PPC studies and benchmarks circulate with attractive numbers: “average CPC of X”, “average conversion of Y”. They serve as reference, but rarely as a target. Anyone using them without understanding the sample, sector and method can make wrong decisions that look rigorous.
The essentials in a definition
Reading benchmarks and industry studies without being misled requires understanding the sample, the period, the metric definitions and the interest of whoever publishes. Average values rarely apply to a specific business. Sources such as Eurostat data, INE (Statistics Portugal) and Think with Google studies are useful, if read with judgement.
What a benchmark really says
It summarises the average behaviour of a set of accounts, in a period and a market. It does not say what is reasonable for your business, your margin, your buying cycle or your market.
An average hides enormous differences between accounts. Half may sit above and half below, with legitimate reasons in each case.
Questions to ask before using a number
Who collected the data and with what interest? Which accounts were included (size, sector, country)? Which period? What exact metric? Is it a mean, a median or a top value? Is there an indication of dispersion or range?
If the answers to these questions are not clear, treat the number as a curiosity, not as a decision reference.
Reading a benchmark critically
- Understand who produced it and with what data
- Confirm metric, period and sector
- Compare with your own evolution
- Test the hypothesis in your account
Vendor and manufacturer studies
Studies published by platforms or tools tend to highlight results favourable to the product. That does not make them useless, but it calls for critical reading: what was the comparison group? Was it randomised? Is the gain statistically meaningful and applicable to your case?
A good practice is to look for independent confirmation: tests in your own account, comparison with earlier periods, other sources.
An external average says how others are, not how you should be.
How to use benchmarks profitably
Use them to set hypotheses and orders of magnitude, not to fix targets. Compare yourself with yourself first: the evolution of your own account over time says more than any external average.
If a benchmark says you are far below or above, look for causes before concluding. It could be different measurement, a different product mix or simply a different sample.
Decision table
| Question | What to check | Warning sign |
|---|---|---|
| Who published it? | Commercial interest and methodology | No methodology or sample |
| What is the sample? | Size, sector, country, period | Small, unrepresentative sample |
| How is the metric defined? | Clicks, conversions, ROAS, margin | Vague or different from yours |
| Is it comparable? | Size, purchase cycle, market | Compares very different businesses |
Common mistakes to avoid
- Using the average as a target. Set your target with your own data and margins.
- Ignoring the date. Old benchmarks may no longer reflect the market.
- Confusing correlation with cause. A study showing a relationship between two metrics does not prove causality.
How to measure the result
Create a table with your own quarterly indicators and use benchmarks only as an order-of-magnitude reference, noting source, date and definition.
Limits and exceptions
Even good studies have limits: they reflect the past, averages and specific samples. They can help calibrate expectations, but do not replace your data or your tests. Moreover, some studies are sponsored and highlight favourable results. Cross several sources, check methodology and, when in doubt, prefer official data and your measured experience, using benchmarks only as context for forming hypotheses.
Illustrative example
Illustrative example: a manager reads that average sector ROAS is 4 and is dissatisfied with 3.2. On checking, the study refers to higher-margin companies and revenue net of returns. With their margin, 3.2 is profitable. They now use benchmarks as context and their own margins as the target.
Frequently asked questions
Are benchmarks useless?
No. They serve as reference, provided they are read critically and not used as a target.
How do I know whether a study is reliable?
By the transparency of the sample and method and the independence of the publisher.
What is the best reference?
Your own evolution, comparing equivalent periods.
Should I trust benchmarks?
As context, yes; as a target, no. The target depends on your margins and purchase cycle.
Where do I find reliable data?
In official statistics (Eurostat, INE), studies with public methodology and platform reports, read critically.
Benchmarks serve as context, not targets: check source, sample, definition and date, and always compare with your own margins and data.
Sources and recommended reading
Last editorial review: October 2026. Informational content; always confirm current conditions in the official sources before deciding.