Meta Ads for online shops: what is different from search

Search has a hard ceiling, Meta only stops at your margin

On Google, existing demand limits your budget. On Meta, it is usually your margin that sets the limit first. That is my assessment. Someone searching for a product already has the need. Once those searches are covered, extra money mainly buys more expensive clicks. Nobody searches on Meta. The system fills the feeds of people who are thinking about something else, and more budget almost always finds more impressions, though in a narrow niche only at a rising CPA.

That sounds like an advantage, and it is also the risk. A search campaign eventually slows down on its own. On Meta, the only brake is your own calculation, and you have to do it before you launch.

This distinction has two limits. The real dividing line runs between search and feed, not between two companies: Demand Gen and YouTube at Google behave more like a feed. And Meta does not create demand automatically. Haus, a provider of measurement tests, describes Advantage+ in its analysis of 640 experiments as particularly good at finding people who are ready to buy, partly at the expense of its own incremental effect. So in the feed, too, you can end up paying for people who would have bought anyway.

The trap is familiar from search. In the field experiment at eBay, brand keywords brought no measurable short-term benefit. Generic ads did reach new and infrequent users, but most of the cost was spent on frequent buyers who would have bought anyway. That was a large marketplace more than ten years ago, and the result does not transfer directly to your shop. The question behind it does: how many of the reported purchases would not have happened without the ad?

Before launch you need two numbers, not a ROAS target

The first number is the most you will pay for an additional new customer. The second is the minimum budget that follows from it. A worked example with invented, round numbers:

Suppose a shop has a net order value of €80 and keeps €32 of contribution margin after goods, shipping, payment fees and returns. Repeat purchases add another €18 on average in the first year. The ceiling for an additional new customer is therefore €50. The retailer wants a buffer and sets €40 as the allowable price.

Meta, however, reports purchases, not additional purchases. That is why the calculation needs a discount:

Allowable platform CPA = price per additional new customer × share of purchases that are genuinely additional

Until there is a test, this share is an assumption. The retailer uses 60% as a working value, which gives €24.

Minimum budget per ad set = 50 purchases per week × allowable platform CPA

The 50 comes from the learning phase. According to Meta, ad sets usually leave it after about 50 results in the week following the last significant edit; until then they are less stable and usually more expensive. In the example, that comes to €1,200 a week, around €5,200 a month, per ad set. The CPA in the first few weeks will probably be higher, and that belongs in the plan.

If the total does not fit your plan, the campaign is not what is wrong. Fewer ad sets or a later start are the right answer. 50 is a guideline: an ad set with 30 purchases a week can still be profitable. It will fluctuate more, so you have to judge it over longer periods and touch it less often. At five purchases a week, by contrast, there is nothing you could seriously evaluate.

A shop needs fewer campaigns than it thinks

The start I recommend is one sales campaign with a single, broadly targeted ad set. Meta itself warns that the system learns less when there are many ads and ad sets per unit. Technically, a sales campaign counts as Advantage+ when the budget sits at campaign level, the audience may be expanded automatically or is restricted by location only, and no placements are excluded. That removes almost every control lever you know from search. What remains is the offer, the creative, the budget and the question of which data you send back.

Every additional building block has to earn its place against the calculation above, because each extra ad set needs its own purchases.

The existing-customer row has a catch. The former budget cap for existing customers is no longer available in new Advantage+ campaigns, and Meta points to two separate ad sets instead. In the example, that doubles the minimum budget. You also need a customer list as a Custom Audience, and you should clarify the legal position on uploading it beforehand. I would only take this step once you know the share of existing customers from your shop system and it justifies the extra cost.

The report in the ad account cannot tell you whether Advantage+ or a manual setup delivers more. In the Haus data from July 2025, the platform reported slightly higher ROAS for Advantage+, but in the experiments the lift including the follow-up period was plus 17% for Advantage+ against plus 32% for manual campaigns. Advantage+ still won for 42% of brands. That is no reason to rule it out, but you should not rely on the Advantage+ ROAS alone. Haus sells these tests, and the average advertiser in its data spends USD 14 million a year on Meta. That is no yardstick for your shop.

Optimising for add to cart is the usual way out when there are too few purchases. According to Haus, whose clients are far larger than the worked example, this optimisation was only 5% less efficient than purchase optimisation for brands with fewer than 5,000 purchases a week, and across all brands its share of new customers averaged 78% instead of 67%. I see it as a stopgap with a side benefit. Whether it produces contribution margin only becomes clear in the control group.

Data: the Conversions API is also your ticket to testing

The Conversions API is often treated as a repair for the pixel. In the DACH region (Germany, Austria and Switzerland), it has a second job: it is what allows you to test Meta itself with a control group in the first place. For the self-serve Conversion Lift test, Meta requires either the Conversions API with an Event Match Quality above 5 or another supported data source. The second route is not open to advertisers who mainly target ePrivacy countries, such as Germany and Austria. Meta also recommends the API as a data source because it depends less on the browser. When the Conversions API is worth the effort, and what needs to be settled on consent, therefore belongs before launch, not in the month you want to run the test.

With attribution, you should know what you are reading. The default setting counts purchases within 1 or 7 days of a click and can also credit purchases made within a day of simply seeing an ad. That is one reason why Meta shows more revenue than your shop. Meta additionally offers an incremental attribution model that uses predictions to estimate whether the ad caused the purchase. That is closer to the right question, but it is still Meta's own model. And according to Meta, ad sets using different models should not be compared directly.

Your shop system remains the reference. It holds the orders after cancellations and returns, and the answer to whether a customer was really new.

Test length depends on the question you ask

Meta runs several time windows side by side, which makes wrong decisions easy. If you switch off a new creative after four days, you have usually measured nothing but the learning phase.

The last two rows are different questions. According to Haus, 96% of account-wide studies already showed significant lift at the midpoint of the test. The ranking can still flip afterwards, though: at the midpoint, Advantage+ was on average 9% ahead of manual campaigns, and after the follow-up period it was behind. Stopping early therefore tells you whether something works, not which variant delivers more.

Meta advises against frequent budget changes and against very small or inflated budgets, because both disrupt learning. I would therefore change budgets in steps with fixed review dates and build the monthly plan around them, rather than adjusting every day.

Judge Meta by total contribution margin with and without it

The right measure of success is the difference in total contribution margin between a world with Meta and one without it. Here is the calculation for the example, again with invented numbers and simplified, because all additional purchases are treated as new customers:

A platform ROAS of 3.3 looks comfortable, but the real decision lies elsewhere. The retailer pre-finances every new customer and only gets the money back through repeat purchases. If the business can carry that and the €18 holds, it can keep going. If neither is true, the same ROAS means losing money. For a range without repeat purchases, the campaign in this example could cost no more than €32 per additional purchase.

A better model is no substitute for the control group. In 663 large Facebook experiments, even more than 5,000 user characteristics were not enough to estimate the effect reliably without an experiment. A retailer has far less data than that, so its before-and-after comparisons are weaker still.

According to Meta, Conversion Lift costs nothing extra and compares a random test group with a control group that sees no ads. As a guideline, however, Meta names a campaign from the past year with at least USD 5,000 in spend and 500 conversions, and proportionally more for longer test periods (as of November 2026). Many smaller shops are unlikely to meet this or will fall short on data quality. What remains is a regional test: you switch Meta off in selected areas and compare shop revenue and brand searches with the other areas. This only gives a usable signal with enough orders per region. Below that, no measurement can support the budget decision, and you should limit the spend to an amount you can afford to lose.

Part of the effect also shows up somewhere you are not looking. I assume that people reached on Meta later turn up as brand searches on Google and are booked there as a Google success. I have no independent evidence of this for the DACH region or for small shops, which is why brand searches belong in every test. For brands with at least 25% of revenue outside their own shop, on Amazon for example, around 32% of the Meta effect landed in those other channels, according to Haus. If you sell through several channels, measuring the shop alone undercounts.

Below around 30 purchases a week, Meta can wait

I would postpone the launch if any of these conditions applies: the contribution margin, even with realistic repeat purchases, cannot support a CPA at which you can win new customers. The budget is not enough for even around 30 purchases a week. Or the offer cannot explain itself in the feed within a few seconds, because without a search query nobody brings the intent with them.

Conversely, there is a good case for Meta when search is exhausted and additional Google budget only buys more expensive clicks. How you then split the budget between Google and Meta follows from the additional contribution margin per channel, not from a standard ratio.