What is Marketing Mix Modeling? Understanding the Basics of MMM

Have you ever wondered how to maximize the effectiveness of your marketing spend? With Marketing mix modeling (MMM), you can gain valuable insights to determine which specific channels and advertising campaigns are working best for your brand so that you could make smarter data-driven decisions when allocating marketing budgets. And there are even more benefits. Let’s dive in!

Content:

• What is Marketing Mix Modeling (MMM)?

• Why should marketers use Marketing Mix Modeling?

• What are the limitations of Marketing Mix Modeling?

• How to build a Marketing Mix Model?

What is Marketing Mix Modeling (MMM)?

Marketing Mix Modeling (MMM) is a powerful tool used to measure and analyze the effectiveness of marketing activities. It helps companies understand how their marketing investments influence their target KPIs, identify which marketing activities are most effective, and accurately predict future performance. MMM enables marketers to optimize their marketing mix in order to get the maximum return on their marketing investments.

Marketing Mix Modeling (MMM) is a data-driven statistical analysis technique. Data scientists develop advanced multivariate statistical models that analyze the contribution of numerous drivers to target KPIs.

To put it simply, Marketing Mix Modeling is an essential tool for marketers looking to get the most out of their campaigns. It helps better understand how different elements of the marketing strategy such as advertising, social media, and promotions are working together with external factors, e.g. weather, seasonality, or competitors' activity.

By measuring the impact of each marketing activity on the ROI, businesses can make smarter decisions which marketing activities to invest in and which ones to cut. The goal is to forecast the impact of future campaigns, reallocate marketing budgets and optimize the media mix and promotional tactics in order to maximize the target KPI, e.g. sales revenue or profit.

Which models are used in the MMM?

Linear regression model

The linear regression model is the simplest form of analysis for marketing mix modelling. It helps to analyse the relationship between two variables - for example, how turnover (dependent variable) changes depending on marketing expenditure (independent variable).

Independent variables are factors such as the level of marketing spend, which are considered as influencing variables. Dependent variables are the results that are influenced by these variables, such as sales.

The model describes the relationship between the variables by drawing a straight line through the data points. This line illustrates the relationship between the independent variable and the dependent variable. A steeply rising line indicates that higher marketing expenditure tends to lead to higher sales. A flat line, on the other hand, shows that marketing expenditure only has a minor influence on sales.

The linear regression model is particularly suitable for analysing the direct influence of a single marketing measure on a business result. It is particularly useful in scenarios in which a linear relationship between the variables is expected.

Multiple regression model

The multiple regression model extends the linear regression approach by analysing the influence of several independent variables simultaneously on one or more dependent variables. This makes it possible to understand the combined effect of different marketing activities such as TV advertising, digital campaigns or promotions on sales or other KPIs. This model provides a more comprehensive overview of the marketing mix and is particularly suitable for more complex scenarios in which several factors influence the result.

Bayesian model

The Bayesian model incorporates prior knowledge or assumptions into the analysis and updates them as new data becomes available. This gives the model particular strength in dealing with uncertainty and variability in the marketing data. This method allows for more flexible and dynamic modelling, especially when the available data is sparse or uncertain. The model is ideal when prior knowledge can be incorporated into the modeling or when complex and uncertain environments exist where traditional methods may reach their limits.

Hierarchical model

The hierarchical model, also known as the multi-level model, analyses data that is structured at different levels, such as regions, branches or customer segments. It takes into account variations at each of these levels, providing detailed and segmented insights. This model is particularly useful for organisations that want to understand the impact of marketing activities in different markets or segments and how they differ and interact with each other at different levels.

Which target KPIs could be optimized with MMM?

Marketing Mix Modeling enables the optimization of a variety of Key Performance Indicators (KPIs) that are directly related to the effectiveness of marketing activities. By analysing and adjusting these KPIs, companies can precisely align their marketing strategies and achieve better business results.

The following KPIs can be optimized by MMM:

• Sales revenue
• Conversions
• Transactions
• App installs or Downloads
• Registrations
• Leads
• Customer Lifetime Value
• Brand Awareness
• ROI (Return of Investment)
• Customer Acquisition Cost

Why should marketers use Marketing Mix Modeling?

Marketing Mix Modeling is an essential tool for marketers who like taking data-driven decisions based on valuable insights, aiming to refine the marketing strategy for optimal results.

With an MMM solution, marketers can identify the most effective channels that contribute to sales, ROI, or other target KPIs. Based on this, they can make better decisions about media budget allocation and refine their marketing mix in order to maximize the ROI.

With this technology, marketers can also gain a clear understanding of the effectiveness of their campaigns. As a result, they can use the insights provided by these solutions when building new or refining existing campaigns to ensure their success. Additionally, marketers can also measure the short-term and long-term effects that campaigns generate.

MMM also helps understand the impact of external factors such as the macro- and microeconomic environment, competition, and seasonality on the target KPI.

What are the benefits of Marketing Mix Modeling?

• Develop an understanding of what influences the target KPI
• Optimize current and future marketing campaigns
• Allocate budgets efficiently across the media mix

What are the limitations of Marketing Mix Modeling?

While MMM is an immensely useful tool for marketers, it comes with a few limitations.

Firstly, the accuracy of the results heavily depends on the data quality and completeness of the dataset used. If the data is inaccurate or incomplete, the output of the analysis might be distorted. The time series length of the data set is also important. To get a high-quality model, data from the last few months is not enough. Depending on the model, an average of three years of data may be sufficient.

It's also important to take all (or at least most) relevant factors into account. For example, if we are modeling sales of ice cream and don't take the weather into account, the model's accuracy will always be inferior.

Additionally, modeling techniques are only as good as the software and algorithms used for analysis. If these are not updated frequently, marketers may see subpar performance compared to models using more advanced methods driven by recent machine learning developments, or sophisticated approaches like bayesian modeling.

How to build a Marketing Mix Model?

Step 1 - Define the objectives: The first step of Marketing Mix Modeling is defining the objectives relevant to your business. You should consider what success will look like in terms of profitability and sales.

Step 2 - Collect data: Before you get started with Marketing Mix Modeling, you need to collect enough data to measure the outcomes accurately. This data should include sales figures, competitor activity, media variables (both contacts e.g. reach or impressions, and spend), promotions, and external factors like weather, inflation rate, and holidays as well. Using this data, you'll be able to identify trends and create insights about your own business performance.

Step 3 - Analyze data: Now it's time for the analysis of all the data collected previously. This process usually involves running different regression algorithms such as simple linear models or multiple regressions in order to isolate the impact of each component of the marketing mix on sales or profitability in specific markets or regions.

Step 4 - Determine the impact of activities: Using the results of the analysis, marketers can now test various hypotheses related to their campaigns without having to make any actual changes. This process helps determine which activities are effective and which ones are not, so the marketers can refine their budgets even further with optimization techniques to maximize the effectiveness and ROI across channels

Step 5 - Generate insights: Marketing Mix Modeling allows marketers to generate high-level insights into how they can optimize their activities in order to generate more leads or increase conversions at a lower cost per acquisition (CPA).

Step 6 - Monitor & adjust strategies: After analyzing the impact of individual factors on the target KPI, it’s important for marketers who do this type of modeling to follow up on its performance by monitoring campaigns against corresponding goals and metrics over time on a regular basis so they know whether their strategies are achieving the desired results or need adjustment as conditions change.

Step 7 - Reassess the objectives: Finally, once the results of these strategies have been tracked over a certain period of time, one should reassess their original goals and objectives based on recent performance metrics so that accurate forecasts of marketing ROI and growth possibilities can be made going forward to support executives and stakeholders in the decision-making process.

What are the most frequently asked questions about marketing mix modeling?

How is the model created?
Historical data on the target KPIs is combined with the relevant influencing factors to create a modeling data set. Each entry in the data set corresponds to an experiment with measured values for the target KPIs and the influencing factors (the more granular the data, the more experiments the data set contains, the more reliably the algorithm learns the cause-effect relationships. Variables are analyzed). Mathematical models are trained on the basis of the data set, which learn the cause-effect relationships.

What results does an MMM deliver?
A marketing mix model (MMM) quantifies the impact of marketing activities on KPIs such as sales or market share and optimizes budget allocation. It analyzes marginal benefit curves to show when additional spending in a channel is less effective. It also calculates the ROI per channel and helps to identify the most efficient ones. The MMM makes recommendations for optimal budget usage and simulates different budget scenarios to maximize revenue and profit. It provides data-based insights for future campaigns and budget decisions.

How often should the model be updated?
Updates are valuable because they can determine whether or not actions taken have led to greater media efficiency. As a standard, it is recommended to start with quarterly updates. However, depending on the brand, business model and data situation, it may make sense to update the models more frequently (e.g. monthly, weekly)

What data is required?
Historical data is required for each week for at least two years retrospectively. Data is required for at least one target KPI and for the relevant influencing factors, in particular, of course, media activities, but also sales activities (discounts, promotions), for example. Depending on the brand, business model and target KPI, the relevant influencing factors can vary greatly. In addition, the more detailed the data available, the more reliable and valuable the models trained on it will be. For example, it is better if the data is available per day and not per week. It is even better if the data is available per day and per federal state, for example.

What are the costs?
The costs vary depending on the scope of an MMM project. A key driver is usually the number of different brands and countries to be modeled. As a rule of thumb, an MMM project with quarterly updates that is to be carried out for one brand and one country can be expected to cost around €25-50k per year.

Which industries benefit most from marketing mix modeling?
Ultimately, many industries that have media spend across multiple channels can effectively use MMMs to optimize their marketing approach. However, the benefits increase with increasing complexity, i.e. the more budget, channels or markets are involved.

Here you can find more information on: The most frequently asked questions about Marketing Mix Modeling.

Conclusion

Marketing mix modeling is a powerful tool for measuring and optimizing the performance of marketing activities. With efficient marketing mix modeling, marketers can gain valuable insights into their advertising campaigns and make informed decisions based on the data. The potential impact of marketing mix modeling is huge, and it is a must-have tool for any marketer who wants to maximize the value of their marketing campaigns.