MTA and MMM compared: which one is better for you?

Today, marketers use multi-channel marketing strategies to reach wider audiences with their campaigns and to maximize the effect of the campaign. This applies to both online and offline marketing channels which makes the measurement of marketing performance particularly difficult across the different campaigns and media mixes. Moreover, being a marketer in 2023 puts you face-to-face with multiple new challenges brought by constant regulatory changes regarding:

• Data privacy and data loss
• Rapid digital expansion caused by the COVID-19 pandemic
• Staying ahead of the competition in terms of marketing effectiveness

The last point is rather tricky because it is heavily influenced by the first two; however, it is of crucial importance since it will allow you to track your investments and their returns, invest your marketing budget in the channels that bring you the best returns, and ultimately, also justify your marketing budget and potentially increase it.

Marketing measurement tools help you prove that marketing spending is not a cost, but rather a valid investment that increases incremental margins and brings quantifiable results to your business. Now, the biggest problem is, which marketing measurement tool to pick? There are numerous options on the market, but Marketing Attribution Models (MTA) and Marketing Mix Modeling (MMM) stand out the most.

This article will dive into topics like:

• What are MTA and MMM?
• How do MTA and MMM work?
• What are the differences between MMM and MTA?
• Shortcomings of both methods
• Which one is better for your business?

What are MTA and MMM?

Multi-touch Marketing Attribution (MTA) is a method of assigning credit for a sale or conversion to multiple marketing touchpoints that a customer interacts with before making a purchase. This approach recognizes that a customer's buying journey often involves multiple marketing interactions, and seeks to understand the contribution of each touchpoint to the final outcome or conversion.

In contrast to single-touch attribution, which only credits the last touchpoint before a conversion, multi-touch attribution uses statistical models to distribute credit across multiple touchpoints based on their relative impact. We have explained the different types below.

Marketing mix modeling (MMM) is a statistical approach used to analyze the effectiveness of different marketing tactics, such as advertising, promotions, pricing, etc. The goal of MMM is to quantify the impact of marketing activities on sales and other business metrics and to optimize the marketing mix by allocating resources to the most effective tactics, channels, campaigns etc. The modeling process involves collecting data on marketing inputs, such as budget, reach, frequency, etc., and linking them to outcomes, such as sales, market share, brand equity, etc. The model then uses a Bayesian modeling approach to determine the relationship between marketing inputs and outputs. You can read more about it here.

How does MTA work?

The basic idea behind MTA is that a sale or conversion is the result of a series of interactions with a brand, and each touchpoint along the way contributes to the final outcome. Examples of touchpoints can be: like, click, open email etc. The goal of MTA is to determine the value of each touchpoint in the customer journey and allocate the appropriate level of credit to each touchpoint.

There are several different MTA models, each with its own approach to assigning credit. Some of the most common models include:

1. First Touch Attribution: All credit for the sale or conversion is assigned to the first touchpoint in the customer journey.
2. Last Touch Attribution: All credit for the sale or conversion is assigned to the last touchpoint in the customer journey.
3. Linear Attribution: Credit is evenly distributed across all touchpoints in the customer journey.
4. Time Decay Attribution: Credit is assigned based on the proximity of the touchpoint to the sale or conversion, with more credit being given to touchpoints that occur closer in time to the sale.
5. Position Based Attribution: A combination of first and last touch attribution, with a portion of credit being assigned to the first and last touchpoints, and the rest of the credit being divided equally among the touchpoints in between.
6. U-shaped Attribution Models tracks all touchpoints from First-Click to Last-Click. However, it evenly distributes the revenue credit between the first and last touchpoints, with the remaining touchpoints receiving a smaller portion.
7. W-shaped Attribution Models, similarity to U-shaped, W-shaped tracked the first and last click and one more touchpoint which the model defines as “Opportunity”.

Which attribution model your company chooses, or if you are even considering an attribution model depends highly on your available data as well as your company’s marketing goals.

However, looking into the future, one must consider the uncertain future of the third-party cookies as well as the constant privacy regulation changes. Third-party cookies have been commonly used in marketing attribution models for the past decades. Cookies are small files stored on a user's device that allow websites to track their activity across different sites. Third-party cookies, in particular, are set by a domain other than the one being visited, and are used to track users across multiple websites.

Marketing attribution models use third-party cookies to track the journey of a customer from first touch point all the way to conversion. However, with the increasing concern for privacy and the rise of privacy-focused browsers, the use of third-party cookies has become more limited. Some browsers have started to block third-party cookies by default, making it more difficult to track user behavior across multiple sites. You can read mode about the cookie-less marketing measurement here:

How does MMM work?

MMM works by using historical sales and marketing data to build a statistical model that shows the connections between marketing inputs and business outcomes. The model takes into account various factors such as sales data, marketing spending, weather, holidays, events, etc. to determine the impact of marketing efforts on ROI.

Once the model is built, it can be used to show the impact of the marketing mix on incremental sales, margin, as well as ROMI. For example, if a company wants to determine the optimal level of marketing spending, they can use the model to simulate the impact of different budget allocation strategies on sales and other business KPIs. By comparing the results of these simulations, the company can determine the optimal level of marketing spending that will achieve the desired results (such as budget allocation, target reach etc).

MMM can be a valuable tool for marketers as it provides a scientific, data-driven approach to understanding the impact of marketing efforts. It can be used to optimize the marketing mix, allocate marketing spending more effectively, and make informed decisions about future marketing initiatives.

MMM does not use third-party cookies since MMM relies on historical sales data or first-party data. As mentioned, third-party cookies have been a key tool for tracking customer behavior across multiple sites, but they are becoming less common due to privacy concerns. MMM does not require this type of tracking, as it focuses on the overall relationship between marketing inputs and business outcomes, rather than individual customer journeys.

Therefore, MMM can still provide valuable insights into the impact of marketing efforts, even in an environment where third-party cookies are becoming less available. MMM can still help you make informed decisions about future marketing initiatives, without the need for individual-level tracking.

There are also different types of MMM on the market, each coming with its own benefits, costs, and timelines. You can read more about the different MMM types here.

What are the key differences between MMM and MTA?

We could say that pretty much everything is different between the two types of marketing measurement models, however, that would be too vague and we don’t like vague. So, let’s break it down:

• Business data: While MTA uses user-level data, MMM focuses on aggregated data like Day-Product-Location level business data
• Marketing data: MTA uses own data and third-party cookies from platforms. MMM models are constructed by using first-party data and wider data sets like promotions to describe the drivers. Therefore, there are no foreseeable legislative barriers as in cookie world.
• Methodology: Most MTA methods are based on algorithmic or subjective user journey mapping on digital media clicks while MMM is based on scientific methods like Bayesian modeling.
• Time-frame: MTA focuses only on the conversions and don’t take into account time after the desired business KPI.
• Cost: As MMM models are typically more complex, require more time to set them up and run on a continuous basis, they are also more expensive than MTA
• Data privacy: MTA is based on company’s own conversion data + third party data. MMM focuses on using only first party data
• Budget optimization: MMM optimizes your marketing spend in order to make the most for your buck. Essentially, it allocates the optimal budget to each media channel or campaign in order to drive the maximum amount of incremental sales. You can read more about the maximum potential of your marketing investments here .
• Use cases: It’s clear that MTA and MMM have different use cases. MTA focuses on analyzing customer journeys while MMM is trying to find the true incrementality of media channels and forecasting optimal media spending per channel.
• Implementation: SaaS MMM is an entire project on its own that requires an implementation time as well as a higher cost and more commitment than MTA. You can read more about the implementation process here.

Below you can see a chart with a more detailed comparison between the two:

Pros and cons of MTA

Tracking the customer journey through touchpoints can bring several benefits to your business, and depending on your goals, MTA can offer you an outlet to explore potential new strategies. For example, MTA can help you establish certain tends across your marketing channels such as the most active first touchpoints.

Other benefits include:

• Holistic View of the Customer Journey: MTA models provide a more complete and accurate picture of the customer journey, including the role of different touchpoints in the path to conversion.
• Improved Decision Making: By providing a more complete and accurate understanding of the customer journey, MTA models can help companies make informed decisions about their marketing mix and budget allocation.
• Micro level optimization: MTA models provide a micro-level view of the customer journey, enabling companies to optimize their marketing mix across multiple channels.

The concept of attribution modeling appears promising as it allows marketers to analyze the customer journey and determine the role each touchpoint plays in generating a conversion. This, in theory, should lead to a more efficient allocation of marketing budgets towards the channels, campaigns, and efforts that have the strongest impact on revenue. In reality, attribution modeling often falls short of its potential.

Attribution models can seem like a convenient, easy to implement solution because many models are free or relatively low cost in comparison to a MMM SaaS solutions. However, relying solely on attribution models can be misleading and lead to incorrect budget allocation decisions. While the initial cost may be low, the cost of poor marketing decisions based on inaccurate attribution data can be significant. On the other hand, continuous MMM can help to identify the true impact of marketing efforts on revenue. Despite the upfront cost, the long-term benefits of using an MMM service in terms of improved marketing ROI can outweigh the initial investment.

Another limitation of MTA is that it can lead to the overvaluation of certain marketing channels like SEM or programmatic. This happens when the model assigns too much credit to a particular channel without taking into account the influence of other channels in the customer journey. This can result in incorrect budget allocation decisions, where more resources are invested in channels that may not be driving significant revenue, while other more impactful channels are underfunded. For better accuracy attribution results should be combined with additional marketing measurement tools.

Pros and cons of MMM

MMM comes with numerous benefits for your company. Depending on your choice, in-house vs SaaS vendor, there are different features that can be included in your model. However, the most stand-out benefits of using SaaS MMM are:

• Improved Decision Making: MMM can help you make more informed decisions about which marketing channels and tactics to invest in and how much budget to allocate. MMM can also track your offline marketing channels, which allows you to have a more encompassing overview of your ROI.
• Increased ROI: With MMM you can identify which channels and tactics are driving the most sales and optimize your marketing budget towards the most profitable channels and tactics. This way, your marketing budget becomes a valuable investment rather than a cost.
• Improved Understanding of Your Customers: MMM can also help you understand your customer pool better. While it does not track the customer journey in the same way MTA does, MMM can still tell you which marketing channels are engaging which customer segments. This way, you can adjust your marketing investment based on your goals. For example, if new customer acquisition and brand growth are your priorities, then MMM can help you understand which strategies to employ in order to achieve them.
• Improved Customer Segmentation: As mentioned above, MMM can help you segment your customer more effectively and target them with the right message at the right time.
• Improved Attribution Modeling: Market mix modeling can help you understand which channels and tactics are most influential in inspiring customers to take action. Since MMM does not use third-party cookies, it can be an interesting tool in your cookieless attribution marketing toolbox.

There are also a few limitations to MMM that you must consider before deciding on an MMM project.

• MMM relies on historical data to build models, depending on your marketing channels, you will need historical data from different timeframes. You could be looking at 12-24 months of data.
• Due to the complexity of the model, you will most likely need onboarding & training in order for you to effectively use the MMM solution
• MMM solutions are typically more expensive than MTA solutions

Which one is better for your business?

In today's fast-paced business environment, staying ahead of the competition requires staying on top of the latest marketing technologies. MMM is a valuable tool for gaining a competitive advantage, as it provides a comprehensive view of the marketing mix and the impact on sales. By incorporating MMM into your marketing strategy, you can make informed decisions that drive business growth and success.

MMM focuses on understanding the impact of various marketing initiatives on sales and market spend, typically at a macro level. It uses statistical models to examine the relationship between marketing inputs (such as advertising spend, promotional activities, pricing, etc.) and sales outcomes. MMM helps companies optimize their marketing mix and allocate their marketing budget effectively.

On the other hand, Marketing Attribution Modeling is a method used to allocate credit for sales or conversions to the various touchpoints in the customer journey. The goal of attribution modeling is to understand which marketing initiatives are most effective in driving conversions and to allocate marketing spend accordingly. Unlike MMM, attribution modeling is typically performed at a micro level and focuses on individual customer interactions with the brand.

Ideally, a top marketer would use both MMM and MTA in 2023 since this is a year of transition for the entire marketing branch due to the new cookie regulation as well as the e-commerce boom after the COVID-19 pandemic. Can MMM fully replace MTA? No, however, it allows marketers to grasp insights from their marketing and sales data that are not present anywhere else. If staying ahead of the competition is a priority, then staying on top on the latest marketing tech is a must. Reach out to us if you’d like to know more.

The disappearance of third-party cookies does not necessarily mean that marketing attribution models will become obsolete. While third-party cookies have been a key tool for tracking user behavior and attributing conversions, the loss of this tool has created an opportunity for the implementation of new and more privacy-focused methods for tracking and attribution.

MMM provides a comprehensive view of the marketing mix and helps identify the true drivers of sales, allowing marketers to make informed decisions about marketing budget allocation and strategy. However, attribution models provide a view of the customer journey and the impact of individual touchpoints, which can be useful for optimizing conversion rates.

While MMM cannot fully replace attribution, it offers a complementary perspective that can provide a deeper understanding of the relationship between marketing spend and sales. By combining the insights from both approaches, marketers can make informed decisions that lead to better marketing ROI.

To stay ahead of the competition, it's important to have a comprehensive understanding of the impact of marketing efforts on sales and revenue. Both attribution models and marketing mix models provide valuable insights, but they have different strengths and limitations. By combining the insights from both approaches, marketers can make informed decisions that lead to better marketing ROI. This can include optimizing conversion rates, allocating budget more effectively, and identifying the most effective marketing mix.

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