Where should you spend your next advertising pound?
It is the question advertisers have asked for decades. And for years, the answers came from media plans built on intuition, historical reports, or complex data modelling. Today, with increased pressure to maximise the effectiveness of every advertising pound & dollar, that just isn’t good enough.
Performance shifts daily, algorithms update, and entire budgets can be wasted in hours. Which means that relying on the old way of planning is not just inefficient, but it is risky. That is where AI can augment Marketing Mix Modelling.
What is Marketing Mix Modelling (MMM)?
Marketing Mix Modelling is a statistical approach that helps advertisers understand how different channels, campaigns, and even external factors like seasonality contribute to business outcomes. Traditionally, MMM has looked backwards. Data is collected over months or even years, then analysed to determine the historical impact each channel had on results. This has long been a valuable method for high-level budget allocation, especially for larger brands investing across multiple channels.
The process of Marketing Mix Modelling typically involves:
- Problem definition – teams usually start with defining questions they need answered
- Data collection – based on the question, appropriate data sets must be identified
- Statistical analysis – initial assessment of the data reveals performance insights
- Model building – statistical techniques are used to create a mathematical relationship between marketing inputs and business outcomes
- Simulation – teams can change different input parameters and values to determine how they impact performance
- Optimisation action & iteration – based on the results of the simulation, marketing teams can take action.
Challenges with current MMM
Despite its value, traditional MMM comes with significant challenges. The models are based on historical data, meaning reports are retrospective, and you often only learn what worked long after the fact. They are also limited; the complexity of today’s fragmented media environment where customers move fluidly between platforms like TikTok, Meta, and Google aren’t always fully accounted for.
MMM is a resource-intensive process; building and maintaining accurate MMM requires specialist skills, robust data, and a significant investment, leaving businesses without resources or skilled data teams unable to use this.
The biggest issue with traditional MMM in an enterprise is the significant gap between insight and execution. Even when a model points to where budgets should shift, it can’t adapt in real time. By the time recommendations are implemented, the market has often moved on, making the insights obsolete – a lack of real-time and agile capabilities which that leaves advertisers behind.
Beyond that, traditional models are plagued by a few other key challenges. Walled gardens like Meta and Google create data gaps, making it nearly impossible to capture the full customer journey. At the same time, the inflexibility of these models means they can’t reflect sudden shifts in consumer behaviour, entry or exit of competitors in the ad auction, or platform changes.
When it comes to scale, the cost and complexity can outweigh the benefits for small businesses, while larger enterprises face the major hurdle of integrating siloed data across multiple teams. This is precisely where AI and automation begin to play a critical role.
The role of AI and automation
AI doesn’t replace MMM; it enhances it by providing the tools to move from a static, retrospective plan to continuous, real-time optimisation. Instead of waiting weeks for reports, AI-powered systems can analyse live performance data across all platforms, automatically shifting ad spend to where it’s most effective. This bridges the critical gap between strategy and execution, ensuring your long-term plan is always aligned with a real-time market.
Think of it like this: traditional MMM is the map. It gives you a comprehensive, long-term view of the terrain. But AI is the GPS, which takes that map and provides real-time directions, ensuring you don’t take a wrong turn or miss a key opportunity in the moment.
How should you be using it?
The way AI supports MMM looks different depending on your business size and resources, but the benefits are universal.
For SMEs: AI tools level the playing field. Instead of managing multiple dashboards and trying to piece together a strategy, smaller teams can use automated solutions to unify performance data, cut wasted spend, and keep budgets working harder. This gives SMEs access to optimisation power that was once only available to large enterprises.
For Enterprises: AI scales what MMM already does. It can be seamlessly integrated into existing models to manage complexity across markets, brands, and product lines. Automation ensures that insights flow directly into execution ; reallocating millions in spend quickly, accurately, and without manual bottlenecks.
Advertisers already embracing AI-based campaign automation gain a significant competitive advantage. Those waiting risk falling behind as platforms become more automated and less transparent.
The bottom line
Most ad platforms have built-in tools designed to optimise for your objectives, but remember that these are fundamentally built to serve the platform’s own interests, which are often at odds with your marketing team’s desire to get more from your ad spend. To truly maximise your ad campaign’s impact, you need an unbiased view that cuts across all channels and campaigns.
This is where a solution like Unyte comes in. By connecting all your platforms and making them work in sync with your customer journey, your marketing activities can deliver a far greater impact. Unyte analyses millions of data points in real time within a single, unbiased system, optimising spend wherever it drives the strongest results. This means you’re no longer locked into underperforming campaigns; instead, you get lower CPAs, stronger ROI, and more time back for strategy and creativity.