Case Study: See how a Dutch skincare brand stopped guessing and used causal inference to triple their ROAS. They uncovered hidden value in their top-of-funnel channels and scaled ad spend profitably.
Read the full article below for detailed insights and actionable strategies.
Case Study: How a Skincare Brand Scaled 3x ROAS with Causal Attribution
Excerpt: See how a Dutch skincare brand stopped guessing and used causal inference to triple their ROAS. They uncovered hidden value in their top-of-funnel channels and scaled ad spend profitably.
The Problem: Last-Click Lies
A rapidly growing DTC skincare brand in the Netherlands saw its ROAS stagnate at 1.5x on a 150,000 EUR/month ad spend. Their last-click model in Google Analytics was over-attributing to branded search and retargeting, leaving them blind to which top-of-funnel channels were actually driving new customer acquisition. They couldn't scale their budget without torching profitability.
The Solution: Causal Clarity
Using Causality Engine's one-time analysis, the brand ran a 40-day lookback period. The Intelligence-Adjusted Attribution model immediately revealed that their last-click setup was undervaluing their prospecting campaigns on Meta and TikTok by over 50%. The Causality Chain Visualization showed that these channels were the primary drivers of new customer journeys, even if they didn't get the final click.
The Results: Profitable Growth
Armed with this causal data, the brand reallocated 60,000 EUR from branded search to top-of-funnel Meta and TikTok campaigns. Within 60 days, their blended ROAS increased from 1.5x to 3.2x, and new customer acquisition grew by 75%. Their incremental revenue from the refined spend was calculated at 210,000 EUR. The formula is simple: Incremental Revenue = (Causal ROAS - Last-Click ROAS) * Spend.
"Causality Engine gave us the confidence to scale. We went from a 1.5x to a 3.2x ROAS in under two months. It's the single most powerful tool in our marketing stack."
CMO, Anonymous Skincare Brand
Ready to Stop Guessing?
Your data is lying to you. Last-click attribution is a broken model that leads to wasted spend and missed opportunities. It's time to upgrade to a causal understanding of your marketing.
Causality Engine offers a clear path to profitable scaling. For just $99, you can get a one-time analysis that will reveal the true impact of your channels. Or, subscribe for €299/month and get continuous refinement and access to our LLM chat interface.
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Key Terms in This Article
Attribution Model
An Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
Causal Analysis
Causal Analysis identifies true cause-and-effect relationships in data, moving beyond correlation to show how marketing actions directly impact outcomes.
Causal Attribution
Causal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
Causal Inference
Causal Inference determines the independent, actual effect of a phenomenon within a system, identifying true cause-and-effect relationships.
Customer acquisition
Customer acquisition attracts new customers to a business. For e-commerce, this means driving the right traffic to the website.
Customer journey
Customer journey is the path and sequence of interactions customers have with a website. Customers use multiple devices and channels, making a consistent experience crucial.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Marketing Attribution
Marketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
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Frequently Asked Questions
How is Causality Engine different from Google Analytics?
Google Analytics primarily uses last-click or other rule-based attribution models. Causality Engine uses Bayesian causal inference to determine the *incremental* impact of each channel, showing you what sales would *not* have happened without that marketing touchpoint. It's the difference between correlation and causation.
Is this difficult to set up?
No. Setup is simple and requires no code. You connect your Shopify store and ad accounts (Meta, Google, TikTok, etc.) via a secure integration, and our models begin training immediately. You can have your first causal analysis within 3-5 minutes.
What if I have a small budget?
Causality Engine is even *more* critical for smaller budgets. When every euro counts, you cannot afford to waste it on channels that aren't driving real, incremental growth. Our €99 one-time analysis is designed for brands who need to make every marketing dollar work harder.
My brand is not in beauty, fashion, or supplements. Will this work for me?
While our expertise is deepest in these verticals, our causal models work for any Shopify-based e-commerce business with sufficient data. If you spend over 50,000 EUR/month on ads, we can likely provide significant value.
