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Case Study

Domino's & Asahi

A tale of two market mix modelling rollouts

Author
Claire Bertolus
Published
15 August 2024
Industry
Food and Beverages
Region
APAC
I think MMM is normally a pretty good sanity check for data maturity. If you're doing MMM, it's going to test how robust your collection and provision processes are.
Henry Innis

Henry Innis

CEO and Co-Founder, Mutinex

Overview

Summary

Pizza and beer complement each other well, yet Domino's and Asahi adopted contrasting strategies for implementing market mix modelling. Domino's leveraged existing data infrastructure, while Asahi employed MMM as a mechanism to transform planning and execution methodologies. Both organizations now access faster performance feedback regarding marketing ROI and growth outcomes.

Data Management Context

Market mix modelling demands two essential data categories: marketing expenditure figures and revenue/sales metrics. While straightforward conceptually, companies struggle with implementation due to campaign complexity and multi-unit revenue streams.

Domino’s Approach

Blake Rand joined Domino’s as Group Digital Strategy Manager in 2023, tasked with implementing measurement and optimization excellence for media decisions. The company had previously conducted project-based market mix modelling and possessed executive alignment around performance metrics. Domino’s organizational culture emphasized sales focus and ROI as fundamental marketing measurements.

Rand successfully advocated for continuous market mix modelling to support consistent optimization across multiple markets.

The Core Distinction

Domino’s had prior MMM experience and existing data infrastructure, while Asahi used MMM as strategic leverage to transform planning processes. The same tool, two very different starting points — and two very different journeys into measurement maturity.

What they said

  • Blake Rand

    Group Digital Strategy Manager, Domino's

    Domino's has always been very focused on sales, contribution to sales and ROI as key marketing metrics.

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