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Media mix modeling (MMM) measures the impact of marketing and advertising campaigns to determine how internal and external elements contribute to a desired business outcome, be it revenue or any other KPI.
Media mix modeling, also known as marketing mix modeling or MMM, is a statistical method used to measure the impact of marketing and advertising campaigns.
MMM reveals how the 4Ps of the marketing mix — product, price, place, and promotion — are contributing to a particular goal.
For example, it can show you how elements like pricing, customer demographics, media spend, and external factors are affecting your sales volume, and predict what would happen if you made certain changes.
In short, MMM helps you identify which elements of your mobile marketing strategy are working and which aren’t, so you can optimize your campaigns.
MMM uses statistical analysis to explore how different marketing efforts affect business outcomes like sales. Using a technique called multi-linear regression analysis, it enables you to link independent variables (such as marketing spend on different channels or user engagement metrics) to a dependent variable (such as app downloads or revenue).
The idea here is to evaluate multiple models to accurately answer the question: “What will happen if we make this change?”
For example, you can use MMM to measure the impact of in-app ads on total revenue. Then you can look at the effect of increased spending on these ads: would it earn you more, or less?
To use MMM effectively, you need aggregated and cleansed data from internal databases and external sources. Ideally, your data will span two to three years to factor in effects like seasonality. Then, you assign a numerical value to every media channel campaign based on the return on investment (ROI), and use this to allocate future spend and create sales forecasts.
A typical MMM process has the following four phases:
A standard effective MMM process has the following four phases:
With the impending demise of third-party cookies, you need to focus on collecting first-party data for a more accurate representation of user reactions and behavior in response to your marketing strategy.
Gather comprehensive historical data on your past marketing activities — things like user engagement metrics, target audience demographics, and ad spend. Look at non-marketing sources and external factors, too — economic conditions, competitor activity, and even the weather all have a role to play.
You also need to ensure data integrity, using methods such as second-party data partnerships (getting information from current or potential business partners for database enrichment) and data clean rooms (using aggregated and anonymized user information to protect user privacy).
To create an MMM model, choose the dependent variable or business outcome you want to explain (for example, revenue or app downloads). Then identify the independent variables that impact it (factors like ad spend and target audience).
Make sure you’re including both controllable variables like price and channel, and uncontrollable variables like competition and inflation.
Finally, assign values to both the dependent and independent variables, and create a mathematical model representing the relationship between them.
In this stage, you’ll use the model from phase 2 to uncover and analyze insights related to your marketing campaigns.
Evaluate the contribution of each channel to the business outcomes and dependent variables you identified before. For instance, you can rank your marketing campaigns based on their impact on revenue or user engagement. From there, you can measure media effectiveness, efficiency, and ROI for each campaign.
Note that you can use your model for forecasting future user engagement and revenue. But models based on historical data assume past patterns will repeat in the future and, therefore, don’t account for landscape changes.
Optimization is the final MMM phase. Using the results from phase 3, this is where you adjust your marketing mix to improve performance in future campaigns.
Consider simulating different marketing scenarios, targeting different audiences, or changing ad spend levels to identify the optimal combination of tactics to achieve your revenue goals faster.
Suppose you spend $2,000 on in-app advertising, and you want to know how effective that was at increasing revenue.
You could simply calculate your return on ad spend (ROAS). If you make $4,000 in revenue that can be directly attributed to those ads, then your ROAS would be 4,000 / 2,000 = 2 (or a ratio of 2:1).
But tying ad exposure to individual user actions like this isn’t always straightforward, particularly in today’s privacy-centric landscape. What’s more, your ads aren’t working in isolation — there’s a range of other factors (both in and out of your control) that can influence a conversion.
Using MMM would give you a much fuller picture and a more strategic overview for campaign optimization. After collecting data on ad spend, target audience demographics, and revenue for the past year, you would create a statistical model representing the relationship between these variables.
Let’s assume the model shows that ad spend and target audience demographics have a significant positive impact on revenue. That means you can optimize your marketing mix for future campaigns by increasing ad spend on the most profitable channels, and targeting a more lucrative audience.
MMM gives you a comprehensive view of marketing impact to help you make informed decisions. It can provide valuable insight into where and when to invest your ad budget, and the optimal media mix to reach your ideal audience.
But it’s not the only way to measure marketing performance, and won’t be right for every business. In many cases, it’s most powerful when used alongside more traditional attribution methods (which we’ll cover shortly), so you can consider performance from every angle.
If you’re thinking about using MMM, here are a few things to consider:
Being a data-driven approach, MMM often involves a significant investment in data collection, modeling, and analysis, making the costs prohibitive for smaller app development businesses. Make sure you can afford to do it properly.
Having access to a large and diverse set of data, including historical marketing data and data related to external factors impacting your app’s success, is crucial for MMM to work. Consider if, and how easily, you can gather and process this.
If your app marketing campaign has a complex marketing mix involving multiple channels and tactics, MMM can enhance your campaign outcomes. On the other hand, if your app marketing campaign is simple and straightforward, MMM may add unnecessary complexity.
MMM is best when you want to understand the impact of different marketing activities on key business outcomes, such as app downloads or purchases. If your objective is to drive short-term results (for example, user-level engagements like clicks or impressions), data-driven attribution would be a better fit.
We’ll explore the similarities and differences between data-driven attribution and MMM in more detail later.
MMM requires expertise in data science, modeling, and marketing analytics. If your team doesn’t have the necessary skills and experience, you may find it difficult to implement, or need to seek external help.
MMM is generally time-consuming, taking several weeks or even months to complete. So, if your app marketing campaign is time-sensitive and you need insights fast, MMM may not be the best choice.
MMM uses an equation to establish the relationship between certain dependent and independent variables.
Here’s a breakdown of some key elements to measure:
When analyzing sales volume in MMM, you need to divide total sales into two components: base sales and incremental sales.
You can further analyze each type of sales volume to understand the specific impact of each marketing activity.
Pricing changes have a direct influence on sales volume, and MMM can help quantify this impact.
When you analyze the relationship between changes in price and changes in sales, you can optimize your app pricing strategies to achieve desired outcomes.
Suppose you increase the price of your app from $3.99 to $4.99. Using the MMM technique, you find that the price increase resulted in a 5% decrease in in-app purchases, but because of the high price point, your revenue increased by 20%.
With this information, you can continue with your app’s new price point, knowing it’ll result in increased revenue with minimal impact on sales volume.
MMM is a valuable tool for analyzing the impact of media and advertising on sales across different channels. Although MMM results may not provide clear-cut answers, they can still give you valuable insights into how changes in advertising strategies influence app sales.
Some examples include:
You can use these insights to optimize your ad spend decisions, making sure you get the most bang for your buck.
An efficient distribution system drives growth more effectively than any other element. You can use MMM to gain a holistic understanding of all your distribution channels and related costs, helping you make informed decisions about the channels to invest in.
Let’s assume you want to expand your distribution efforts for your mobile app.
You can use MMM to analyze the sales data from different distribution channels, such as partnerships, social media platforms, and app stores. If you find that partnering with other popular apps drives app downloads and purchases, you can focus your distribution efforts on building more partnerships, rather than relying on app stores and social media alone.
Data-driven attribution modeling is a way of monitoring user-level engagements throughout the customer journey, so you can see which tactics and touchpoints had the biggest impact. There are various models, including simple first-touch and last-touch attribution as well as more comprehensive multi-touch attribution strategies (more on these below).
Both MMM and data-driven attribution modeling use statistical models to analyze data and show how your marketing tactics affect a specific business objective.
But that’s where the similarities end.
MMM doesn’t factor in user-level engagements — instead, it measures the impact marketing efforts have on meeting predetermined business objectives, without considering the customer journey. On the other hand, data-driven attribution focuses on person-level data, such as the total number of impressions and clicks.
Here’s a table to help you compare MMM with data-driven attribution modeling, and the role each can play in mobile app marketing.
| Features | Media mix modeling (MMM) | Data-driven attribution modeling |
| Purpose | To understand the impact of marketing mix on sales and revenue | To understand the impact of individual marketing touchpoints on conversion |
| Data used | Historical aggregated data from marketing activities and external factors | Detailed individual-level data such as clicks, impressions, and conversions |
| Modeling approach | Multi-linear regression analysis | Machine learning algorithms |
| Key outputs | Marketing mix optimization, media effectiveness and efficiency, ROI, and forecasting | Attribution of conversion to individual touchpoints, allocation of budget and resources |
| Timeframe | Uses historical data for several months to a year | Uses real-time or near real-time data |
| Complexity | High, due to multiple variables and complex regression models | Low to moderate, depending on the complexity of the attribution model |
| Limitations | Assumes past patterns will repeat in the future and doesn’t account for market changes | May not accurately capture the overall impact of the marketing mix and multiple touchpoint interactions |
MTA stands for multi-touch attribution: it’s a method of assigning credit for conversions to various marketing touchpoints along the customer journey. It’s more sophisticated than single-touch attribution, taking into account the many touchpoints a customer can be exposed to before converting.
There are various models for MTA — for example, you might assign equal credit to each touchpoint, give higher weightings to the first and last touchpoints, or add credit as you get nearer the conversion.
MTA is sometimes described as a “bottom-up” approach, focusing on individual user interactions, while MMM is “top-down”, looking holistically at all the factors influencing a business outcome. And while MTA provides quick and granular insights into the full customer journey, enabling agile decision-making, it can be challenging to gather accurate user-level data.
We all know that measuring campaign performance is critical. Without it, how will you know if your budget is working effectively, or what improvements you should make?
If you’re finding it difficult to measure the impact of your marketing spend, adding MMM as part of your marketing strategy might be advantageous. But first, let’s look at its pros and cons:
Implementing MMM starts with identifying the KPIs you want to measure and the questions you want to answer. Gather historical data from a range of sources, cleanse and aggregate it, and then use a statistical model like regression analysis to understand the impact of your variables and potential changes. Keep refining your model and using the insights to optimize campaigns.
All this takes considerable time and expertise, so many businesses prefer to outsource it to an experienced mobile measurement partner. A good one will typically require at least 12-18 months of data. They should be able to clearly explain how their model works and what the results mean — including why they might differ from those you gained via attribution.
Media mix modeling (MMM) is a statistical method used to measure the impact of marketing and advertising campaigns on specific business goals. Using aggregated historical data, it lets you analyze and compare how various factors influence revenue, predict the impact of changes, and make informed campaign decisions.
MMM helps you understand the impact of your marketing efforts on business goals, so you can optimize campaigns. By taking into account a range of factors, it can help you assess things like the ROI of different media channels, the best channels to invest in, and the impact of external factors like competition and seasonality.
To build a media mix model, you first need to identify the variables you want to explore (such as sales volume, pricing, and media spend) and the questions you want to answer. Then, gather and cleanse historical data and use regression analysis to understand the relationship between variables. Because of the statistical expertise required, it’s a good idea to work with a measurement partner.
MMM and MTA (multi-touch attribution) both measure the impact of marketing activities to help you optimize campaigns. However, while MMM gives a big-picture view, MTA provides granular insight into user interactions with specific marketing touchpoints. It relies on user-level data to understand the journey to conversion. MMM, on the other hand, uses aggregated data to avoid privacy issues.
