Media Mix Modeling (MMM) vs. Multi-Touch Attribution (MTA): Deep dive

Media mix modeling is super-hot thanks to our massive focus as an industry on privacy right now, and it’s something that Singular has a unique and effective solution for. But what does MMM mean for the long-held hope for effective multi-touch attribution in digital marketing?

As a marketer, you are always looking for the best way to measure the impact of your campaigns. You want to know what’s working and what’s not, so that you can make informed decisions about where to allocate your resources.

Two popular methods for measuring marketing performance are Media Mix Modeling (MMM) and Multi-Touch Attribution (MTA). We’re going to take a deep dive into both models, their pros and cons, and how they can impact your marketing strategies, and how they differ.

We’ll start by explaining how MMM works, its key elements, and how to implement it. Then we’ll dive into MTA, define it, and compare it with MMM. Finally, we will examine the suitability of MMM based on budget, campaign objective, and skillset while also exploring the differences between MMM and MTA and where brands should try to use each one.

Understanding Media Mix Modeling (MMM)

Let’s start with MMM.

Marketing mix modeling, also known as media mix modeling (MMM), is a powerful but complicated technique used to optimize marketing activities on a broad scale.

By analyzing historical data, MMM helps determine the impact of various marketing tactics on business outcomes. This statistical analysis provides valuable insights into return on investment (ROI) and the effectiveness of different marketing channels. With MMM, marketers can identify the contribution of major marketing channels and drivers, such as Facebook ads or IP partnerships, and make data-driven decisions for their marketing strategy.

MMM is historically useful for consumer goods companies and digital marketing teams looking to enhance brand awareness and base sales, but it is also now available for mobile marketing, app marketing, and user acquisition campaigns.

The Concept of MMM

Media mix modeling is a powerful analytical technique that explores the relationship between marketing inputs and sales. Marketing inputs, of course, include ad campaigns and marketing tactics, but go far beyond them.

By considering both internal and external factors that influence sales, MMM helps marketers understand the impact of various marketing channels on their brand’s performance. It measures the effectiveness of marketing efforts on overall installs and revenue, and enables marketers to make data-driven decisions for their marketing strategy.

MMM utilizes regression analysis, a statistical method, to uncover the causal relationship between marketing activities and sales. This high-level approach allows marketing teams to assess theROI of their campaigns and optimize their media mix for maximum brand awareness and base sales.

How MMM works: a deep dive

Media mix modeling leverages statistical techniques like linear regression to analyze historical data and identify seasonality or long-term trends. One thing you need for MMM to work well is variation. Steady-state marketing that always applies the same budgets to each channel will make it harder for MMM to assign causal relationships to conversions and sales.

MMM goes beyond just analyzing data – it can also incorporate customer journey data to attribute sales to specific marketing touchpoints.

This allows marketers to forecast the impact of future marketing campaigns and gain insights into the optimal allocation of marketing budgets across different channels. With MMM, marketing teams can make data-driven decisions to maximize ROI and enhance brand awareness.

Implementing Media Mix Modeling: analytics for ROI

Implementing Media Mix Modeling requires careful consideration and analysis of relevant data.

Collecting accurate and comprehensive data is crucial for the success of media mix modeling, and it’s also one of the hardest part. (The good news here is that if you use Singular MMM, that step is already done. It’s totally completed for you, and you can move on to the next steps.)

Typically, once the data is gathered, it needs to be cleaned, transformed, and prepared for modeling: another step that is completely done for you with Singular MMM.

MMM involves selecting a dependent variable, such as total app installs or subscriptions, and identifying independent variables like pricing, channels, and marketing campaigns. Statistical techniques like multi-linear regression are then used to build MMM models, which provide insights into the effectiveness of different marketing channels and help optimize the allocation of marketing budgets.

Collecting relevant data

No media mix modeling happens without relevant data from all your marketing sources. This includes gathering data from sales records and marketing activities, such as pricing and media spend. Accuracy and completeness of the data are crucial to ensure reliable MMM results, and Singular uses high-level data science to process the data.

Again, Singular makes this step easy.

Analyzing data for media mix modeling

Analyzing data for media mix modeling involves conducting statistical analysis to identify patterns and relationships in the data.

Singular applies regression analysis to determine the impact of marketing activities on sales, and advanced analytics techniques to uncover insights from the data. By analyzing data using these methods, marketing teams can gain valuable insights into the effectiveness of their media mix and make informed decisions to optimize their strategies.

Key Elements Measured by MMM

Sales and pricing play a crucial role in traditional media mix modeling, and are still relevant for mobile user acquisition focused marketers.

MMM analyzes the impact of media marketing and advertising on installs, sales, registrations, or whatever metrics matter for you, taking into consideration distribution systems and channels. It measures the effectiveness of different marketing channels and helps understand the role of channel and partner changes in driving results. By evaluating these key elements, MMM provides insights into how different factors contribute to overall sales performance. This information is vital for marketing teams to make data-driven decisions and optimize their media mix strategy for maximum ROI.

Sales revenue and marketing partners

Analyzing the relationship between revenue and marketing partners is a key aspect of MMM for mobile apps.

MMM helps publishers identify the optimal channels and partners that maximize sales by analyzing adjustments and understanding their impact on advertising outcomes. MMM can also provide valuable insights into the relationship between price and demand, particularly for retail or subscription apps, allowing marketing teams to make informed decisions about pricing.

This high-level analysis of revenue and partners within the market mix model can guide marketing decisions and ultimately improve return on investment. Brands can leverage MMM to enhance brand awareness, optimize pricing, and drive sales.

Impact of media marketing and advertising

MMM plays a crucial role in evaluating the effectiveness of media marketing and advertising campaigns.

By measuring the contribution of different media channels such as in-app, CTV, traditional TV, social media, and influencer ads, MMM helps marketing teams allocate budgets to the most impactful tactics. It also analyzes the reach and frequency of marketing messages, providing valuable insights into optimizing brand awareness and customer engagement.

Additionally, MMM uncovers the positive impact of media marketing on overall sales, helping brands achieve a higher return on investment. With its ability to assess the performance of various marketing channels, MMM proves to be essential for high-level decision-making in consumer goods industries.

Distribution systems

Traditionally, distribution systems play a crucial role in driving sales.

While MMM for mobile app user acquisition campaigns is different than CPG, MMM can still help examine their impact and factor in events such as being featured by Google or Apple, or ranking high in an App Store.

By analyzing distribution systems, MMM can identify the most effective distribution channels and guide marketing decision-making. This helps marketing teams allocate budgets to the most impactful tactics. Incorporating distribution data into MMM allows brands to understand how changes in distribution can affect overall sales and return on investment.

Is Media Mix Modeling the right choice for your brand?

Analyzing the effectiveness of your marketing efforts through media mix modeling can help determine if it aligns with your budget, campaign objectives, and skill sets. By making an informed decision, you can drive data-driven decision-making and find out if media mix modeling is the right choice for your brand.

Often, app publishers and marketers find the juxtaposition of MMM insights and more tactical and traditional MMP insights extremely insightful, with the combination offering more than each alone.

Evaluating the suitability of MMM based on budget, campaign objective, and skillsets

Assessing the suitability of media mix modeling involves considering various factors.

First, it’s crucial to evaluate whether your budget allows for the costs associated with MMM implementation. Spending under $10,000/month on your app growth campaigns might not provide enough data. Alternatively, if you’re just using 1 or 2 partners, MMM might not be your best option.

Additionally, aligning your campaign objectives with MMM can help determine if it’s a suitable option. Typically, evaluating your team’s skillsets is also essential as MMM requires expertise in data analysis and statistical modeling, though Singular’s solution vastly reduced the complexity for marketing teams.

By assessing these aspects, you can make an informed decision about whether MMM is the right fit for your brand’s needs. It’s also important to explore the potential benefits, such as enhanced return on investment and improved brand awareness, that implementing MMM can bring.

OK: let’s turn to MTA, or multi-touch attribution.

An overview of Multi-Touch Attribution (MTA)

Multi-Touch Attribution (MTA) allows marketers to understand the customer journey by tracking and attributing credit to different marketing channels and touchpoints along the customer journey.

By implementing MTA, brands can gain a comprehensive view of their marketing efforts and measure the effectiveness of each touchpoint. This data-driven approach can help optimize marketing budgets and improve return on investment. MTA complements other models like media mix modeling, offering a high-level understanding of the various touchpoints across different channels, including digital marketing platforms like Facebook.

Implementing MTA in marketing teams in some verticals can enhance brand awareness and drive data-driven decision-making for better campaign performance and results.

The challenge for MTA in today’s mobile app marketing ecosystem: privacy. Thanks to privacy regulations and technologies, it’s generally no longer possible to track each individual touchpoint, ad view, or conversion. That’s especially true on iOS right now, and increasingly will be true on Android from 2024 and on to 2025, as Google deprecates the Google Ad ID, or GAID.

Defining MTA

Multi-touch attribution is a crucial concept in marketing that assigns credit to different touchpoints in the customer journey. It plays a significant role in measuring marketing effectiveness by analyzing the impact of each touchpoint on consumer behavior and conversion rates.

Various attribution models are used in MTA, such as linear attribution, time decay, and position-based attribution. By understanding these models, marketers can gain insights into the effectiveness of their marketing efforts and make data-driven decisions. Implementing MTA can have a profound impact on decision-making, allowing marketing teams to optimize their strategies and allocate resources more effectively.

The core concept, however, is simple: capture each touchpoint, track them, and

How does MTA for apps work?

Diving into the mechanics of multi-touch attribution, we can gain a deeper understanding of how it analyzes and attributes marketing efforts.

Statistical analysis plays a crucial role in MTA, allowing it to track and credit various touchpoints throughout the customer journey, such as clicks, ad views, installs, and so on. However, implementing MTA does come with its challenges and limitations, with the key one being that each touch from an individual person being connected to additional touchpoints: something that’s harder if not impossible on iOS, at least, today, and potentially on Android starting next year.

Despite this, the future of multi-touch attribution can be promising in some areas and provide valuable insights for marketing teams. By exploring the workings of MTA, brands can make informed decisions about their marketing strategies and optimize their return on investment.

Comparing Media Mix Modeling and Multi-Touch Attribution

To effectively analyze the impact of marketing efforts, it is essential to understand the connection between media mix modeling and multi-touch attribution.

While both models aim to measure marketing effectiveness, they differ in their methodologies and approaches. Media mix modeling takes a high-level approach, using statistical techniques to determine the optimal allocation of marketing resources based on historical data. On the other hand, multi-touch attribution focuses on assigning credit to different touchpoints in the customer journey, considering factors such as brand awareness and consumer behavior.

Evaluating when to use which model — or whether to use them at all — is a decision your marketing team needs to make. Factors to consider are which models align with your brand’s needs. And you’ll need to consider factors such as budget, campaign objectives, and the skillset of your marketing teams.

Both models have their pros and cons and use cases for different scenarios.-

Media mix modeling provides an overview of the market mix and its impact on sales, while multi-touch attribution offers insights into individual touchpoints’ effectiveness. Ultimately, finding the right balance between media mix modeling and multi-touch attribution is crucial for maximizing return on investment and shaping effective marketing strategies.

In addition, you may find that while MTA is challenging or simple too hard in a mobile app growth scenario, selecting key touches to track is indeed useful, even if you can only connect them to preferred outputs like installs and revenue in aggregated, privacy-safe ways.

The connection between MMM and MTA

While MMM and MTA are different methods of marketing measurement that use different technologies to optimize results, the data that drives MMM can provide valuable inputs for MTA analysis.

By contrasting the top-down statistical analysis of MMM with the granular bottom-up attribution analysis of MTA, marketers can gain comprehensive insights into the effectiveness of their marketing efforts. This relationship between MMM and MTA allows marketing teams to understand the impact of various channels on consumer behavior, optimize their media mix, and make data-driven decisions to maximize return on investment.

Leveraging both models can lead to a more holistic understanding of marketing performance.

Key differences between MMM and MTA

Media mix modeling and multi-touch attribution are two distinct approaches to measuring marketing effectiveness.

MMM focuses on understanding the impact of different marketing channels on overall sales and return on investment. It analyzes historical data and calculates the contribution of each channel, such as television, print, digital marketing, and social media, in driving sales and brand awareness. MTA, on the other hand, tracks individual customer touchpoints throughout their journey, allowing marketers to attribute credit to specific interactions and optimize future advertising efforts.

Both models have their strengths and limitations.

MMM provides a holistic view of marketing performance and is useful for long-term planning and budget allocation. However, it isn’t great at providing insight into tactical changes to digital marketing efforts that can drive performance improvements in the short term. MTA, on the other hand, offers detailed insights into the effectiveness of individual touchpoints and can help optimize campaigns in real-time, while it struggles to account for external factors that are not captured within the digital realm.

When evaluating MMM and MTA, it’s crucial to consider the unique needs and objectives of your brand. MMM is beneficial when you want to understand the overall impact of your marketing mix and assess the ROI of different channels. MTA, on the other hand, is ideal for digital-first brands looking to optimize their digital advertising efforts.

The benefits of implementing MMM

Implementing media mix modeling offers a multitude of benefits for mobile publishers seeking to maximize their ROI. By using data-driven insights from MMM analysis, marketing teams can optimize channel mix and marketing spend and gain a deeper understanding of the impact of various marketing tactics on ROI.

MMM also allows marketers to align their efforts with the preferences of their target audience, enhancing brand awareness and driving consumer engagement. Plus, MMM enables businesses to understand and leverage seasonality patterns, allowing them to make informed decisions regarding pricing changes and the introduction of new products.

Limitations of media mix modeling

While media mix modeling is a valuable tool for optimizing marketing strategies, it does have its limitations.

One of the main drawbacks of MMM is its reliance on historical data, which may not accurately reflect current market conditions or changes in consumer behavior. (Think pre-Covid data being used to drive marketing decisions during Covid … especially in the gaming vertical.)

Additionally, MMM does not account for individual user behavior or the impact of specific marketing channels on individual conversions. Traditional MMM is a relatively static model and may not be able to adapt quickly to changes in the market or marketing strategies.

The good news for Singular customers is that one of the main objections to MMM — the fact that implementing and maintaining MMM requires a significant amount of data and resources — is no longer the case. That makes MMM much more accessible to smaller businesses and app publishers.

Despite these limitations, MMM still provides valuable insights into the effectiveness of marketing efforts.

How can brands find a balance between MMM and MTA?

Brands can strike a balance between MMM and MTA by leveraging person-level data and utilizing forecasting techniques.

This combination allows for data-driven decision-making, taking into account both long-term trends and future campaigns. MTA provides insights on the customer journey, while MMM offers a holistic view of marketing efforts. Integrating these approaches helps brands evaluate digital channels effectively.

In conclusion, both Media Mix Modeling and Multi-Touch Attribution offer valuable insights into the effectiveness of marketing strategies. MMM provides a holistic view of the impact of various media channels on sales volume, pricing, and distribution systems. On the other hand, MTA focuses on analyzing individual touchpoints along the customer journey to understand their contribution to conversions.

When considering which approach to use, it is important to evaluate factors such as budget, campaign objectives, and available skillsets. Both MMM and MTA have their strengths and limitations, and finding a balance between the two can provide a more comprehensive understanding of marketing effectiveness.

Ultimately, the choice between MMM and MTA depends on the specific needs and goals of your brand. By leveraging the insights provided by these methodologies, you can optimize your marketing strategies and drive better business outcomes.