Tim Wilson discusses the logical frameworks required to prove cause-and-effect relationships in marketing data when definitive proof is difficult to obtain.
Tim Wilson discusses the logical frameworks required to prove cause-and-effect relationships in marketing data when definitive proof is difficult to obtain.
Justin Rashidi explains how to move beyond simple correlation to understand true cause-and-effect relationships in marketing despite privacy-related data gaps.
Tom Leonard discusses operationalizing Media Mix Modeling (MMM) and incrementality testing to build more causal marketing models.