INCRMNTAL vs. Fospha

What are the differences between INCRMNTAL and Fospha? Both platforms promise better marketing measurement. The real difference is philosophical. Fospha asks, “Who touched the user?” INCRMNTAL asks, “Did this campaign actually yield incremental results?”

INCRMNTAL is not a multi-touch attribution platform, and that is by design. It was built in response to the structural weaknesses of user-level tracking, the foundation on which platforms like Fospha operate. For years, marketers believed stitching every touchpoint together would unlock clarity.

But stitching journeys is not the same as proving impact. Even before privacy changes, path-based attribution relied on correlation, not causation. Now, with cookie deprecation, limited mobile identifiers, and an increasing number of brands operating campaigns in an omni-channel world, that correlation has weakened further. 

The issue is not that measurement suddenly broke. 

The issue is that assigning credit was never the same as measuring incremental impact.

The Ceiling of Multi-Touch Attribution

Fospha’s model depends on reconstructing user journeys across devices and channels. That assumption is becoming fragile across mediums – influencers, CTV, linear TV, OOH, Mobile Apps, Web. When identity resolution becomes partial or probabilistic, the output inherits those weaknesses. The result may look precise, but that does not equal accuracy.

Key limitations include:

• Signal Dependency: Multi-touch attribution depends on persistent identifiers. As those identifiers fragment or disappear, model reliability declines. A partial journey cannot produce a complete truth.
• Correlation vs. Causation: Seeing a touchpoint before a conversion does not prove it caused the conversion. It only proves proximity.
• Upper-Funnel Blind Spots: Channels like CTV, podcasts, influencer, and DOOH do not fit neatly into stitched user paths. MTA platforms often undervalue them or rely heavily on modeled assumptions.

This creates a core problem. If a customer would have converted anyway, attribution models still distribute credit across observed touchpoints. They cannot distinguish between influence and inevitability.

Would this conversion have happened without paid search?
Is retargeting generating new demand or capturing existing intent?
Are upper-funnel campaigns driving lift or just appearing early in journeys that were already in motion?

Attribution answers who was present. It does not answer what changed.

Causal Measurement: The INCRMNTAL Approach

INCRMNTAL starts from a very different premise. Instead of reconstructing user paths, it measures incremental impact against a modeled counterfactual baseline, while banning PII data all-together. The question is not who touched the user. The question is what would have happened if this change had not occurred.

Using always-on causal AI, INCRMNTAL continuously measures the incremental contribution of each and every change, starting with ad groups, campaigns, channels, and mediums, across every country, and OS. It was designed for a privacy-first ecosystem where user-level data is limited and often misleading. INCMRNTAL never required PII or any other user data from customers. Rather than fighting signal loss, it operates at the aggregate level where statistical validity is stronger.

This enables practical, budget-driving answers:

• What is the marginal CPI if we increase TikTok spend by 15 percent?
• If we pause retargeting, how much incremental volume do we actually lose?
• What is the true incremental ROAS of CTV or influencer investment?
• Which channels are cannibalizing organic demand?

This is not about creating a narrative of participation. It is about measuring contribution. Cross platform advertisers do not care about fractional credit across a journey. They care about incremental impact on revenue.

In Summary

The difference between Fospha and INCRMNTAL is structural. Fospha refines multi-touch attribution to map journeys and distribute credit, an approach that depends on identity resolution and path reconstruction.

INCRMNTAL measures causality. It focuses on what actually moved the needle.

In a world defined by privacy constraints and fragmented signals, the question is no longer who gets the credit. It is what truly drove growth. If the goal is to optimize media based on incremental business outcomes rather than modeled paths, the shift is clear: from attribution to incrementality.