A top 3 U.S. auto insurer was managing a $1B media portfolio with limited visibility into what was actually working. Budget allocations were driven by historical precedent and vendor relationships rather than measured performance.
Without channel-level return data, the team had no basis for making reallocation decisions — and significant spend was going to underperforming channels as a result.
I led a Marketing Mix Modeling engagement to quantify return on ad spend at the channel level. The models accounted for lag effects, saturation curves, and cross-channel interactions — moving beyond last-touch attribution to a fuller picture of how media investment drove policy sales and quotes.
Once the models were validated, I worked with the client's marketing and finance teams to translate the findings into actionable reallocation recommendations — identifying which channels were under-invested relative to their marginal return, and which were receiving spend beyond their efficient frontier.
The engagement gave the client a defensible, data-driven framework for budget decisions across a $1B portfolio — replacing gut and precedent with a model they could update as market conditions changed.