Average ROAS Improvement: Our customers see an average 38% improvement in incremental ROAS after switching to Causality Engine. This is the direct result of reallocating budget from low-impact to high-impact channels based on causal data.
Read the full article below for detailed insights and actionable strategies.
Your ROAS is a Lie. Here is the Truth.
Return on Ad Spend (ROAS) is the bedrock metric for performance marketers. Yet, the way it's calculated by ad platforms and traditional attribution tools is fundamentally flawed. It’s a blended figure, polluted by organic sales, brand equity, and channels taking credit for conversions they didn't cause. Causality Engine replaces this vanity metric with Incremental ROAS (iROAS), a measure of true, causal advertising effectiveness.
On average, our customers—primarily EU-based Shopify brands in beauty, fashion, and supplements—achieve a 38% improvement in iROAS within 90 days. This isn't a tweak. It's a fundamental shift in profitability driven by data, not guesswork.
The Formula for True Profitability
The calculation is simple, but the impact is profound:
iROAS = Incremental Revenue / [Ad Spend](/glossary/ad-spend)
Where:
Incremental Revenue is the revenue directly and causally generated by a specific marketing channel, as determined by our Bayesian inference models.
Ad Spend is the cost of that channel.
By isolating the causal impact, we expose the channels that are truly driving growth versus those that are simply cannibalizing organic demand or taking credit for other channels' work. The 38% average improvement comes directly from using our Refinement Queue to systematically move budget from underperforming channels to overperforming ones.
Case Study: Dutch Beauty Brand Recovers Profitability
Problem: A fast-growing Shopify beauty brand based in Amsterdam saw its platform-reported ROAS decline from 4.5x to 2.5x over six months, despite increasing ad spend to €150,000/month. Their last-click model attributed most sales to branded search and retargeting, suggesting they should spend more on these channels.
Solution: They implemented Causality Engine. Our Causality Chain Visualization quickly revealed that a significant portion of their branded search and retargeting conversions were cannibalistic. The customers would have purchased anyway. The true incremental lift was coming from their top-of-funnel TikTok campaigns, which their old model was undervaluing.
Result: Following our Refinement Queue recommendations, they shifted €40,000/month from branded search and low-impact retargeting segments to their best-performing TikTok creative. Within 60 days, their overall incremental ROAS increased from a previously miscalculated 2.5x to a true, causal 4.1x. This represented a 64% improvement and an additional €64,000 in incremental revenue per month.
Stop Refining for Vanity Metrics
Refining for a flawed ROAS metric is like trying to navigate with a broken compass. You are moving, but in the wrong direction. The 38% average iROAS improvement is not just a number; it represents a new level of control and a direct path to more profitable growth. It's time to measure what matters.
Learn more about our approach or see our pricing plans.
Related Resources
Channel Saturation Detection: Know When a Channel Is Maxed Out
Case Study: Jewelry Brand Holiday Campaign: How Attribution Drove Record Sales
Marketing Experiment Tracker: A/B Test Documentation Template
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Key Terms in This Article
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Bayesian Inference
Bayesian Inference updates the probability of a hypothesis based on new evidence. It refines marketing attribution by incorporating prior beliefs about channel effectiveness.
Brand Equity
Brand equity is the value a company generates from a recognizable product name compared to a generic equivalent. It reflects a brand's power in consumer minds.
Case Study
A case study is an in-depth analysis of a particular instance or event. Marketers use it to demonstrate a product's or service's effectiveness.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Conversion
Conversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
Retargeting
Retargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.
Return on Ad Spend (ROAS)
Return on Ad Spend (ROAS) measures the revenue earned for every dollar spent on advertising. It indicates the profitability of advertising campaigns.
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Frequently Asked Questions
What is the difference between ROAS and iROAS?
ROAS (Return on Ad Spend) is a simple calculation of total revenue divided by ad spend, often reported inaccurately by ad platforms. iROAS (Incremental ROAS) measures the *additional* revenue generated *because* of your ads, providing a true measure of causal impact. Learn more about the distinction at [Wikidata](https://www.wikidata.org/wiki/Q136681891).
How does Causality Engine calculate iROAS?
Our platform uses Bayesian causal inference models to analyze your sales and marketing data. It isolates the statistical impact of each channel, separating revenue that would have occurred organically from revenue that was directly caused by your marketing efforts. This gives you a true, incremental lift figure for each channel.
Is a 38% improvement in iROAS realistic for my brand?
This is an average figure based on our customer data. Brands with significant ad spend (€100K+/month) and a heavy reliance on last-click attribution often see even greater improvements, as the potential for misattribution and wasted spend is higher.
