Domino’s, Asahi see executive leaders buy-in to better decision-making; now moving Mutinex MMM beyond media’s P&L impact into business planning

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Domino’s and Asahi are both using Mutinex’s GrowthOS platform to make very different media investment decisions, faster, in a fluid market. Both have buy-in across the business after unlocking the impact of media investment on sales. Both are now taking the platform beyond media and into decisions around seasonality, pricing and planning. “It’s not just a marketing tool, it’s a finance tool, ultimately supporting you how to maximise your investment across the portfolio,” says Jemma Downey, Group GM of Commercial Excellence at Asahi. “We definitely have an intention of looking at how we can ingest the data from our trade promotion and our trade optimisation tools to look at price elasticity.”

Asahi has been using the GrowthOS platform for four years. Over that time it has “significantly changed” its channel allocation, per Downey, with the ROI data accelerating its shift from TV to digital video while answering questions around the effectiveness of social channels. “Social delivers the highest ROI on average across the board,” she says. Asahi is now also “far more fluid” with its investment approach.

Domino’s Group Digital Strategy Manager, Blake Rand, likewise has executive buy-in with the pizza giant also using the platform beyond media and into pricing decisions. In terms of media channel allocation, GrowthOS has also thrown up some interesting insight – like the power of old-school flyers through letterboxes. Easy to see as “antiquated”, says Rand, “but we're still seeing a really high commercial impact from that investment”.

Both Rand and Downey are moving towards predictive media budget and channel allocation. Mutinex CEO Henry Innis thinks that development will further underline the platform’s credentials – because brands can save Mutinex’s forecasts. “Anybody in the business of predictive analytics who doesn’t give you the functionality to save those predictions so you can hold them to account,” per Innis, “is probably lying about how predictive their model is.”

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