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

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.

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