The Challenge
The client, one of the UK’s largest supermarket chains, needed a more actionable measurement framework to understand the impact of its marketing activity.
Its incumbent partner had built a technically impressive store-level modelling suite, but it was rarely used in practice because media planning was not done at store level. As a result, the outputs were difficult to activate and had little influence on planning.
The brief was to design a more strategic solution that retained analytical rigour, could be refreshed quarterly, and aligned with how the business actually made decisions.
The Solution
To ensure long-term relevance, we designed a marketing mix modelling suite structured around the client’s internal Commercial Planning Divisions. That meant Fresh Produce, Chilled & Frozen, Groceries & Dry Goods, Household Items & Clothing, Health & Beauty, and General Merchandise each had access to insights that reflected their own trading patterns, seasonality profiles, and promotional cycles, while still supporting a joined-up view of total marketing performance.
We also built in automated quarterly refreshes so analysts could update models and rerun outputs with minimal friction. Broader business initiatives, including the client’s Price Match programme, were incorporated so the framework aligned more closely with internal commercial analysis.
The result was a measurement framework that fit how the business actually planned, making outputs easier to trust, use, and maintain whilst keeping high technical and analytical rigour.
The Impact
Within six months of deployment, the new MMM framework was already influencing media planning across three divisions. Marketing teams were able to shift investment using clearer incremental ROI estimates, improving allocation across both traditional and digital channels.
Overall ROAS remained in line with the client’s previous modelling suite, but the new structure made reporting far more actionable. Aligning the framework to Commercial Planning Divisions drove a 21% improvement in overall budget efficiency, measured through internal ROI benchmarks and externally validated econometric outputs.
Crucially, because the system matched how commercial teams already structured planning and forecasting, insights were easier to embed into day-to-day decision-making.
Key Takeaways
Division-aligned MMM – Structuring outputs by Commercial Planning Division increased relevance and adoption across the business.
Reduced complexity – Moving away from store-level modelling improved clarity and made the framework easier to use.
Regular refresh cycles – Built-in updates made the solution more sustainable for the client’s analytics team.
Commercial fit – Incorporating initiatives like Price Match strengthened credibility and stakeholder trust.
Tools and Techniques
- Data Mapping to Commercial Planning Divisions
- Marketing mix modelling
- SQL for data extraction and transformation
- R for modelling
- Snowflake (Data warehousing)
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