A tale of two market mix modelling rollouts: Asahi, Domino’s build ‘north star’ metrics machines with GrowthOS. But there are different paths to success

Domino’s and Asahi have taken two very different approaches to their market mix modelling rollouts. The former had already built data muscle, the latter used the MMM as a “Trojan horse” to recalibrate the way activity is planned and executed.

A tale of two market mix modelling rollouts: Asahi, Domino’s build ‘north star’ metrics machines with GrowthOS. But there are different paths to success

Pizza and beer go together brilliantly, but Domino’s and Asahi have taken two very different approaches to their market mix modelling rollouts. The former had already built data muscle, the latter used the MMM as a “Trojan horse” to recalibrate the way activity is planned and executed. Both now have much faster feedback on where marketing investment is driving best ROI and growth impact.

Market mix modelling has come a long way in just five years, with the introduction of SaaS based platforms like Mutinex GrowthOS promising ease of use, speed to insight and a wealth of predication capabilities to ease the path to better marketing investment decisions. With so many benefits, why would large companies not be using marketing mix models to help drive growth? For some companies the answer is data management.

Market mix models basically require two types of data. Marketing spend data allows the model to see where marketing money is being invested and revenue or sales data allows the model to see how much money the company is earning and via which revenue streams or products. It sounds like a simple set of numbers, but most marketers will be able to tell you that with a proliferation of campaigns in market and revenue generated across multiple products or business units, getting a good view of this data can seem challenging.

Henry Innis, CEO and Co-Founder at Mutinex, likens data collection for market mix models to a muscle that businesses must learn to flex. But once the muscle has been trained, flexing it becomes second nature.

“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,” says Innis. “Most businesses that have done data projects before tend to at least have some muscle around doing that versus businesses that haven’t done anything like this before who tend to need to build that muscle.”

Mutinex works with customers across the full spectrum of data maturity to ensure that MMM can deliver value quickly. Executives at Asahi Beverages and Domino’s have taken two very different approaches to their rollouts, each with their own path to that data workout.

Blake Rand rejoined Domino’s as Group Digital Strategy Manager in 2023 with a remit to ensure that business growth was bolstered by “best in class measurement and optimisation capabilities when it comes to media investment”. Domino’s had undertaken market mix modelling on a project basis in the past and Rand was already confident that buy-in on the business-focussed metrics provided by market mix modelling could be won at an executive level. “Domino’s has always been very focused on sales, contribution to sales and ROI as key marketing metrics,” says Rand. With the company geared towards growth across multiple markets, Rand was able to make a good case for an “always on” market mix that would help to optimise decision making on a regular basis.

The data ready approach: Aim for shorter feedback loops

The Domino’s data muscle was primed and ready to go. So Rand’s main challenge would be to find a solution that would ease the ongoing pain of data provision for the team and reduce time to insight. “We had completed marketing mix projects using the legacy consulting model in the past in some of our major markets. While those projects delivered a lot of value, they were definitely cumbersome.” reports Rand. “The feedback loop was long. So we’d do a project like that at best once a year, but more realistically every two years.”

“Each time around, you’re having to involve a lot of teams who provision the data and work with the consulting firms on the modelling and set them up for that data collection from scratch … that was generally a six month process. And by that time, the results are already out of date. Then you’re then left waiting until the next time around to answer questions that the model might provoke from an econometrics perspective,” he says. “We saw Mutinex really disrupting that legacy model by using a SaaS-based approach to solve a lot of the problems we’d experienced in the past and unlock the always-on approach to marketing mix that we’d developed the executive buy-in on.”

Easing data indigestion with DataOS

Mutinex DataOS has become central to easing data provision pain for Domino’s. Mutinex DataOS (a companion platform to GrowthOS) is a one-stop-shop for data cleaning and management for customers using GrowthOS. “It’s really easing that tension point around the data wrangling and ingestion that’s made these projects hard in the past,” says Rand.

The Domino’s team have already hit a monthly insights refresh cadence in Australia (with other APAC markets to follow) and the team are uncovering new optimisations quickly. “When we’re talking about investment decisions and we’re talking about media effectiveness, Mutinex is now central to those conversations, which is really great,” adds Rand.

Building data readiness with commercial impact metrics

Jemma Downey, Group General Manager, Commercial Excellence at Asahi Beverages, began a journey to implement marketing measurement at the company alongside Mutinex over four years ago. The value of market mix modelling was clear to Downey. “The big turning point for us was actually having a commercial impact metric,” she explains.

“We didn’t have a data warehouse … and the media and sales data was somewhat fragmented at the time. This rollout has been bit of a Trojan horse, if you will, in getting a more structured and organised understanding of where the pockets of data exist in our business. We’re much more sophisticated with that four years on.”

Downey unlocked movement on data with a gradual rollout of GrowthOS, first conducting a proof of concept within one brand and then gradually scaling the tool’s use across a broader portfolio.

“Sometimes that works really, really well – because the business needs to see something that’s actually demonstrating value back before you sign off on a bigger investment. So definitely it was a good approach for us,” she says. “Obviously, we’ve made great inroads and are actually now building that alignment and building that cohesion with our senior people all the way through into our teams.”

All roads lead to ROI

Downey has used the implementation of GrowthOS to inspire a more sophisticated conversation around data.

“It can make people feel quite uncomfortable,” she admits. “The things that you’ve looked at before and relied on to tell the story aren’t necessarily the sort of the things that you should be doubling down on in the future. And I think this is what this tool has highlighted. But it’s also forcing really good conversations. It’s bringing really good hypotheses to the table for us to test, explore and think about. And I believe that all roads really do need to lead back to ROI. So having that North Star is really key.”

If data readiness is a muscle that businesses can learn to flex, then these two businesses demonstrate that big gains are on the table for those working with the right market mix provider.

But the path to success is not the same for every business. Both Rand and Downey have been savvy about the data readiness of their respective business and – to their credit – tailored their rollout approach to suit different stages of data maturity.