Marketing mix modelling platform Mutinex has launched Data MAITE, an AI-powered auto-labelling feature for its DataOS product, aiming to reduce the time marketers and its agencies spend preparing data for modelling by up to 95 per cent.
Whilst most data bound for MMM (bar some above the line channels) is collected via API, data collection is only half the job. Once collected, data must still be structured into standardised taxonomies to be parsed by a model. With data collected from dozens of platforms, teams and agencies even the best APIs often deliver fragmented, messy and error riddled data.
Data MAITE solves this problem by setting up taxonomies as standard and deploying machine-based agents to align taxonomies: unlocking clean, standardised data in minutes for enterprises across its markets, brands and products.
The feature, already in use by enterprise customers like Asahi, applies AI agents to align data for critical model inputs to taxonomies — including channels, markets, and campaign names —
accelerating time to insight and improving data consistency. Using Data MAITE, customers can cut labelling time by up to 95 per cent and save weeks of low level work, including massively reducing onboarding times for new brands.
Asahi’s head of marketing effectiveness and analytics, Alex Capper, said the feature has already freed up time for both internal teams and external partners.
“Our models in GrowthOS have hundreds of dimensions that need to be labelled. It is exceptionally important and has been a real time drain until now. The new auto-labelling feature in Mutinex’s DataOS is saving both our internal teams and agency partners significant time and improving our labelling quality. This gives us more time to analyse better models,” he said.
Mutinex says the tool is designed to speed up labelling without removing human oversight.mCustomers retain full control, with the ability to accept, reject, or edit machine-suggested labels.
Mutinex, which has rapidly become APAC’s leading MMM SaaS platform and is now expanding in the U.S., said the new feature reflects its broader strategy: cutting through complexity to help marketers get to answers faster and make decisions that drive growth.
Mutinex senior product manager Neil Shelly said the update reflects growing pressure on marketers to deliver faster answers from increasingly complex data.
“Clean, consistent data is the foundation of effective MMM. Automating clean taxonomies gives customers a way to maintain quality while cutting hours from workflows—especially for enterprise clients managing dozens of brands or markets.”
