European Ecommerce Benchmarks: A Flawed Perspective
European ecommerce brands are often guided by regional benchmarks for key metrics like ROAS and CAC. However, these benchmarks are inherently flawed as they are based on correlation-based attribution models and fail to account for the vast diversity of the European market.
By Joris van Huët, Founder & CEOUpdated 3 min read
Run the numbers for your store: the free conversion rate calculator.
The Challenge of European Ecommerce
The European ecommerce market is one of the largest and most diverse in the world. For Shopify brands operating across Europe, this presents a unique set of challenges and opportunities. To succeed, you need a sophisticated understanding of the different markets and a data-driven approach to marketing attribution. The problem is, many brands are relying on flawed benchmarks that don't reflect the reality of the European market.
The Myth of Pan-European Benchmarks
It's tempting to look for pan-European benchmarks to gauge your performance. You might see reports on the average ROAS or CAC for ecommerce in Europe. However, these benchmarks are incredibly misleading. Europe is not a monolith. It is a collection of diverse markets, each with its own unique culture, language, and consumer behavior. A benchmark that is based on an average of all of these markets is essentially meaningless.
Deconstructing the Continental Metrics
Let's look at why common benchmarks are so problematic in the European context:
Return on Ad Spend (ROAS): A high ROAS in Germany, where consumers are typically more price-sensitive, might mean something very different than a high ROAS in the UK, where consumers are more likely to be influenced by brand and trends. A pan-European ROAS benchmark completely ignores these important nuances.
Customer Acquisition Cost (CAC): The cost of acquiring a customer can vary dramatically from one European country to another. A CAC that is considered low in a high-value market like Switzerland might be considered high in a lower-value market like Poland. A single European CAC benchmark is simply not useful.
Conversion Rate: Conversion rates can be influenced by a wide variety of factors, including language, payment preferences, and trust signals. A conversion rate that is considered good in one country might be considered poor in another.
The Only Benchmark That Matters: Incremental Lift in Each Market
Instead of chasing misleading pan-European benchmarks, you should be focused on the only benchmark that truly matters: incremental lift. Incremental lift is the measure of the sales that you are generating in each European market that you would not have generated without your marketing. It is the true measure of your marketing's effectiveness in each market.
Incremental Market Sales = (Total Market Sales with Marketing) - (Baseline Market Sales without Marketing)
To measure incremental lift, you need a causal understanding of your marketing, which is something that traditional attribution models simply cannot provide.
Causality Engine
Causality Engine reads one file, the Attribution paths export from GA4, and shows what each channel caused next to what last-click gave it.
| Feature | Traditional Benchmarking | Causality Engine |
|---|---|---|
| Methodology | Correlation-based | Causal Inference |
Related Resources
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Have an audience of ecommerce operators? Earn 20% recurring commission by recommending the €99 read.
Keep reading
Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal AnalysisCausal Analysis identifies true cause-and-effect relationships in data, moving beyond correlation to show how marketing actions directly impact outcomes.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- Conversion rateConversion Rate is the percentage of website visitors who complete a desired action out of the total number of visitors.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- Customer acquisitionCustomer acquisition attracts new customers to a business. For e-commerce, this means driving the right traffic to the website.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- Trust SignalsTrust Signals are website elements that build visitor confidence. Examples include security badges, customer testimonials, and clear contact information.