Origins of MMM

As a result of developments in marketing research, data availability, and statistical approaches, media mix modeling has evolved. While it is difficult to identify a specific period or individual who ‘invented’ MMM, its roots can be traced back to the mid-20th century development of econometric modeling and marketing mix analysis.

The origins of econometric modeling may be traced back to the 1930s and 1940s when scientists such as Jan Tinbergen and Ragnar Frisch were awarded the first Nobel Prize in Economic Sciences in 1969 for their work in building and implementing dynamic models to understand economic processes.

Marketing scholars began to use econometric approaches to investigate the influence of marketing activities on sales in the 1960s and 1970s, creating the groundwork for media mix modeling.

With improved statistical tools and the availability of granular data on consumer behavior, media consumption, and marketing expenditures in the 1990s and 2000s, the adoption of media mix modeling gained traction.

Early adopters of media mix modeling included large consumer packaged goods (CPG) companies and enterprises with large marketing budgets that sought to optimize spending across multiple channels. MMM has gradually gained traction in various industries, including retail, automotive, financial services, and telecommunications.

What is Media Mix Modeling?

We’ve all heard of the four Ps of marketing: product, price, place, and promotion. It is a core component of marketing theory that uates what factors are necessary for a business to prosper.

Marketing mix modeling is closely related to the 4Ps. It aims to establish how much success was achieved by each factor and anticipate how much future success can be generated by changing and improving the marketing mix.

Marketing mix modeling is a statistical tool that helps estimate the effectiveness of marketing campaigns. It does this by breaking down aggregate data and differentiating between the impact of marketing tactics, promotional activities, and other factors beyond our control.

The outcomes of your marketing mix model study, or ‘output,’ will then inform the composition of future marketing efforts with a high degree of certainty. Changing input, ‘a’ will alter the output, ‘b.’

Benefits of Media Mix Modeling

  • MMM allows marketers to demonstrate the ROI of their efforts.
  • It provides information that enables optimal budget allocation.
  • MMM allows for enhanced sales trend forecasting.

Limitations of Media Mix Modeling

  • It requires the ease of use of real-time modern data analytics.
  • Measurement criteria and transparency need to be improved. Obtaining information on how models are created or the metrics they employ is frequently challenging.
  • It is difficult to quantify advertising material.
  • Simply increasing investments by 10% may not always lead to a 10% increase in conversions.

Media Mix Modeling vs. Attribution

Media mix modeling is more comprehensive than attribution. It allows you to see the overall impact of marketing tactics across channels. Individual channels are examined to discover whether they are responsible for driving sales and money.

Attribution assists you in understanding how each channel contributes to your total success. Still, it must consider how they work together in an integrated marketing plan. On the other hand, media mix modeling is utilized when it is necessary to analyze long-term patterns and comprehend how present efforts will affect future results.

Attribution models such as multi-touch attribution are critical. On the other hand, brands can profit from combining solid attribution with media mix modeling.

When you apply media mix modeling, you will have aggregate data that shows:

  • What was the total sum of money spent on each channel?
  • What effect did this have on marketing performance and sales?

Furthermore, many marketers employ incrementality measurement. When all three are merged, they provide a holistic view of your marketing performance.

How Does Media Mix Modeling Work?

Media mix modeling can be a challenging and intricate task. There are various procedures to take, which will differ depending on the type of your firm. Here’s a overview of how it works:

1. Data Collection

Data is critical to every marketing plan, including media mix modeling. Not only do you need to gather data, but the quality of that data can make or break your decision-making process.

First, you must collect data from real people. You should use zero-party or first-party information. Your models will be accurate if you use reliable or complete data. Before proceeding, ensure the data is correct, complete, and representative of your target audience.

The information you collect should be as specific and detailed as possible, including the person’s age, gender, location, income level, education level, and occupation. You’ll want to know your spending and costs for each channel you utilize for MMM. Then there’s the CVR, or conversion rate, followed by transactions and sales.

2. Channel Mapping

Next, plan out the channels you’ll test (if any) and ensure an easy way to collect data.

For example, if you’re testing social media advertising on Facebook and Instagram, include a link to a survey asking respondents where they saw your ad. This will assist you in determining which channel is the most effective and whether spending on other ads on that platform is worthwhile.

Make it extremely clear how you will obtain the analytics you test to compare the various channels’ results and determine the most effective.

3. Aggregating Results

Once you’ve collected your data, it’s time to analyze it to identify which channel was most efficient in driving visitors and sales from each ad campaign.

It will allow you to decide whether marketing activities will succeed or fail based on all available facts rather than anecdotal evidence from each item alone.

4. Determine the Cause

After identifying the more effective advertisement and understanding its reasons, you can utilize this knowledge to develop a superior approach for your upcoming campaign. If you observed a reduced ROI from the ad campaign, your target audience probably consists of individuals who are unlikely to purchase. In such cases, trying to convert them through further ads is not worthwhile. Instead, it is recommended to concentrate on acquiring new customers (specifically those who are likely to buy).

5. Keep Testing and Learning

Please keep in mind that this is not the end of your testing. This is a minor step towards progress. You should keep doing these experiments until you’ve found what works for your company and can reproduce its success.

Media mix modeling will advise you where to put your marketing dollars based on what has worked best across all channels at every stage of your campaign’s lifecycle- consideration, awareness, purchase, and even loyalty building.

You can use this data to continuously optimize your campaigns to improve over time. This is especially true for eCommerce enterprises, which often have a more significant return on investment than traditional businesses.

Consider media mix modeling if you’re already running advertising and want to take your marketing plan to the next level.

Popular Media Mix Modeling Tools

Various tools and software solutions are available for media mix modeling, ranging from general-purpose statistical software to specialized marketing analytics platforms.

MMM tools commonly utilized include:

  • R: It is a free, open-source programming language and graphics software environment.
  • Python: A universal open-source programming language with numerous libraries and data analysis tools, such as pandas, NumPy, and SciPy.
  • SAS: An advanced analytics, data management, and predictive modeling software suite.
  • IBM SPSS: A commonly used statistical analysis software suite in social science and commercial research.
  • Microsoft Excel: Because of its simplicity and general use, Microsoft Excel is a popular tool for fundamental MMM analysis. At the same time, it could be better for more sophisticated models or big datasets.

When choosing a tool for MMM, it is critical to examine elements, such as the model’s complexity, the amount of the dataset, the statistical approaches required, and your expertise with the software.

Optimizing Your Marketing Mix:

Enhancing ROI through Fact-Based Analysis with New Path Digital

Marketing mix optimization through fact-based analysis can be achieved with MMM. Using statistical data eliminates guesswork, and ROI is enhanced through carefully allocated expenditures considering seasonal and channel-specific elements.

Accurate data in marketing allows for more creativity in content creation and more time to focus on delivering a memorable customer experience. By investing in marketing mix modeling, you can gain the confidence to make bold moves in your market and ultimately outperform your competition.

We at New Path Digital create marketing and advertising strategies focused on consumers and their decision-making process. Our expertise lies in crafting “path-to-purchase” models that constantly learn, adapt, and enhance the effectiveness of our client’s marketing and advertising investments. Contact us if you have any questions; we will be happy to help.

Blog Last Updated on 1 year by New Path Digital

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