Case Study: A Dutch beauty brand refined their Meta vs. TikTok ad spend using causal inference, leading to a 15% revenue increase without raising their budget.
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Channel comparison
Reported vs. true incremental ROAS
Data relevant to: Case Study: Beauty Brand Optimizes TikTok vs Meta Budget Spl
The Problem: Misleading Metrics
A rapidly growing Shopify beauty brand in the Netherlands, with a revenue of 15M EUR, found itself at a crossroads. Their 150,000 EUR monthly ad spend was evenly split between Meta and TikTok. The problem? Their standard last-click attribution model painted a rosy, but ultimately flawed, picture. Both channels appeared profitable, but the leadership team suspected significant audience overlap and channel cannibalization. They were flying blind, unable to determine which platform was truly driving incremental growth versus simply poaching conversions that would have happened anyway.
The Solution: Intelligence-Adjusted Attribution
Instead of relying on outdated, rule-based models, the brand turned to Causality Engine. By implementing our Bayesian causal inference models, they unlocked a true understanding of marketing impact. Our Intelligence-Adjusted Attribution feature went beyond surface-level clicks to measure the actual incremental lift of each channel. The core of this analysis lies in calculating the incremental Return on Ad Spend (iROAS), using the formula: iROAS = (Total Attributed Revenue - Organic Revenue) / Ad Spend. This approach isolates the true causal effect of each marketing dollar.
Causality Engine's analysis revealed a provocative insight: while Meta boasted a higher last-click ROAS, TikTok was the real engine for new customer acquisition, delivering a significantly higher incremental lift. The Causality Chain Visualization tool made this undeniable, mapping the complex customer journeys that last-click models completely miss.
The Results: 15% Revenue Lift, Zero Extra Spend
Armed with causal data, the brand made a decisive, data-backed move. They reallocated their budget, shifting to a 70/30 split in favor of TikTok. The outcome was immediate and impressive. Within just 60 days, they achieved:
A 25% increase in new customer acquisition.
A 15% lift in overall store revenue.
Zero increase in their total advertising budget.
This case study demonstrates the power of moving beyond correlation to causality. By understanding the true incremental impact of each channel, the brand was able to sharpen their marketing mix for real growth, not just vanity metrics. They stopped wasting budget on channels that were cannibalizing organic sales and doubled down on the platform that was genuinely expanding their customer base.
Ready to uncover the true performance of your marketing channels? Causality Engine provides the clarity you need to make profitable decisions. Stop guessing and start measuring what matters.
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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 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.
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.
Marketing Mix
The marketing mix is the set of actions a company uses to promote its brand or product. It traditionally includes product, price, place, and promotion.
Subscription Model
Subscription Model is a business model where customers pay a recurring price for product or service access. It generates consistent revenue streams.
Vanity Metrics
Vanity Metrics are measurements that look good but do not provide insight into business performance or aid decision-making. They do not reflect true impact.
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Frequently Asked Questions
How does Causality Engine measure incremental lift?
Causality Engine uses Bayesian causal inference models to isolate the true impact of your marketing activities. We compare the behavior of customers exposed to marketing with a synthetic control group that was not, allowing us to measure the actual incremental revenue generated, not just correlation.
What is the difference between last-click and causal attribution?
Last-click attribution gives 100% of the credit for a conversion to the final touchpoint. Causal attribution, like our Intelligence-Adjusted Attribution, analyzes the entire customer journey to determine the statistical probability that a channel actually *caused* the conversion, providing a much more accurate view of its true value.
How long does it take to see results with Causality Engine?
You can get your first one-time analysis with a 40-day lookback period within 48 hours. For subscribers, our platform provides continuous, real-time insights. Most brands, like the one in this case study, are able to implement changes and see a measurable lift in revenue within 60 days.