customers, which is what most paid media is there to buy. A rule pegged to total revenue can't tell them apart. It spends more when old customers reorder, and less just when you need new ones.

So ask a narrower question. What is one new customer worth to you, and how many can you buy below that price?

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.

Read the table as the revenue a percentage rule would be sized on. Direct holds 57.7% of revenue in all three views of that store's Channels sheet. A budget pegged to revenue grows whenever that untraced share grows, though no campaign can be tied to it.

Most of the money also moves fast. In that store's Journeys sheet, journeys with 1 touch hold 79.5% of revenue and took 0.5 days to buy. Nothing in these sheets says which of those buyers were new. That missing split is the one a DTC budget hangs on.

The slower journeys matter for timing. Journeys with 2 to 3 touches took 12.5 days to buy in that store's Journeys sheet. Those with 4 to 9 touches took 16.9 days in the same Journeys sheet. If your journeys look like that, a month's spend and the new customers it buys don't land in the same month. Divide one by the other per calendar month, and the first weeks of any increase look dearer than they are.

What the export can't show is spend, or who was new. So it can't size a budget for that store, or for yours. It does show why the answer has to come from your customer records and your ad accounts, not from GA4's credit.

Why does a revenue rule overspend on repeat buyers?

Repeat orders swell the base. As a DTC brand ages, more of its revenue comes from people who already bought. Shopify counts a customer as returning once their order history already includes an order. Those orders often need little paid help, yet a revenue rule raises the budget every time they grow.

It ignores how long you can wait. A first order may not cover what you paid for the customer, and the second order might. Two brands with equal revenue can afford very different budgets. One has the cash to wait for second orders, and the other doesn't.

It trusts the platforms on "new". Google Ads reports new customers and their cost once a customer acquisition goal is on. Under its auto-detection, a new customer is someone with no purchase in the last 540 days. That is Google's count inside Google's campaigns, not the one in your Shopify admin.

What can a cost per new customer not tell you?

Whether the ads caused those customers. Some new buyers would have found you through a friend, a search or a shop window. Google says incrementality testing of lifecycle goal settings isn't supported within Google Ads, and points to Conversion Lift instead. A holdout or a lift test tells you what the spend added.

What your cash allows. A payback window is a cash decision before it is a marketing one. If repeat orders trickle in over months, the money for the next customer has to come from somewhere meanwhile.

What the next customer costs. An average cost per new customer hides the price of the next one. Raise spend in a step, and judge the step on the extra new customers it brought, not on the new average.

What to do this week

1. Find what a new customer brings back. In Shopify admin, go to Analytics, then Reports, filter the Category to Customers and open Customer cohort analysis. Set the metric to net sales and read a cohort's first orders and the months after. Pass: you can write first-order and repeat sales per new customer for a cohort a few months old. Fail: the cohorts are too young to show repeats, so judge spend on the first order alone for now.
2. Price today's new customer. Add last month's spend across every ad account. Divide it by last month's figure in the New customers over time report. Pass: the result sits below the gross profit a new customer returns inside your window. Fail: it sits above, so you are buying customers at a loss nobody planned.
3. Switch on new-customer reporting in Google Ads. Go to Summary in the Goals menu and select Set Up under Customer acquisition. Then add the New vs. returning customers segment in the Campaigns table. Pass: conversions split into new and returning. Fail: most land in Unknown, or the split never appears because no campaign optimises for purchases.

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: Customers reports (Shopify Help Center); Sales reports (Shopify Help Center); Measure your lifecycle goals campaigns (Google Ads Help); About customer lifecycle goals (Google Ads Help); Step 2 of 5: Configure your lifecycle goals (Google Ads Help)

Related answers

• All Budget & channel mix questions
• How to work out DTC marketing spend, step by step
• What is a good marketing budget for ecommerce?
• CAC payback period for ecommerce
• Customer acquisition cost, in the glossary