How We Helped a Retailer Reduce Margin of Error by 30% With Holistic MMM
How we stopped weather from taking credit for media and gave a seasonal retailer a model it could actually trust.
The Story
For retailers selling seasonal products, weather isn’t just context, it’s a performance driver in its own right. A hotter-than-average summer can spike sales regardless of what’s running on TV. A late spring can suppress demand no campaign could overcome. When weather and media move together, it’s easy to mistake one for the other.
That’s exactly what was happening here. Media appeared to be driving strong results in seasonal categories, but a significant portion of that signal was actually weather. Without accounting for temperature swings and climate anomalies, the model was giving media credit it hadn’t earned, and decisions were being made on a distorted picture.
We addressed this by shifting from a traditional Media Mix Model to a holistic Marketing Mix Model. The result was a cleaner, more accurate read on what media was actually doing.
Our Impact
30% Reduction in Margin of Error
Giving the client a reliable foundation for planning and optimization
10% of Media Overvaluation Corrected
Before and After weather correction: a material bias that was redirecting budget based on false signals
How We Solved This Large Retailer’s Problem
Problem
Sales in this retailer’s key categories were highly weather-dependent. Without controlling for temperature swings and seasonal anomalies, media was absorbing credit for results it hadn’t driven. The model was systematically overvaluing media performance, creating a false baseline that made optimization impossible and eroded confidence in the outputs.
Solution
We moved from a standard Media Mix Model to a holistic MMM framework, integrating weather variables including temperature anomalies, and seasonal demand shifts as explicit model inputs. By adjusting for climate variation before assessing media performance, we ensured that every channel was evaluated against a clean, weather-corrected baseline. The result was a model built to reflect reality, not just correlation.
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