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​​Battle of Models: MMM vs. MTA - Adsmurai

​​Battle of Models: MMM vs. MTA

Both Marketing Mix Modeling and Multi-Touch Attribution Modeling are valuable tools in marketing analysis. While MMM focuses on understanding the overall impact of marketing elements, Multi-Touch Attribution Modeling provides insights into the specific contribution of each touch point in the customer journey. 

By leveraging these approaches, companies can make informed decisions, allocate resources intelligently and improve their marketing strategies for better results.




Marketing Mix Modeling (MMM) and Multi-Touch Attribution Modeling are two approaches used in marketing analytics to understand the effectiveness and impact of different actions of a marketing strategy.


Marketing Mix Modeling (MMM)

It is an advanced statistical technique used to measure and evaluate the impact of various marketing assets (online and offline) on a company's sales or other key performance indicators that may be affecting our objective (KPI around which we are going to optimize the model). These indicators typically include variables such as advertising spend, pricing strategies, distribution channels, etc.

MMM involves analyzing historical data to identify relationships between marketing assets and results. By quantifying these relationships, the contribution of each action to overall sales or KPIs can be estimated. MMM helps answer questions such as: What impact does advertising have on sales, what is the optimal allocation of marketing resources across channels, or how do price changes affect sales?

MMM models require a robust data set that encompasses historical marketing and sales data, along with other relevant variables such as seasonality, market trends or other competitive factors. The analysis involves creating a statistical model that captures relationships and generates information to inform marketing strategy and resource allocation.


Multi-Touch Attribution Modeling (MTA)

On the other hand, the Multi-Touch Attribution Model is an analytical approach that aims to attribute credit or assign value to each marketing touchpoint or interaction along the customer journey. 

In today's complex marketing landscape, users often interact with multiple marketing channels and touch points before making a purchase or conversion. These attribution models help companies understand the contribution of each touchpoint in the conversion process.

There are several attribution models within multi-touch attribution, such as:

  • First Touch Attribution: Gives credit to the first touchpoint a customer interacts with before converting.

  • Last Touch Attribution: Assigns credit to the last touchpoint before conversion.

  • Linear Attribution: Distributes credit equally among all touchpoints in the customer journey.

  • Time-Decay Attribution: Allocates more credit to touchpoints closer to conversion and less to earlier ones.

These models, among others, offer different perspectives on how touchpoints contribute to conversions. The choice of attribution model depends on business objectives and the specific dynamics of the customer journey.

The Multi-Touch attribution model uses data tracking and analytics techniques to gather information about customer interactions across multiple marketing channels and touchpoints. By understanding the impact of each touchpoint, companies can optimize their marketing assets, allocate resources effectively and improve overall campaign performance.


What is Marketing Mix Modeling (MMM)?

Marketing Mix Modeling (MMM) is an analytical tool that allows us to understand how different aspects of marketing affect the performance of a business. Instead of analyzing each marketing element separately, MMM considers the combined impact of all these elements. This includes aspects such as advertising spending, promotions, pricing and distribution channels. By examining historical data, MMMs help us identify the relationships between these elements and sales.

By understanding the influence of each marketing element on our objectives, we can make better decisions about how to allocate our resources. We can discover which aspects of our marketing strategy are most effective and which need improvement. In addition, the MMM helps us plan our marketing budget more intelligently, optimizing our investment for the best results.

Broadly speaking, the operation of an MMM involves the following steps:

  1. Data collection: Relevant historical data is collected, such as information on sales, advertising expenditures, promotions, pricing, distribution channels and other marketing-related factors.

  2. Statistical analysis: The data collected is subjected to advanced statistical analysis. This involves the use of econometric techniques and mathematical models to determine the relationships and patterns between different marketing elements and sales.

  3. Model building: Based on the above analysis, a customized MMM model is built for each company. This model represents the quantitative relationship between marketing actions and sales results, taking into account factors such as seasonality or other external influences.

  4. Simulations and scenarios: Using the MMM model, simulations are run and what-if scenarios are created to evaluate how different adjustments to marketing elements can affect business results. This provides valuable information for decision making and marketing strategy optimization.

  5. Optimization and decision making: Based on the results of the MMM model and the simulations performed, informed decisions can be made about the allocation of marketing resources. This involves adjusting budgets, optimizing marketing strategies and maximizing return on investment (ROI).

  6. Continuous monitoring and updating: MMM models are iterative and continuous processes. Actual results are monitored and the model is updated to reflect changes in marketing factors or the business environment. This allows for continuous improvement in decision making and optimization of the marketing strategy.

Overall, while MMMs provide valuable information on the effectiveness of different marketing elements and the optimization of resources, it requires expertise, reliable data and careful consideration of its limitations to produce accurate and actionable results.

For Against
  • Holistic view

  • Aggregate data, not granular

  • Restrictive reporting policies and user-level date do not affect

  • Data-driven insights based not only on investment and target KPI, but also on brand environment and context

  • Resource allocation optimization

  • Decision making on budget allocation across channels with desired recurrence

  • Requires advanced tools and a team of experts
  • Need for access to accurate, complete and large volumes of data
  • High efforts in data collection, analysis and modeling


What is Multi-touch Attribution Modeling (MTA)?

Multi-touch Attribution Modeling (MTA) is an analysis technique that seeks to understand how each touch point or interaction with customers throughout their buying journey contributes to generating business results. Rather than attributing all credit for a conversion to a single touch point, the multi-touch attribution approach recognizes and assigns value to each relevant interaction. This enables a more accurate understanding of how different marketing channels and tactics work together to influence customer decisions.

It is data-driven and uses analytical methods to determine the relative contribution of each touchpoint in generating conversions. These models can vary in complexity and can take into account factors such as the sequence of interactions, the time elapsed between interactions and the differential impact of each touchpoint. By understanding how different interactions combine and complement each other, companies can optimize their marketing strategies, allocate resources more effectively and improve the efficiency and effectiveness of their campaigns.

How Multi-Touch Attribution Modeling (MTA) works involves the following steps:

  1. Data collection: Data is collected from customer interactions throughout their buying journey. This data may include records of clicks, website visits, ad views, social media interactions and other relevant activities.

  2. Touchpoint identification: The different touchpoints or interactions a customer has with the brand throughout their journey are identified and recorded. These can include ads viewed, emails received, online searches, website visits, social media interactions and more.

  3. Value assignment: Value is assigned to each touchpoint based on its influence on the conversion process. This can be done using different attribution models, which can be predefined rules, statistical algorithms or more advanced models based on machine learning.

  4. Analysis and insights generation: Data is analyzed and insights are generated on the contribution of each touchpoint in the conversion process. This may include identifying the most effective touch points, understanding sequential interactions, and determining which combinations of touch points have the greatest impact on generating results.

  5. Optimization and decision making: Using the insights gained, companies can make informed decisions about the allocation of marketing resources. They can adjust their strategies to maximize the effectiveness of the most influential touch points, optimize the sequence of interactions and improve the overall customer experience.

It is important to note that the effectiveness of MTA models can vary depending on the industry, business model and data availability. Organizations should carefully consider these pros and cons and tailor their approach based on their specific needs and capabilities.

For Against
  • Less data volume required than in Marketing Mix Modeling
  • Detailed information on each touch point
  • Data-driven decision making
  • Personalization and segmentation
  • Complex implementation
  • Restrictive granular-level information policies make it impossible to work with the entirety of data and require modeling
  • Attribution challenges, introducing subjectivity and limitations.
  • Methodology focused at campaign level, at business level MMM is preferable
  • Lack of real-time information



Differences between Marketing Mix Modeling and Multi-Touch Attribution Modeling

Understanding the differences between Marketing Mix Modeling (MMM) and Multi-Touch Attribution Modeling is crucial for marketers and companies for the following reasons:

  • Integral vision

MMM provides a holistic view of the overall impact of various marketing elements on business results or KPIs. It takes into account the combined effect of different marketing elements, such as advertising expenditures, promotions, pricing strategies and distribution channels. 

On the other hand, the Multi-Touch Attribution Model focuses on the analysis of individual touch points and their contribution to conversions. By understanding both perspectives, marketers gain a complete picture of the effectiveness of their marketing efforts and can make more informed decisions.

  • Optimization of resource allocation

MMM helps optimize resource allocation by identifying the marketing actions with the greatest impact. It helps marketers understand which channels or actions are generating the best results. 

On the other hand, the Multi-Touch Attribution Model provides insights into the specific contribution of each touchpoint in the customer journey. By combining these insights, marketers can effectively allocate their resources across both channels and touchpoints, optimizing their marketing strategies for maximum impact.

  • Strategic decision making

MMM and Multi-Touch Attribution Modeling offer different perspectives that inform strategic decision making. MMM provides information on the long-term impact of marketing elements and helps to allocate and plan the budget. It helps answer questions such as, "How do different marketing activities contribute to overall sales?". 

On the other hand, Multi-Touch Attribution Modeling provides insights into the customer journey, helping marketers understand the effectiveness of specific touch points in driving conversions. It helps answer questions such as, "Which touchpoints have the greatest impact on conversions?" 

By understanding both perspectives, marketers can make well-rounded strategic decisions that align with their business objectives.

  • Campaign optimization

MMM and Multi-Touch Attribution Modeling play a crucial role in optimizing marketing campaigns. MMM helps marketers evaluate the effectiveness of their overall marketing mix and identify areas for improvement. It helps answer questions such as, "How can we optimize our marketing activities for better results?". 

Multi-Touch Attribution Modeling provides insights into the performance of individual touch points, enabling marketers to optimize specific elements of their campaigns. It helps answer questions such as, "Which touchpoints should we focus on to improve conversions?" By using both approaches, marketers can fine-tune their campaigns, strategically allocate resources and achieve better campaign performance.


Choosing the right approach for your company

When choosing between Marketing Mix Modeling (MMM) and Multi-Touch Attribution Modeling, several factors must be taken into account to make a decision:

  • Business objectives: Consider your specific business objectives. Determine whether you need a holistic view of the impact of your marketing strategy (MMM) or whether you need detailed information on individual campaigns (MTA). Align your chosen approach with your overall business objectives.

  • Data availability and quality: Evaluate the availability and quality of your data. MMM typically requires aggregated historical data, while MTA relies on more granular and detailed data on individual customer interactions. Assess whether you have access to the necessary data and whether it is of sufficient quality to support your chosen modeling approach.

  • Time sensitivity: Consider the time sensitivity of the information you need. MMM typically provides information on long-term trends and strategic planning, while MTA can provide real-time information for tactical optimization. Determine if you need immediate information or if you can work with a longer-term perspective.

  • Resources and expertise needed: Assess the resources and expertise needed to apply each approach. MMM typically requires advanced statistical modeling skills and specialized tools for data analysis, while attribution modeling may require expertise in data collection, attribution modeling techniques and advanced analytics. Consider whether you have the capabilities in-house or whether outside experts such as Adsmurai are needed.

  • Scope and granularity: Evaluate the level of granularity you need in your analysis. MMM provides an overall view of the marketing mix as a whole, while attribution modeling focuses on individual touchpoints. Consider whether you need a broader understanding of the overall impact or whether you need detailed information on the contribution of specific touchpoints.

  • Industry and channel characteristics: Consider the unique characteristics of your industry and your marketing channels. Some industries or channels may lend themselves better to one modeling approach than another. Evaluate which approach best fits your industry dynamics and marketing landscape.

  • Cost and budget: Evaluate the economic implications of each modeling approach. Consider the investment required in terms of technology, tools, expertise and data collection efforts. Evaluate whether the potential benefits outweigh the costs and fit within your available budget.

With these factors in mind, you will be able to make an informed decision about which approach is best suited to your specific needs and objectives. It may also be worth exploring whether a combination of both approaches can provide a more complete understanding of your marketing effectiveness.

Using the models together can provide a comprehensive understanding of marketing effectiveness. Here's a brief explanation of how these approaches can be used together:

  1. Start with MMM to analyze the overall impact of your marketing mix on business results. Use MMM to identify the relative contribution of different marketing elements, such as advertising, promotions, pricing and distribution channels. This will provide a holistic view of the effectiveness of your overall marketing strategy.

  2. While mobility management provides an overview, it is essential to collect detailed data on each customer's interactions and touch points. Collect data on customer journeys, interactions and conversion events across multiple channels and devices. This data will serve as the basis for attribution modeling.

  3. Use MTA to analyze the specific contribution of each touchpoint to drive conversions and desired outcomes. Attribution models, such as first touch, last touch, linear or data-driven, help assign credit to individual touch points based on their influence.

  4. It complements both perspectives to gain a comprehensive understanding of marketing effectiveness. We recommend working with both MMM and MTA methodologies. Both analyses help validate results, uncover new insights and refine marketing strategies.

  5. Use MMM and MTA information to optimize your marketing efforts. MMM provides strategic insights on resource allocation, budgets and overall optimization of the marketing mix. While MTA provides tactical information on the optimization of specific touch points, campaigns or channels. Use this information to make data-driven decisions, adjust marketing strategies and allocate resources effectively.

  6. Continuously monitor the performance of your marketing activities and refine your models over time. Update models based on new data, changing market dynamics and business objectives.

By combining the holistic view of MMM with the granular insights of Attribution Modeling, you can gain a comprehensive understanding of the effectiveness of your overall marketing strategy. This integrated approach will enable you to optimize resource allocation, refine strategies and make informed decisions that drive business growth.

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