The Buried Secret: Marketing ROI Underreported by a Staggering 35% (Part 1)".

This dirty little secret has persisted for too long, whispered about but never openly addressed with credible evidence and solutions until now.

Traditional marketing mix modeling (MMM) and multi-touch attribution (MTA) methods should value and recognize paid media more. The extent of this undercounting is far more severe than most marketers realize.

A Harvard Business Review study found that MMM models undervalue paid media's impact by a staggering 15-30% on average. On the attribution front, Gartner research reveals up to 60% variation in credit assignment across different MTA models for the exact same conversion paths.

Let that sink in - our established, industry-standard measurement approaches are flawed to the point of misjudging paid media performance by as much as 30-60%. The implications are deeply unsettling.

This dirty little secret has persisted for too long, whispered about but never openly addressed with credible evidence and solutions, until now.

The reasons behind this undercounting are multifaceted.

Not able to measure cross-channel synergies/ interactions: As a McKinsey study highlighted, ignoring these higher-order interactions leads to undervaluing drivers by up to 40%. A Forrester analysis showed that MMM's period-based siloing systematically understates paid media's investor-year effects.

Correlation and not causation: But most critically, MMM and MTA need to establish true causality behind the media's incremental impact on consumer behavior. As a Marketing Science Institute paper found, they conflate correlated factors with actual causal drivers.

Ignoring long-term effects of media: MMM's static modeling approach ignores compounding long-term media effects across years, undervaluing long-term impacts. Both MMM and MTA use linear regression, which is unable to capture the higher-order, non-linear dynamics of cross-channel marketing.

The undercounting has been an open secret marred in imprecise analytical methods. But no more...

Here's where Data Poem corrects the wrong.?

We leverage the power of Causal AI and Deep Learning to provide a more comprehensive view of marketing attribution. In fact, a 2021 study by Google, a 2022 paper in the Journal of Marketing Research, and a 2023 report by the Massachusetts Institute of Technology (MIT) have all highlighted the superior performance of Causal AI and Deep Learning in solving the limitations of traditional MMM and MTA.?

Together, we can build a better new paradigm to get a comprehensive picture of ROI and optimize further.