Say Google Ads reports half a million for the month, Meta a third of a million, the email tool another few hundred thousand, and the total is higher than the revenue the store actually took. Each figure is correct by its own system’s rules; the same order simply appears in several of them. Credited revenue is therefore not separate money you can split the budget by. Set the overall budget from the store’s revenue and margin. Only a controlled test can show whether a particular channel brings additional orders.
Each channel claims the order by its own rules
Ad platforms and email tools do not count orders; they count contacts with their own channel that were followed by an order. Each sees only its own contacts, and each uses a different time window. Sklik, in the table below, is the ad system of Seznam, the Czech search engine:
| Channel | Default crediting rule | What it sees |
|---|---|---|
| Google Ads | 30 days after a click (adjustable 1–90), 1 day after a view | Google paid channels only |
| Meta | 7 days after a link click and 1 day after a view; separately 1 day after other engagement such as a share or save | Meta ads only |
| Sklik | 30 days after a visit from an ad | Sklik ads only |
| Ecomail | 30 days after a click, 10 days after an open | Its own campaigns only |
| Leadhub | 7 days after a click, 24 hours after an open | Its own campaigns only |
These are defaults; your accounts may be set differently. What matters is that the windows overlap. A customer sees an Instagram ad on Monday, clicks a Sklik result on Wednesday, opens a newsletter on Friday evening and buys on Saturday morning after clicking a Google ad. In Shoptet that is one order. Meta does not claim it for the view, because more than a day has passed. Sklik, the email tool and Google Ads, however, each claim the whole order.
Leadhub, a Czech email tool, states directly in its own documentation that the sum of revenue credited by Google Analytics, Meta and Leadhub is higher than the store’s actual revenue. This is not a measurement fault but a property of measurement. How large the overshoot tends to be, and why the store’s blended cost of sale is what counts, we worked through with an example in One advertising number is not enough.
For email, the signal itself is also weak. Both Ecomail and Leadhub credit an order after a mere email open. Since 2021 Apple has prevented senders from knowing when a recipient opens an email in its Mail app. An open therefore does not reliably show that someone read the email, and crediting by opens is the most generous part of the email figure.
Credited revenue is not additional revenue
For a budget decision, what matters is incrementality: orders that would not have happened without the channel. Attribution does not answer that question, because customers who would have bought anyway also click ads and open emails. Research at eBay and Facebook showed that standard estimates without a control group often miss the real effect of advertising. At eBay, ads also did not influence frequent customers, even though they accounted for most of the spend.
The error does not only run one way. In 2011 Google estimated from more than 400 studies in which advertisers paused their search ads that 89% of clicks from those ads were not replaced by organic results. That measures clicks, not orders, and the analysis came from the ad platform itself, but it shows that incrementality can be high. For email, 70 randomised experiments at a US ticket resale platform found that emailed discount offers raised recipients’ spending by 37.2%, and 90% of the gain did not come through redeeming the offer. Crediting by clicks or discount codes can therefore understate what email does.
All of this research comes from large US companies, and its figures cannot be transferred to a Czech store. The lesson for decisions is simpler: you do not know in advance either the direction or the size of the gap between credited and caused revenue.
Anchor the budget in store totals
Because credited revenue cannot settle incrementality, the budget needs a fixed point that does not overlap. That point is the whole store’s revenue and margin.
Total spend against total revenue. Add up spend across all paid channels and divide it by the store’s revenue excluding VAT for the same period. This number cannot count anything twice. If you add budget and neither total revenue nor margin moves in the following weeks, the extra spend was probably buying mostly orders that would have come anyway, however many the platform claimed.
Use attribution within a channel. Comparing Google Ads campaigns by Google Ads figures, or newsletters by the email tool’s figures, makes sense because the same rules apply. Comparing Google Ads with email by their own figures does not.
No model is the referee. It is tempting to crown one model as the truth, such as the one in Google Analytics. But GA4 simply divides credit differently: by default it credits paid and organic channels and looks back up to 90 days, while Google Ads credits only Google paid channels. GA4 is useful for comparing channels because it measures them all with the same yardstick. It is no better at answering what would have happened without a channel.
Spend across all channels against store revenue
See total ad spend next to the store’s revenue and what each platform claims on its own.
When a test is worth it, and which one
A test costs money and time, and at low volume it often gives no clear answer. Even large experiments produce very wide estimate intervals because customer purchases fluctuate strongly. Google recommends running experiments for at least four to six weeks and choosing high-volume campaigns to get a conclusive result. A test pays off where a large share of the budget is at stake, or where you plan to scale a channel up or down substantially.
| Test | How it works | Watch out for |
|---|---|---|
| Switch off and on | For a set number of weeks you switch off one type of campaign, such as ads on your own brand, and watch total store revenue | Seasons and promotions distort the comparison; competitors may start bidding on your brand |
| Email holdout group | You leave a random part of the list out of a campaign or automation and compare the two groups’ purchases using store data | The group must be random and large enough; the excluded customers miss the campaign during the test |
| Geographic test | You run ads only in randomly chosen regions and compare them with the rest | The Czech Republic has few comparable regions, and they vary widely in size |
| Platform-run test | Conversion Lift in Google Ads compares test and control groups of users or regions | Not available to all accounts; the platform itself designs and evaluates the test |
An email holdout group is usually the most accessible for an online store, because you control the contact list. The eBay research also used a switch-off test, turning off part of its advertising and measuring the result on total sales. Whichever test you choose, set the success criterion and the duration in advance and read the result from store revenue, not from the tested channel’s attribution.
When a test does not make sense, a controlled change remains: adjust the budget gradually, one channel at a time, and watch total revenue and margin. It is not conclusive proof, but it is a better basis than comparing credited revenue.
What to do now
- Write down the crediting rules. For each channel, the window after a click, after a view or open, and what the channel can see.
- Stop adding up credited revenue. Add up only spend and compare it with store revenue excluding VAT for the same period.
- Find the biggest disputed decision. The channel with the largest spend, or the one whose credit is most questionable, such as brand ads or emails credited by opens.
- Plan one test for it. Decide the test type, duration, the store-side metric and what you will do with each possible result.
- Between tests, change the budget one channel at a time. Read the result from total revenue and margin, not from attribution.
In its PPC section, Korzaro adds up only the spend across Google Ads, Meta and Sklik and calculates true cost-of-sale from the whole store’s revenue in Shoptet. It shows the revenue each platform claims separately and compares their sum with store revenue only as a check on measurement. Revenue from email comes from Leadhub in its own section. Korzaro does not design or evaluate an incrementality test for you.