Looking for a better way to prove out your marketing strategy and advertising spend? Media Mix Modeling (MMM) is an analytical method used to measure the performance of advertising across media channels. Through statistical analysis, MMM helps marketers optimize media budgets, forecast future outcomes, and evaluate channel success.
A form of MMM emerged in the 1950s, when the retail and CPG industries sought a way to enhance their budget allocation, sales, and forecasting. It gained widespread adoption in the late 1960s and early 1970s.
Today, MMM is important to ad campaign budget management and having access to a broader view of your marketing program achievements, sales forecasting, attribution modeling, understanding performance metrics, and increasing ROI.
Understanding Media Mix Modeling
As marketing analytics and media planning have evolved, MMM has evolved with them. While its frequency of use diminished in the last few decades, it has now matched the complexity of marketing with big data and machine learning, bringing it back into practice.
MMM works best with a large amount of data collected over time and requires extensive historical data (ideally 12-36 months) to accurately analyze seasonality, channel impacts, and external market factors.
That amount of time, data, seasonality, and channels provides a more accurate view to model and forecast how future spending should be allocated. Unlike attribution methods relying on individual user journeys or click/impression-level data, MMM assesses base and incremental sales impacts, offering a macro view of advertising effectiveness.
Frequently confused with Marketing Mix Modeling, Media Mix Modeling specifically focuses on analyzing the effectiveness of advertising channels, whereas Marketing Mix Modeling encompasses broader marketing elements, including product, price, place (distribution), and promotion.
Prerequisites for Marketers
To get started, ideally, you’ll have two to three years of clean, segmented media and sales data by channel; this may also include social media and email sales data, along with revenue and conversions. Be clear on what external factors may skew outcomes, such as economic influences.
The process of MMM also requires statistical software like R or a language like Python, modeling platforms, data storage, and a validation model.
The technical expertise of a data scientist and/or marketing analyst is needed to leverage the data, analysis, and reporting.
Optimal Utilization of MMM
At a high level, the process of using MMM entails gathering all organizational data, defining goals, and outlining what success means to you. Pick your model — linear regression, machine learning, Bayesian, and your programming language and statistical software. Know what variables are at play and any external factors affecting input and outcomes. Clean, prep, and segment your data.
More specifically, there are six important steps to execute media mix modeling successfully.
- Define Your Goals: Know what your end goal is and what success looks like.
- Collect All Data: Be prepared to have all historical media spend, data from sales, and data related to any external influences.
- Pick your model: Linear regression, machine learning, Bayesian, and your programming language and statistical software.
- Data Management: All data should be reviewed, cleaned, and segmented.
- Test — Test — Test
- Optimize: Implement marketing data analysis, make recommendations and changes based on learnings, rinse, repeat.
MMM Implementation Challenges
There are some challenges to Media Mix Modeling strategies. Organizations need the right resources, expertise, and enough data to be statistically significant. The luxury of time and budget is also a factor in success. The process takes a lot of resources, is costly, and may not provide the useful results you are looking for if the input is limited or of poor quality.
The ability to implement accurate data analysis, employing statistical modeling skills and tools, and the talent of a data scientist and or marketing analyst are key. The modeling process requires a wealth of historical data and a commitment to long-term implementation.
Many businesses have the challenge of data silos throughout their organization which have to be overcome. In the end, directly attributing to specific channels may prove to be too difficult if all the right resources are not available.MMM vs. Multi-Touch Attribution (MTA)
MMM vs. Multi-Touch Attribution (MTA)
Media Mix Modeling is not the only way to go about analyzing the outcomes of your advertising and media plans. Multi-Touch Attribution (MTA) is a very different way to identify the success or failure of your chosen media strategy.
When comparing the two processes, MMM equates to a high-level view of marketing, strategic planning, and budgeting for traditional media channels. Multi-Touch Attribution optimizes digital channels by evaluating tactical and campaign performance.
MTA focuses on unique elements and conversions, while MMM focuses on statistical analysis of the overall impact of marketing on business outcomes, which is broader and higher-level in strategy. MTA requires understanding the customer journey and user-level data, optimizing digital campaigns, and allocating budget.
While both MMM and MTA aim to measure marketing effectiveness, their approaches and applications differ significantly:
- Media Mix Modeling (MMM):
- Macro-level analysis
- Evaluates channel-level impact
- Ideal for strategic budgeting and forecasting
- Broad, aggregated data
- Multi-Touch Attribution (MTA):
- Micro-level, tactical analysis
- Tracks individual user touchpoints
- Focuses on digital campaign optimization
- Requires granular, user-level data
Both methodologies can complement each other for a comprehensive view of marketing effectiveness.
MMM vs. Reach and Frequency Tracking
Another performance management approach is reach and frequency tracking. Reach is the total unique individuals, or in some cases households, who view an ad or campaign more than once during a specific period. While frequency is focused on the number of times that the audience is exposed to the ad or campaign.
Together, reach and frequency (R&F) measures how many unique individuals see an ad (reach) and how often they are exposed to it (frequency). It assesses message exposure and effectiveness at delivering messages.
In contrast, MMM uses broader statistical analysis to correlate media spend with sales outcomes rather than direct audience measurement. While MMM can integrate R&F data as inputs, it predominantly evaluates overall performance and budget optimization.
MMM impacts media strategies using a data-driven approach to evaluating channel performance and improving media spend. Reach and frequency measures the impact of message delivery, campaign effectiveness, and budget allocation.
Ideal Clients and Agencies for MMM
Media Mix Modeling is not for everyone. It’s a heavy lift for SMBs and other organizations that lack the necessary resources or the product or service that would benefit the most from MMM.
Brand marketing benefits from MMM with optimized budget allocation and clarity of effective channels, but requires a level of sophistication around data management and resources.
Media Mix Modeling is particularly beneficial for:
- Legacy brands with extensive historical marketing and sales data
- Large consumer-facing companies in retail, CPG, telecom, and similar sectors
- Organizations requiring precise media budget allocation and long-term strategic planning
Examples include brands like Kraft, Coca-Cola, Verizon, and Procter & Gamble. MMM may be too resource-heavy for smaller businesses, which may need to lean on simpler attribution methods or alternative analytics.
Example: Coca-Cola's Successful MMM Implementation
In the early 2020s, during the pandemic, Coca-Cola evaluated a multitude of budget scenarios using a MMM simulator. This allowed the organization to develop a pricing strategy to increase or maintain ad spend while competitors were cutting back. This strategy enabled them to increase their market share.
Coca-Cola also employed MMM to optimize ad spend globally across various markets. It analyzed historical ad and sales data and evaluated the economic environment to understand which channels were most successful. With MMM Coca-Cola fine-tuned budget spend which positively impacted its profitability.
In an ideal world, marketers have a clear understanding of what marketing tactics are achieving their goals. Attribution, modeling, and other marketing measurement techniques are complex. Media mix modeling is one of the tools that help with effective media strategies and budgeting.
While the resource requirements and complexity for MMM are high, for the right brands, with access to their historical data, adopting MMM can be an effective way to enhance marketing strategies, optimize budgets, and provide long-term ROI.
