Marketing mix modeling (MMM) has seen a surge of popularity in marketing analytics in recent years, with open-source solutions making it more accessible than ever. But alongside these advances come critical challenges that can make or break decision-making.
In a recent Marketing Accountability Standards Board (MASB) webinar, our Executive Partner Dr. Peter Cain explored the structure and outputs of modern MMM approaches, highlighting two major pitfalls that organizations must address:
- Meaningful estimation of long-term marketing effects: most models treat “long-term” as just a slow decay of short-term sales, but true long-term impact comes from marketing that shifts the baseline and builds lasting growth. Dr. Cain recommends explicitly modeling both short-term and long-term networks, separating temporary call-to-action effects from persistent baseline growth.
- The challenge of valid causal inference: beyond technical issues like autocorrelation, the core challenge is the identification problem – ensuring coefficients capture true cause and effect rather than bias from confounders or simultaneity. Quick fixes, such as nested models or reallocating search to TV, often fall short. Dr. Cain emphasized the value of robust econometric tools like panel models, instrumental variables, or DAG analysis, among others, paired with expert judgment, to separate correlation from causation and make MMM outputs reliable inputs for business decisions.
For marketing leaders, analysts, and data scientists, grappling with these challenges is not just a technical exercise, it’s the key to ensuring that MMM delivers insights that are both accurate and actionable, driving smarter investment decisions and more sustainable growth.
If you’d like to explore how these principles can strengthen your company’s marketing effectiveness and measurement strategy, get in touch with our team.
