Media Mix Modelling 101: An Introduction to MMM
Table of Contents
Welcome to a beginner’s guide to understanding media mix modelling (MMM). In this article, we will explore the concept of media mix modelling, its importance in marketing analytics, key components involved, benefits, limitations, implementation steps, challenges, and real-world case studies. So, let’s explore the world of media mix modelling.
1. Introduction
Marketing has evolved significantly in the digital age, with companies investing in various advertising channels such as TV, radio, print, online, and social media. With so many platforms available, it becomes crucial for businesses to understand the impact of their marketing efforts on sales and customer behavior. This is where media mix modelling comes into play.
2. What is Media Mix Modelling?
Media mix modelling is a statistical analysis technique used to measure the effectiveness of different marketing channels and determine the optimal allocation of resources across these channels. It involves analyzing historical data to quantify the impact of various marketing activities on sales, customer acquisition, brand awareness, and other key performance indicators.
3. Why is Media Mix Modelling Important?
Media mix modelling provides valuable insights to businesses by helping them make data-driven decisions about their marketing strategies. It allows companies to understand the return on investment (ROI) of each marketing channel and optimize resource allocation to maximize the overall marketing impact.
4. Key Components of Media Mix Modelling
Media mix modelling consists of several key components that work together to provide meaningful insights. Let’s explore each of them:
4.1 Data Collection and Preparation
The first step in media mix modelling is to gather relevant data from various sources, including, for example, sales data, media spending, market research, and customer behavior. This data needs to be cleansed, transformed, and consolidated to create a unified dataset for analysis.
It must be noted that to conduct proper analysis you need a lot of historical data, at least a year if possible for best results.
4.2 Statistical Modelling
Once the data is prepared, statistical models are developed to establish relationships between marketing inputs (such as advertising spend) and business outcomes (such as sales). These models can be developed using techniques such as linear regression, time series analysis and then refined through machine learning.
4.3 Attribution Analysis
Attribution analysis aims to assign credit to different marketing activities based on their contribution to desired outcomes. It helps determine the incremental impact of each channel and paints a picture of each channel’s contribution to overall outcomes.
4.4 Optimization and Forecasting
In this stage, the models are used to optimize marketing investments by finding the ideal allocation across channels. By using MMM advertisers can make better decisions and give themselves a greater chance of success in the future through planning their marketing budgets effectively.
5. Benefits of Media Mix Modelling
Media mix modeling offers several benefits to businesses:
- Data-driven decision-making: By leveraging historical data and advanced analytics, companies can make more informed decisions about their marketing strategies instead of relying entirely on intuition or guesswork.
- Optimized resource allocation: Media mix modelling helps identify underperforming channels and reallocate resources to those with higher ROI, resulting in improved marketing effectiveness.
- Improved budget planning: By forecasting the impact of different marketing scenarios, businesses can allocate budgets more accurately and set realistic goals.
6. Limitations of Media Mix Modelling
While media mix modelling is a powerful technique, it has certain limitations:
- Data limitations: Availability and quality of data can impact the accuracy and reliability of the models. Incomplete or biased data may lead to misleading insights.
- Complexity of analysis: Media mix modelling requires statistical expertise and advanced analytical tools. Companies may need to invest in skilled resources or collaborate with external experts.
- Changing media landscape: With new marketing channels and touchpoints emerging, models have to be rebuilt with the introduction of new data points, which creates challenges in accurately capturing and measuring the impact of all channels.
6.1 Software
Whilst media mix modelling can be completed manually, it is important to choose a software provider to give you the outputs you need. There are many to choose from. For guidance on how to choose a software, please see our article on choosing a software partner in 2023.
7. Steps to Implement Media Mix Modelling
Media Mix modelling is run in an iterative loop, but generally follows these steps:
7.1 Define Objectives
Clearly define the objectives you want to achieve through media mix modelling, such as improving ROI, understanding channel synergies, or optimizing marketing spend.
7.2 Gather Data
Collect relevant data from various sources, including sales data, marketing expenditures, customer demographics, and any other variables that may impact the business outcomes, including context variables that might affect your business where relevant – like economic confidence, or mean temperature.
7.3 Select Variables
Identify the key variables to be included in the analysis. These can be advertising spend, media impressions, website traffic, social media engagement, or any other factors that influence your objectives.
7.4 Build Models
Develop statistical models that relate the selected variables to the desired outcomes. Choose appropriate techniques based on the nature of your data and objectives. Factors are considered like seasonality, market dynamics and any external events (such as sales, service outages, promotions) that may affect the results.
7.4 Build Models
Develop statistical models that relate the selected variables to the desired outcomes. Choose appropriate techniques based on the nature of your data and objectives. Factors are considered like seasonality, market dynamics and any external events (such as sales, service outages, promotions) that may affect the results.
7.5 Analyze, Validate and Refine
Analyze the results to gain insights into the effectiveness of each marketing channel and understand their individual contributions to business outcomes.
Validate the models using historical data and refine them to improve accuracy. Consider factors like seasonality, market dynamics, and external events that may affect the results.
Conduct additional experiments using Randomised Control Trials (RCT) to prove out causality between factors.
7.6 Optimize and Implement
Based on the insights gained, optimize your marketing mix by reallocating resources across channels to maximize the overall impact. Implement the recommended changes and monitor their performance over time.
8. Conclusion
Media mix modelling is a powerful analytical technique that enables businesses to make informed decisions about their marketing strategies. By analyzing historical data, businesses can optimize their marketing spend and improve overall marketing effectiveness. However, it is essential to overcome challenges such as data quality, analysis complexity, and the evolving media landscape to achieve accurate insights.
Incorporating media mix modelling into your marketing analytics toolkit can drive better results and help you stay competitive in the ever-changing landscape of digital marketing.
9. FAQs
Yes, media mix modelling can be applied to various industries, including retail, e-commerce, finance, and healthcare. The fundamental principles remain the same, but the specific variables and metrics may differ based on the industry and business goals.
The frequency of conducting media mix modelling depends on various factors such as business objectives, budget cycles, and the pace of market changes. It is recommended to conduct the analysis at least once a year or when there are significant changes in the marketing strategy or external environment.
Yes, media mix modelling can analyze the impact of both online and offline marketing channels. It aims to provide a holistic view of all touchpoints that contribute to business outcomes.
Yes, media mix modelling can be beneficial for small businesses as well. It helps them understand the impact of their marketing efforts, optimize their limited resources, and improve ROI.
The timeframe to see the results of media mix modelling implementation may vary based on the complexity of the analysis, data availability, and the speed of decision-making within the organization. First a model must be built – building the model typically takes a few weeks, but depending on the complexity involved this can take a little longer.
In terms of making the changes recommended by MMM, the timeframe in which the impact can be seen is variable. These can be immediate i.e. cutting a channel that does not meaningfully contribute to the overall goal or reveal longer term improvements i.e. showing great cost efficiency month on month.
