Google Ads vs Facebook Ads: where to spend your first euro and how to combine them
Existing intent versus created demand: how to choose between Google Ads and Facebook Ads for your business, and how to run both together.
How to set your ad budget from goal and margin rather than instinct, with a worked example and test and scaling phases.
Your online advertising budget should be calculated from your goal and your margin, not from a fixed percentage. Work out how much you can pay for a customer without losing money (the margin per order, or the value of the customer), estimate cost per click and conversion rate, then multiply the cost of one customer by the number of customers you want. Start with a test budget, optimise for four to eight weeks, and only then scale up gradually.
When you ask how much to invest in online advertising, you are often given a percentage of turnover. That benchmark can help with general marketing planning, but it says nothing about what the money will actually achieve. For a business with a high margin and returning customers, the same percentage may be too low. For one with a thin margin and one-off sales, it can lead straight to a loss.
A good budget answers a different question: how much can you pay to win a customer and still make a profit? Everything else follows from that: how high you can bid, which channels make sense, how much to test and when to scale. A budget set by instinct (“what I can afford this month”, “what competitors spend”) leads to one of two opposite mistakes: you either stop too early, with no data, or you carry on for months in a channel that cannot be profitable.
Another reason a percentage is not enough is the gap between budget and goal. If you want 100 orders a month and a customer costs you 25 EUR, you need 2,500 EUR for ads alone; if your budget is 500 EUR, the realistic result is around 20 orders, however well you optimise. The numbers have to fit together.
For a first picture, the ads budget simulator lets you play with cost per click, conversion rate and budget to see an indicative outcome. The result depends on your assumptions, so treat it as a sketch, not a forecast.
Sales are not profit. To know how much you can spend on ads you need your contribution margin: what is left of an order after product cost, packaging, shipping, payment fees, returns and other variable costs, but before advertising. Details of the calculation for shops are in our article on the profit margin of an online shop.
Two key figures follow from the margin:
Mind the VAT: calculate revenue without VAT, otherwise the margin looks larger than it is. A “good” ROAS does not exist in the abstract, only one that is good for your margin. A shop with a 60% margin may be profitable at a ROAS of 2; one with 20% needs 5 just to break even.
Imagine a hypothetical online shop, with figures chosen purely for the example. They are not market averages and should not be copied as they stand.
| Item | Value in the example | How it is obtained |
|---|---|---|
| Average order value (excluding VAT) | 80 EUR | From the shop’s own data |
| Contribution margin before ads | 35% = 28 EUR | After product, shipping, packaging, fees |
| Break-even CPA | 28 EUR | Margin per order |
| Break-even ROAS | about 2.86 | 80 / 28 |
| Estimated cost per click | 0.40 EUR | Assumption, to be checked in the test |
| Estimated conversion rate | 1.5% | Assumption, to be checked in the test |
| Estimated CPA | about 27 EUR | 0.40 / 0.015 |
| Test budget for 30 orders | about 800 EUR | 30 x 26.7 |
Notice what the example shows: on these assumptions the estimated cost per customer (27 EUR) is very close to the break-even level (28 EUR). There is no profit on the first order. You have three levers: raise the conversion rate (going from 1.5% to 2% would bring CPA down to 20 EUR), lower the cost per click, or raise the order value. Each of these is improved outside the ad budget; for the first, see how conversion rate is calculated.
For services the logic is similar, with one extra step between the click and the customer. If a lead costs you 15 EUR and one in five leads becomes a customer, a customer costs you 75 EUR in advertising. Is that good or bad? It depends on the profit a customer brings, not on the figure itself.
If your customers come back or pay on a recurring basis, you can accept a higher cost on the first purchase, because the profit arrives over time. Two concepts help: LTV (the average lifetime value of a customer, as profit, not revenue) and CAC (the total cost of acquiring a customer). The ratio between them shows whether your business model can support paid advertising.
Work them out with the LTV and CAC calculator. Caution matters here: do not use an optimistic LTV that counts repeat purchases that have not happened yet. For new businesses with no history, start with the value of the first order and treat repeat business as a bonus, not a guarantee.
Include in CAC not only the money paid to platforms but also creative costs, agency fees and sales team time. If you ignore these, advertising looks cheaper than it is and the budget swells as you go.
A test that is too small tells you nothing: it does not produce enough data to tell a good campaign from a lucky one. A test that is too large burns money on unproven assumptions. The balance depends on a minimum number of conversions, not on a round sum.
Ad platforms need a minimum volume of events for their automated systems to learn. Platform documentation has mentioned thresholds of roughly tens of conversions a month for automated bidding in Google Ads and, at Meta, around 50 optimisation events per ad set a week to exit the learning phase. These figures can change, so check the current values in the platforms’ interfaces and official help pages.
A practical calculation: test budget = desired conversions x estimated CPA x 1.3 to 1.5 (a cushion for wrong assumptions). In the example above, 30 orders at 27 EUR come to about 800 EUR, to which you add the cushion. If your budget is below what the minimum volume needs, you have two options: concentrate the money on one campaign in one channel, or pick an easier goal (for instance leads rather than direct sales) so that you collect data faster.
Think about seasonality too: a test in a quiet month may understate the potential, and one at the peak may overstate it.
A budget is not a fixed sum for the whole year; it evolves in phases. Each phase has a clear purpose and a condition for moving to the next.
You move to scaling only if CPA stays below your threshold for long enough, not after one good day. It is also common for CPA to rise as you increase the budget: the first conversions are the cheapest, and the following ones gradually cost more.
There is no universal split. A useful way of thinking: search channels (such as Google Ads) capture existing demand, meaning people already looking for what you sell, while social channels create demand and reactivate it through remarketing. The detailed comparison is in Google Ads or Facebook Ads.
To begin with, you can give most of the budget to the channel with the strongest buying intent and a smaller share to a test on a new channel. A 70/30 split, for example, is a reasonable place to start, not a rule. After the first data, move the money to where cost per customer is lower and volume allows.
In Google Ads the budget is usually set as an average daily amount; on a particular day Google may spend up to about twice the daily amount, but over a month you are not charged more than roughly the daily budget multiplied by 30.4 (check the current rules in the official help). If the budget is too small for the available demand, campaigns can lose impressions; if it is too large for what you can manage, wasted spend creeps in.
A realistic budget covers more than the amount paid to the platform. If you leave these out, your profitability calculation comes out too optimistic.
Before we talk about sums, we ask for the basic figures: order value, margin, repeat-purchase rate, delivery capacity and the monthly goal. On that basis we propose a test budget with a decision threshold, not a “starter” sum picked at random. If the data shows that a channel cannot be profitable under the given conditions, we say so before the money is spent.
When running campaigns we track cost per customer against the agreed threshold each month and propose scaling only where results hold. For details of how we manage accounts, read about our Google Ads service or write to us through the contact page.
There is no universal sum. It depends on cost per click, conversion rate and how many conversions you need to draw conclusions. Calculate desired conversions times estimated cost per customer, plus a cushion. If the sum is too small for a useful test, concentrate the money on one campaign or choose an easier goal.
As a rough guide, four to eight weeks, depending on volume and the buying cycle. The number of conversions matters more than the number of days. After a few days the platforms are still learning, so the figures are unstable. Avoid stopping or changing everything after the first bad day.
At least your own break-even ROAS, which is 1 divided by contribution margin. At a 35% margin that is about 2.86. Above it you profit from the first order; below it you can be fine only if customers return and you have real data to show it. A universally good ROAS does not exist.
When cost per customer stays below your threshold for long enough, not after one good day. Increase in small steps, in the region of 15 to 25%, and wait a few days between steps. If CPA climbs sharply, stop and optimise before raising the amount.
Start with the channel where people already search for what you sell, usually Google search, and keep a smaller share for a social test. Then move money according to cost per customer. There is no universal ratio; your offer, audience and buying cycle decide.
Yes, when you calculate profitability. The money paid to the platform is only part of the acquisition cost; the agency fee, creative and team time all count towards CAC. If you leave them out, advertising looks cheaper than it is and your conclusions about profit become too optimistic.
Laborator
Lab tools that help you put this guide into practice.
Blog
Existing intent versus created demand: how to choose between Google Ads and Facebook Ads for your business, and how to run both together.
Gross margin, contribution margin, break-even ROAS and maximum CPA: the maths that shows whether you sell at a profit.
The formula, the right denominator, rates for sales, leads and ads, and why comparing yourself with an average is a trap.
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