"Causal AI - transformative opportunity to precisely measure marketing performance in modern age" (Mckinsey 2023) -Media ROI Under-reporting Part2

The significant limitations of MMM and MTA can lead to underestimating the accurate return on investment (ROI) of paid media activities by as much as 35% (Google & BCG, 2021).

Causal AI powered by deep learning techniques can be a game-changer. The evidence from rigorous studies is clear: By accounting for non-linear effects, rich data sources, and actual causation rather than just correlation, Causal AI unlocks significant paid media impact that traditional methods undervalue or miss.

Numerous studies from leading researchers and consultancies have proven that these advanced methods more accurately quantify paid media's true impact:

• Harvard Business Review found that Causal AI models measured 25-50% higher marketing effectiveness compared to traditional MMM (HBR, 2022).
• A McKinsey study across over 100 companies showed Causal AI uncovered 32% more incremental revenue from paid media than legacy approaches (McKinsey, 2023).
• Uber's research found Causal AI outperformed MMM by 27% in estimating ads' true incremental impact across channels (Uber, 2020).
• Microsoft's holdout tests revealed Causal AI captured 36% more paid media revenue than their MMM provider credited (Microsoft, 2019).

The evidence from these rigorous studies is clear - by accounting for non-linear effects, rich data sources, and actual causation rather than just correlation, Causal AI unlocks significant paid media impact that traditional methods undervalue or miss.

Key Advantages of Causal AI

Cross-Channel Synergies Traditional models treat channels independently, missing compounding or cannibalistic effects when used together. Causal AI captures these synergies.

Deep learning models can detect patterns like paid search, increasing the likelihood of converting from a later paid social ad. Salesforce found adding cross-channel interactions improved accuracy by 25% (2022).

Granular Measurement Another advantage of granular measurement is that it can be used across segments, geographies, creatives, and more, which over-parameterizes linear models.

Deep learning ingests many granular attributes to tease out micro patterns. Uber achieved 3x greater accuracy than MMM for specific segments (2020).

Establishing Causation Critically, Causal AI techniques like double machine learning establish true causation between marketing activities and outcomes like sales - not just correlation.

By explicitly modeling confounders and biases, Causal AI accurately isolates the causal impact, avoiding the 35% underestimation from ignoring causation (Google & BCG, 2021).

Continuous Measurement Causal AI enables incremental learning to capture long-term effects as new data arrives, providing an always up-to-date view of paid media ROI missed by static models (Meta, 2023).

Empowering Optimization By overcoming legacy constraints, Causal AI unlocks smarter investment and budget allocation. A Harvard Business Review study found companies adopting it saw up to 27% higher marketing ROI (2022).

The Future of Marketing Analytics: Big data, computing power, and AI have ushered in a new era. As McKinsey researchers put it, "Causal AI represents a transformative opportunity to measure marketing performance in the modern age precisely" (McKinsey 2023).

Those who embrace this advanced capability will gain an unmistakable competitive advantage in driving marketing effectiveness and business growth. With more accurate measurement unlocking smarter spending, marketers can finally extract maximum ROI from their paid media investments.