How ePPC Solved Ecosh's Attribution Puzzle and Grew Revenue by 56%
When GA4 said Meta Ads drove 6% of revenue and Meta's own reporting claimed 54%, Ecosh needed a better answer. Marketing Mix Modeling gave them one — and the confidence to make budget decisions that led to record-breaking results.
Ecosh Life
Ecosh Life is Estonia's first dietary supplement manufacturer and retailer, offering high-quality natural supplements made from the best exotic and local herbs. Their products are 100% GMO-free, made from the finest raw materials to support health and well-being.
The company sells primarily through its own ecommerce store and runs paid campaigns across Google Ads and Meta Ads to drive revenue.
Three sources, three different stories
In the first two quarters of 2024, Ecosh faced a measurement crisis that many ecommerce brands know well. Google Ads claimed it was responsible for 51% of all revenue. Meta Ads claimed 54%. And GA4 — their supposed source of truth — credited Meta with just 5.89%.
The numbers couldn't all be right. And the stakes were real: every budget decision was being made on data that contradicted itself.
Meanwhile, GA4 told a very different story
GA4's last-click attribution model heavily favors lower-funnel touchpoints — organic search, email, and direct visits. According to GA4, Meta Ads contributed just 5.89% of revenue, while email marketing via Klaviyo accounted for 13.7%.
This created a practical problem: if the team trusted GA4 at face value, they would have cut Meta spend significantly. But Meta Ads was potentially driving the brand awareness that fueled all other channels.
The question the team needed to answer: What is the true causal impact of each channel, independent of how platforms choose to count credit?
Identifying the real drivers with causal discovery
Rather than relying on any single platform's attribution, we built a Marketing Mix Model that uses Bayesian statistics and causal discovery algorithms to identify what actually drives revenue.
- ✓Used expert knowledge and a Causal Discovery Algorithm to map the relationships between channels, brand interest, and revenue
- ✓Applied ad stock transformations to capture the lingering impact of ads days and weeks after exposure
- ✓Modelled saturation effects so we could see exactly where diminishing returns began
Key finding: After adjusting for all factors, Meta Ads had the strongest positive impact on revenue — something neither GA4 nor platform-reported attribution could reveal.
Meta's marginal impact was 2.5× higher than Google's
The MMM showed that for every additional euro spent, Meta Ads generated significantly more incremental revenue than Google Ads — especially in the mid-spend range where Ecosh was operating.
This was invisible in GA4's reporting because Meta's impact worked indirectly: it drove brand awareness, which then converted through organic search, direct visits, and email — channels GA4 gave all the credit to.
- ✓Traditional attribution models failed to anticipate Meta's performance at scale
- ✓The model also showed diminishing returns, helping the team avoid overspending
Simulating outcomes before committing budget
With the model in place, the team built a forecasting system that could predict next month's revenue based on planned spend. This wasn't a black-box forecast — it was transparent, showing worst-case, most probable, and best-case scenarios.
- ✓Thousands of Monte Carlo simulations incorporating real-world uncertainty
- ✓Each month, the team reviewed risk/return trade-offs before allocating budget
- ✓The system used planned spend, dates, and historical patterns to estimate impressions, clicks, and revenue
Record-breaking quarter driven by better decisions
With the MMM guiding budget allocation from September through Q4 2024, Ecosh achieved the strongest period in the company's history — surpassing their annual growth target in just four months.
YoY (Sep + Q4)
vs prior year
Down from 13.6%
The period included two of the highest revenue months in the company's history, and the year closed with the highest annual revenue ever recorded — all while media spend as a percentage of revenue actually decreased.
Key takeaways
GA4 systematically undervalues Meta Ads because its last-click model gives credit to lower-funnel touchpoints. The real contribution of upper-funnel channels is invisible in standard analytics.
Meta has a broader ecosystem impact than direct-response metrics suggest. It boosts brand interest, which then drives conversions through organic search, direct traffic, and email.
Standout creative is the catalyst. The brand interest increases were often driven by distinctive ad creative that sparked curiosity and tied all brand elements together.
Facing a similar attribution challenge?
If your GA4 and ad platform data don't match — you're not alone. Book a free call and we'll assess whether Marketing Mix Modeling is the right fit for your business.
Or write to us: karl@ppc.ee