Marketing measurement has gone through three distinct phases: 1. Legacy MMM Era (pre-2010s) 2. Attribution Boom (2010s) 3. MMM Rebirth (2020s) This isn’t a story about preference. It’s a story about adaptation to thrive in the everchanging business world.

Data Detail is Crucial: To perform causal analytics like Marketing Mix Modeling, you must preserve raw, event-level data (joint distributions). Summary reports (marginals) discard the necessary context to attribute success to specific combinations of customer traits. Avoid the "Average" Trap: Aggregated data can hide the true story. Two completely different customer behaviors can look identical in summary reports, making accurate attribution impossible without the raw source

Marketers have more performance data than ever, yet trust in that data isn’t really improving. The fix lies in performance measurement using Marketing Mix Modeling.

AI is redefining finance, but trust remains its currency. As AI systems make increasingly high-stakes decisions—who gets a loan, who’s flagged for AML, who receives an offer—the cost of error multiplies. The message for financial leaders is clear: A black-box AI might optimize for accuracy but can expose the bank to unseen bias, regulatory penalties, or wasted budgets. A Responsible AI framework builds resilience, customer confidence, and long-term value.

Going beyond correlation to causality can reveal tremendous value and insights Key Takeaway Drug discovery has entered the era of big data. Every year, thousands of disease and normal samples are profiled — yet one critical bottleneck remains: identifying clinically meaningful target–marker gene pairs for new drug development . The core value of Apotek Path is for this purpose: Identify disease-relevant targets → Causal modeling reveals mechanistic gene interactions tailore

Why causal AI is becoming pharma’s next strategic differentiator — from target validation to clinical trials and real-world evidence. Visualizing the Ladder of Causation in Pharma: from seeing correlations, to doing interventions, to imagining counterfactuals — the foundation of causal AI in drug discovery. Introduction: The Limits of Correlation in Drug Discovery Drug discovery today generates unprecedented volumes of data. Omics, imaging, electronic health records, and real

Ad platform reports from Google, Meta, and others usually over-credit the last click and don’t reveal the true drivers of ROI....

Empowering Clinicians and General Populations with Actionable Insights Diabetes is one of the world’s fastest-growing health crises,...

Authors: Drs. Hung-Ken Chien and Yu-Feng Wei, Co-Founders of Vizuro Introduction In the ever-evolving pursuit to measure promotional effectiveness, marketing mix modeling (MMM) has long been the norm to assess campaign lifts and help allocate their spending. However, the emergence of omnichannel marketing in recent years has imposed a great challenge to the conventional MMM methodology, due to its requirements for high tactic mix and segment granularity. Coincidentally, both

Expanding Global Reach with Kolr's Rebranding and Cutting-Edge AI Technologies (Left to right) Vizuro Co-founder and CEO, Yu-Feng Wei;...

Key Takeaways: The 2024 Nobel Prize in Chemistry was awarded for groundbreaking advancements in computational protein structure...