Causality Engine vs Triple Whale vs Northbeam vs Rockerbox: Causality Engine uses causal inference (95% accuracy). Triple Whale, Northbeam, and Rockerbox use multi-touch attribution (30-60% accuracy). Upload GA4 data and see the difference in minutes.
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Quick Answer
Causality Engine uses behavioral intelligence (causal inference) to achieve 95% attribution accuracy. Triple Whale, Northbeam, and Rockerbox use multi-touch attribution (correlation) with 30 to 60% accuracy. The key difference: Causality Engine measures what happens to revenue when a channel is removed, not just which channels were present before a sale. For Shopify beauty and fashion brands, this reveals hidden revenue paths that correlation-based tools miss entirely.
Key Takeaways
1. Causality Engine uses causal inference (95% accuracy). Competitors use multi-touch attribution (30 to 60% accuracy).
2. Causality Engine measures incremental revenue per channel. Competitors distribute credit using rules (linear, time-decay, position-based).
3. Causality Engine identifies causality chains (TikTok to Meta, Pinterest to Google). Competitors show touchpoint sequences without causal relationships.
4. Causality Engine is built specifically for Shopify beauty and fashion brands. Competitors serve all industries with generic models.
5. Causality Engine costs 99 euros for trial, €299/month. Competitors range from 100 to 2,000 dollars per month with annual contracts.
The Fundamental Difference: Correlation vs Causation
Marketing attribution tools fall into two categories: those that measure correlation (what channels were present before a sale) and those that measure causation (what channels actually drove the sale).
Correlation-based tools (Triple Whale, Northbeam, Rockerbox): These tools track which channels a customer interacted with before purchasing, then distribute credit using rules. They cannot tell you what would have happened if you removed a channel.
Causation-based tools (Causality Engine): These tools measure what happens to revenue when a channel is present versus absent. They answer the only question that matters for budget allocation: does this channel drive incremental sales?
Causality Engine vs Triple Whale
Triple Whale uses pixel-based tracking and multi-touch attribution models. It provides a unified dashboard for Shopify brands with platform-reported data alongside its own attribution model. Accuracy: 40 to 60% for multi-channel attribution.
Causality Engine uses behavioral intelligence and causal inference. It does not just track touchpoints. It identifies which touchpoints caused the sale. Accuracy: 95% for incremental revenue attribution.
Key difference: Triple Whale tells you which channels a customer touched. Causality Engine tells you which channels actually drove the sale. For beauty brands running TikTok and Meta simultaneously, this difference can reveal 140,000 euros or more in hidden revenue.
Causality Engine vs Northbeam
Northbeam uses machine learning and multi-touch attribution with custom models. It provides flexible attribution windows and cross-device tracking. Accuracy: 50 to 65% for multi-channel attribution.
Causality Engine uses causal inference to measure incremental impact. It identifies causality chains that show how channels interact over time. Accuracy: 95% for incremental revenue attribution.
Key difference: Northbeam distributes credit across touchpoints using ML models. Causality Engine measures what happens to revenue when channels are present versus absent. For fashion brands with 21 to 28 day purchase cycles, this reveals Pinterest to Google causality chains that Northbeam's shorter attribution windows miss.
Causality Engine vs Rockerbox
Rockerbox provides multi-touch attribution with media mix modeling capabilities. It offers broad channel coverage including offline channels. Accuracy: 45 to 60% for digital attribution.
Causality Engine focuses specifically on Shopify e-commerce with deep integration. It uses behavioral intelligence to identify causality chains specific to beauty and fashion purchase patterns. Accuracy: 95% for Shopify attribution.
Key difference: Rockerbox serves enterprise brands across all industries. Causality Engine is purpose-built for Shopify beauty and fashion brands, with models trained on the specific purchase patterns of these verticals.
Why Accuracy Matters
The difference between 50% and 95% accuracy is not academic. For a brand spending 150,000 euros per month on ads, 50% accuracy means 75,000 euros per month is allocated based on wrong data. 95% accuracy means only 7,500 euros per month is misallocated. That is a 67,500 euro per month difference in budget efficiency.
Over 12 months, that is 810,000 euros in better-allocated ad spend. Even if only 20% of that reallocation improves revenue, that is 162,000 euros in additional annual revenue from the same ad budget.
See your causality chains . €99 one-time analysis with 40-day data look-back. 95% accuracy. Full refund if you do not see it.
Frequently Asked Questions
How is Causality Engine different from Triple Whale?
Triple Whale uses multi-touch attribution (correlation, 40 to 60% accuracy). Causality Engine uses behavioral intelligence (causation, 95% accuracy). Triple Whale tells you which channels a customer touched. Causality Engine tells you which channels actually drove the sale.
Is Causality Engine better than Northbeam for Shopify brands?
For Shopify beauty and fashion brands, yes. Causality Engine achieves 95% accuracy versus Northbeam's 50 to 65%. More importantly, Causality Engine identifies causality chains (TikTok to Meta, Pinterest to Google) that Northbeam's correlation-based models miss. Causality Engine is also more affordable at €299/month versus Northbeam's enterprise pricing.
What is the difference between multi-touch attribution and causal attribution?
Multi-touch attribution distributes credit across touchpoints using rules (linear, time-decay, position-based). It measures correlation. Causal attribution measures what happens to revenue when a channel is present versus absent. It measures causation. The difference is 30 to 60% accuracy versus 95% accuracy.
Which attribution tool is best for Shopify beauty brands?
Causality Engine is purpose-built for Shopify beauty and fashion brands. It identifies beauty-specific causality chains (TikTok tutorials to Meta conversions, influencer posts to direct purchases) with 95% accuracy. Generic tools like Triple Whale and Northbeam serve all industries with 40 to 65% accuracy.
How much more accurate is Causality Engine than other attribution tools?
Causality Engine achieves 95% accuracy for incremental revenue attribution. Triple Whale achieves 40 to 60%. Northbeam achieves 50 to 65%. Rockerbox achieves 45 to 60%. The difference is the methodology: causal inference versus multi-touch correlation.
