Pinterest shows 1.6x ROAS.
Meta shows 3.9x.
Which one do you scale?
For Dutch Shopify home & living brands scaling from €100K to €400K/month. Upload your GA4 data. Know which channels drive incremental sales in minutes.
€99. Results in minutes. Full refund if you don't see it. 84% stay.
The €210K question: Is Pinterest working or stealing credit?
You're spending €250K/year on Pinterest. Room inspiration boards. Shopping pins. Home renovation content.
Pinterest dashboard says 1.6x ROAS. Meta dashboard says 3.9x. But what if Pinterest creates the room-planning discovery that Meta converts 4-8 weeks later?
The math:
Pinterest "attributed" revenue: €250K × 1.6x = €400K
Pinterest incremental revenue: Test market shows 33% drop = €210K lost
Difference: €210K revenue Pinterest drives but doesn't get credit for.
Cut Pinterest = save €250K, lose €210K revenue, collapse funnel. Keep Pinterest = justify "low ROAS" to CFO forever.
You're stuck. Because you're measuring correlation (last click), not causality (what actually drives sales).
Correlation vs Causality: The difference is €210K
Traditional attribution (correlation):
Meta had the last click → Meta gets 100% credit → Pinterest shows "low ROAS" → Cut Pinterest → Funnel collapses.
Behavioral intelligence (causality):
Pinterest creates room inspiration → Google Shopping drives comparison → Meta converts → That's a 45-day customer journey → Pinterest drives €210K incremental revenue → Keep Pinterest, scale it.
Incremental Sales = (Revenue with Channel) - (Revenue without Channel)
Traditional attribution: Measures correlation (which channel touched the customer last)
Behavioral intelligence: Measures causality (which channel drives incremental sales)
Confidence-scored results with data health indicators. 964 companies. 38 Dutch home & living brands already see their real numbers.
How home & living customers actually buy: The paths your dashboard hides
Path #1: Pinterest → Google Shopping → Meta
Customer pins room inspiration (Day 1) → Browses Google Shopping for specific products (Day 20) → Clicks Meta retargeting ad (Day 45) → Buys.
Last-click: Meta gets 100% credit. Pinterest shows "low ROAS."
Causal analysis: Pinterest drives 33% of this sale. Cut Pinterest = lose this customer entirely.
Path #2: Instagram → Pinterest → Blog → Google → Purchase
Customer sees home makeover on Instagram (Day 1) → Creates Pinterest board for renovation (Day 7) → Reads style guide blog posts (Day 21) → Googles specific product (Day 40) → Buys direct (Day 55).
Last-click: Google gets 100% credit. Instagram and Pinterest show zero ROI.
Causal analysis: Instagram + Pinterest drive 45% of this sale. Cut them = 38% revenue drop.
Path #3: TikTok → Pinterest → Google Shopping → Meta
Customer sees TikTok home transformation (Day 1) → Pins individual products (Day 10) → Compares on Google Shopping (Day 30) → Clicks Meta ad (Day 50) → Buys.
Last-click: Meta gets 100% credit. TikTok and Pinterest show "low ROAS."
Causal analysis: TikTok + Pinterest drive 42% of this sale. Cut them = collapse top-of-funnel discovery.
Pattern: Discovery and planning channels (Pinterest, Instagram, TikTok) show "low ROAS" in dashboards. But they drive 40-55% of incremental sales through room-planning journeys that span 30-60 days. Cut them = collapse your funnel.
Dutch brands using CE
Avg ROI increase
Customer retention
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Why home & living attribution is harder than other verticals
30-60 day consideration cycles
Fashion: 3-7 days. Beauty: 14-30 days. Home & living: 30-60 days. Customers plan rooms, compare styles, measure spaces, and coordinate purchases. The longest consideration cycles in DTC = the most attribution errors. Last-click gives 100% credit to the final touchpoint, ignoring 10-20 prior planning interactions.
Highest AOV in DTC (€100-€300)
Higher price points mean more research, more comparison shopping, and more touchpoints before purchase. A €250 designer lamp gets researched across Pinterest, Instagram, Google Shopping, and review sites. Every channel claims credit. None show the full picture.
Seasonal renovation peaks
Home renovation season (spring/summer) drives 60% of annual revenue. Attribution models trained on off-season data fail during peak. Channel effectiveness shifts dramatically with seasons. Static attribution models miss seasonal demand creation entirely.
Bottom line: Home & living brands have the longest consideration cycles, highest AOV, and most seasonal demand patterns in DTC. Traditional attribution fails hardest here. Causal analysis works best here.
38 Dutch home & living brands already see their real numbers
280%
Average ROI increase
A+
Data health score
84%
Stay because the data is undeniable
964 companies switched to behavioral intelligence. Not because we're great salespeople. Because once you see which channels drive incremental sales, you can't unsee it.
Learn the fundamentals
Attribution Model
Defines how credit for conversions is assigned to marketing touchpoints.
ROAS
Measures the revenue earned for every dollar spent on advertising.
Causal Inference
Determines the independent, actual effect of a phenomenon within a system.
Cross Channel Attribution
Determines how different marketing channels contribute to customer conversions.
Compare attribution tools
Common questions from Dutch home & living brands
Why does Pinterest show 1.6x ROAS while Meta shows 3.9x for my Dutch home & living brand?
Pinterest creates room inspiration that Meta converts 30-60 days later. Last-click attribution gives 100% credit to Meta, but Pinterest started the planning journey. Cut Pinterest = 33% revenue drop in 30 days.
How do I know if cutting Pinterest will collapse my home & living funnel or save money?
Upload your GA4 CSV and get causal inference analysis in minutes. Measures incremental sales: Revenue with Pinterest - Revenue without Pinterest. If Pinterest drives room-planning discovery that feeds conversions weeks later, cutting it collapses the funnel. Confidence-scored results.
What is the difference between correlation and causality in home & living marketing attribution?
Correlation: Meta had the last click, so it gets 100% credit (wrong). Causality: Pinterest created room inspiration, Google Shopping drove comparison, Meta converted (right). Traditional attribution measures correlation. Behavioral intelligence measures causality.
Can I see which channels drive sales for my Shopify home & living brand with GA4 data?
Yes. Upload your GA4 CSV and get results in minutes. Connect Shopify to enhance the analysis with revenue data. Requires 3+ months of historical data. 38 Dutch home & living brands already use it.
How does seasonal demand affect attribution for home & living brands?
Renovation season drives 60% of annual revenue. Pinterest discovery in January converts in April. That's a 90-day gap that last-click misses. Causality Engine adapts to seasonal patterns and reveals demand creation vs. capture.
Know which channels drive incremental sales
Not guesses. Not correlations. Upload your GA4 data and see the real numbers in minutes.
Book a Demo€99. Results in minutes. Full refund if you don't see it. 38 Dutch home & living brands already know.
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