Lifesight vs Other Attribution Vendors

Feature	Description	Lifesight	Traditional Attribution Vendors
Incrementality Measurement	Attribution outputs are grounded in modeled and experimental incrementality, ensuring reported impact reflects true causal lift rather than observational correlation.	✅	❌
Privacy-First Measurement	Designed for privacy-safe measurement using aggregated, modeled, and experiment-calibrated signals without reliance on user-level tracking or identifiers.	✅	❌
Multiple Attribution Methodologies	Supports multiple attribution approaches (rules-based, MTA-style signals, and modeled attribution) rather than locking users into a single method.	✅	⚠️
Calibration with Experiments	Attribution results are calibrated using geo and platform experiments to correct systematic bias and over-attribution.	✅	❌
Calibration with Modeled Incrementality	Uses MMM- and causal-model-derived incrementality factors to align attribution credit with long-run business impact.	✅	❌
Granular Attribution Outputs	Provides attribution at high granularity—campaign, ad set, ad ID, audience, creative, and placement—without sacrificing causal alignment.	✅	⚠️
Cross-Channel Consistency	Ensures attribution results are consistent with cross-channel incrementality and total demand constraints.	✅	❌
Bias & Overcounting Detection	Actively diagnoses and corrects common attribution biases such as last-touch inflation, platform self-attribution, and channel overlap.	✅	❌
Generalization Beyond Observed Paths	Generalizes attribution insights beyond observed user paths using calibrated models, enabling attribution in privacy-restricted environments.	✅	❌
Actionable Optimization Signals	Produces optimization-ready signals that align tactical decisions (bids, budgets, creatives) with true incremental value.	✅	⚠️