How to Overcome the Common Pitfalls of Media Mix Modeling
Published: 12/9/2024
Media Mix Modeling (MMM) emerged in the 1950s because marketers needed a better way to understand how advertising techniques impacted sales. With roots in economic theory and advertising research, MMM was developed to help bridge the gap between marketing decisions and measuring the impact of brand strategies. The technique provides insights that help businesses ensure the success of their marketing investments. Understandably, MMM became widely adopted as technology improved through the late ‘90s and is still used by many large advertisers today.
Today’s data-driven marketing has embraced several new approaches to analyze the impact of marketing activities: including attribution models, A/B testing, path-to-purchase modeling, digital analytics and others. While MMM is still seen as highly relevant, the evolution of digital marketing and the use of sophisticated omnichannel tactics have uncovered some very real challenges of using MMM for marketing insights. Let’s discuss.
What is MMM?
Media mix modeling (MMM) is a statistical technique used to analyze the effectiveness of marketing channels in driving key business outcomes. Marketing channels refer to TV, radio, social media and digital platforms, while business outcomes could refer to sales, customer acquisition and general brand awareness. MMM uses historical data — some require two years or more — to quantify the effectiveness of each marketing channel, which helps marketers optimize how their spend is allocated.
A custom, in-house MMM platform requires several tools and applications, from statistical software to platforms that integrate data and analytics. Obviously, a company’s decision to invest in a highly customized MMM solution can be significant. MMM in-housing also hinges on retaining employees with a specialized skill set and knowledge base. Turnover, training and onboarding make it particularly costly.
There are less cost-prohibitive MMM tools on the market that are easier to adopt, such as Nielsen Marketing Mix Modeling.
Whether out-of-the-box or fully custom, there is no denying the significance of MMM data science. MMM continues to provide valuable insights for businesses looking to maximize their marketing spend and understand the broader effects of their investments. While it is a powerful tool, there are several challenges that companies often face when using traditional marketing mix models.