How Google Meridian Enhances Marketing Mix Analysis & ROI

Google recently released Meridian (https://developers.google.com/meridian), an open-source marketing mix model (MMM) designed to solve key challenges in marketing measurement. If you work in data analysis or marketing, you might be familiar with MMMs, they help with evaluating return on investment (ROI) across multiple advertising channels. 



Take a company like Samsung or Apple. These businesses invest millions in ads across platforms such as TikTok, Instagram, YouTube, and even TV. With ads running on multiple channels at once, a potential customer might see a TV commercial, watch a related ad on YouTube, and then come across another ad while searching for phones on Google. It can be difficult for businesses to pinpoint which ads actually drive sales, especially with so many touchpoints involved.

Lots of different ads, but which ones matter?

This is where Meridian comes in. Meridian helps businesses optimise their ad spend by analysing the impact of each channel. It collects data on sales, advertising spend, and external factors like seasonality or market conditions, then uses statistical analysis to assess how much each channel contributes to overall sales. This allows marketers to identify which channels are performing best and allocate budgets more effectively.



However, MMMs face a few challenges. One issue is endogeneity, where cause and effect become unclear because multiple variables influence each other. For example, marketers may spend more in areas where they expect high demand, making it hard to tell if sales are driven by advertising or simply market trends. Another challenge is multicollinearity (when variables are correlated, making it hard to separate their effects), where spending rises across all channels during peak seasons, like the holidays, making it tough to isolate the impact of individual channels.

Example Meridian response curve graph for different marketing channels.

Meridian addresses these issues by using advanced statistical techniques to better isolate the effects of each channel (these techniques are really interesting, and worth a read up on). It reduces the impact of endogeneity by factoring in external influences, and its open-source nature allows analysts to tweak models, enhancing transparency and precision.

What are the outputs?

Some of Meridian's key outputs include:

• Channel Impact: Identifies how much each channel (e.g., TV, digital ads, social media) contributes to sales.
• Return on Investment (ROI): Calculates the ROI for each channel, helping businesses determine where to focus their budgets.
• Attribution Insights: Provides clearer insights into which touchpoints in the customer journey are most effective at driving conversions.
• Optimised Budget Recommendations: Suggests how to reallocate budgets for maximum marketing effectiveness.

Our thoughts?

For those of us in web analytics, Meridian represents a great leap forward. Unlike traditional "black-box" solutions, it being open-source gives us the flexibility to customise models, test assumptions, and gain deeper insights into how channels interact. This means better tracking of marketing effectiveness, smarter budget allocation, and more informed decision-making.

If you're also involved in web analytics or in marketing, Meridian is definitely worth exploring. Its flexibility and transparency make it a powerful tool for refining marketing strategies and enhancing data-driven decision-making.