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.

Three main challenges companies face when using MMM:

• Data availability and quality
  MMM requires sales figures and a lot of historical data, which can be as much as two years of data across different channels. For the most holistic analysis, it also requires data on external factors (such as seasonality or economic conditions) and sometimes even competitive activity. If the data is incomplete, inconsistent or inaccurate, it can undermine the model’s ability to provide reliable insights. Data is often siloed across departments or systems, which makes it difficult to aggregate and analyze.
• Attribution complexity
  Overcoming the complexity of multi-channel interactions is a key shortcoming of MMM. Accurately attributing sales or other outcomes to specific marketing activities can be very challenging — especially when the effects are indirect or delayed. For example, TV campaigns may have a long-term impact, while digital ads might result in immediate conversions. Untangling these effects, particularly for an omnichannel strategy, often requires more sophisticated techniques.
• Model calibration and overfitting
  Building an effective Media Mix Model requires a balance of model complexity and generalizability. Overfitting occurs when the model is too tailored to the training data, capturing noise or irrelevant patterns that don’t apply to future periods. This can lead to inaccurate predictions. On the other hand, an overly simple model might miss critical interactions between channels or external factors. Achieving the right level of sophistication is a major challenge in the modeling process.

READ MORE: MMM & MTA: Build in-house or buy?

Using the Alembic Marketing Intelligence Platform to build upon MMM insights

Traditional Media Mix Modeling requires careful attention to data management and a huge amount of historical data to ensure the insights generated from MMM are actionable and reliable. Alembic was designed to overcome these challenges and provide a holistic approach to multi-touch attribution: empowering CMOs through predictive intelligence.

Alembic improves on the biggest pain points of MMM by:

• Accelerating time to market. Once a brand’s digital, social media and analytics accounts are linked to Alembic, the platform ingests data from across the funnel and provides meaningful insights quickly — reducing the up-front costs of building and maintaining a custom MMM solution.
• Taking a holistic approach to attribution. Alembic uses proprietary algorithms to observe data, harnesses AI to link causal chains together and predicts insights from marketing data. In the final stage, these insights are converted into action plans that provide an understanding of a touchpoint, event attribution and impact on a brand’s marketing strategy.
• Monitoring and predicting marketing in real-time. Static models and models that rely on calibration simply aren’t built for today’s market. Alembic’s sole focus is to become the leading marketing intelligence platform. To that end, the Alembic platform leverages one of the fastest computers in the world to ingest and analyze data, while adjusting for outliers and anomalies.

MMM is an approach developed before the advent of digital. The tools (and the stakes) in marketing have evolved, and the Alembic platform is suited to provide actionable insights. While MMM focuses on building and analyzing models to optimize marketing strategies, Alembic uses predictive intelligence to help stakeholders and executives find the insights that get lost in the data — and then move faster.

Looking for a better way to measure marketing impact?

Alembic is an AI-powered marketing intelligence platform that empowers CMOs to make better strategic decisions. Used by industry titans like NVIDIA, backed by executives from DreamWorks, and trusted by the top Fortune 500 companies, Alembic was built to raise the bar for multi-touch attribution models. Interested in a live walkthrough of our platform? Book a demo with Alembic to experience the ultimate in MMM data science.