This article will answer the following questions:

How Does Media Mix Modeling Work?

What is the The Role of Media Mix Modeling in Modern Marketing

What is the difference between Digital Attribution and Media Mix Modeling?

What are the limitations of marketing mix modeling?

How Media Mix Modeling Leads To Smarter Ad Spend Decisions?

What are Common Misconceptions About Media Mix Modeling? 

How to bring your marketing mix modeling into the 21st century

What is Media Mix Modeling?

Media Mix Modeling or Marketing Mix Modeling or in short: MMM is a statistical method to estimate the impact of various marketing tactics on sales in order to forecast and come up with a better marketing strategy to reach incremental lift.

The 3 stages of Media Mix Modeling:

1. The collection of any relevant factors that may have influence over sales
2. Analysis of the data, using all factors
3. Quantifying the weight for each factor to create a model to predict marketing results for future strategy

The method was developed in econometrics for the consumer packaged goods industry and has become common with brand cross platform Advertisers in the last years.

Marketers in sectors where B2C is a small part of sales usually do not have enough insights into consumer behavior, therefore, must rely on a more strategic approach to their ad spend to increase sales. Car manufacturers, Sport goods, consumer electronics, packaged goods, drinks and so on are just a few examples.

How Does Media Mix Modeling Work?

Media Mix Modeling allows you to answer a really interesting question: What will happen if you make this change ?

Media Mix Modeling captures the mix of your marketing influencers, those within your control and those that aren’t, to help you build a predictable marketing strategy, allowing you to test out your assumptions and validate those by making changes to the mix.

Creating a marketing mix model requires a strong bias towards your own products. An analyst would need to know details such as changes in your product pricing, planned changes, promotions, your expected budget, as well as the past performance of your sales data based and ad spend.

While there are some frameworks offering analysis tools to develop media mix models - media mix models require customization and personalization for each company based on their own product and plans.

What is the The Role of MMM in Modern Marketing?

Media Mix Modeling was almost forgotten about during the past twenty years. Digital Advertising made a bold promise to Advertisers: “Everything is trackable”. Marketing attribution platforms  made Advertisers think that their marketing activities were data driven, allowing them to lean back and watch as ad spend goes up while sales go up as well. This was true until two major things happened: the rise of attribution fraud , and the deprecation of the cookie and device identifier.

Media Mix Modeling has been around for over 50 years, with agencies consolidating reports about ad spend and tying those up with changes in the product pricing as well as conducting market research about competitor activities during an ad campaign to provide evidence that the marketing spend of an Advertiser was producing incremental results.

Media mix modeling offered a very strategic tool for agencies, allowing them to model “what will happen if we increase spend by x% ?”

While it was understood that media mix modeling is not a deterministic method of measurement - it has been used to make strategic decisions.

A smart marketer will not choose between attribution, incrementality measurement, and media mix modeling - but will utilize two or more methods to make strategic marketing decisions.

What is the difference between Digital Attribution and MMM?

There is a lot of confusion in the community between media mix modeling and attribution. The terms are often used incorrectly in conversions and even in professional articles. 

Marketing attribution is a term of tracking used to determine a deterministic or probabilistic link between ad inventories acquired (impressions and clicks) and the users who perform an action within the advertisers’ domain (website or app). Creating this link, allows advertisers to have a proxy indicating how many conversions “came” from certain inventories acquired. Attribution is far from deterministic. And even when attribution uses a deterministic parameter such as cookies or a device ID - marketing attribution is not a form of measurement.

Media Mix Modeling does not claim to be a deterministic measurement method. Media Mix modeling is a strategic marketing tool allowing marketers to make decisions about how much and how to allocate budgets in order to achieve certain marketing objectives.

What are the limitations of marketing mix modeling?

The limitations of a marketing mix model is that it requires a lot of historical data inputs including, but not limited to, data about your sales, ad spend data, any price changes you had and promotions. Information about your competitors products and marketing activities, as much as you can get. Seasonality data - factoring special holidays, days of week and certain external factors that could have influenced your marketing performance. 

Digesting all of this data and coming up with a media mix model will allow you to create a prediction of what will happen if you increase your ad spend over Facebook by 100% , or what will happen to Sales in New York, if you ran an out of home campaign during Christmas.

Marketing mix modeling requires marketers to execute marketing campaigns (tests) and validate their hypotheses by plugging actual sales data into the media mix model to refine their strategy with actual results. 

What are Common Misconceptions About Media Mix Modeling? 

Media mix modeling has been used recklessly in our industry, especially during the last few years. Following the deprecation of the IDFA the industry resorted to term-dropping, using media mix modeling as an alternative solution to attribution.

Media mix models are not a replacement for attribution. Media mix (or MMM) is a strategic marketing tool to allocate budgets wisely in order to reach certain marketing objectives.

Learning from History

Media Mix Models require historical data to have any helpful outputs. Often, the data needs to include external influencing factors such as competitors activity, product launches, financial events, weather and any major event that may have influenced the performance of a product (i.e. during an Olympics year, more people buy sport goods).

Due to these requirements - Media Mix Models work best for refining a strategy, expecting influencing factors such as changes in the media mix and/or external factors to help understand what would be the best media mix to market a product over time.

Media Mix does not work well for new product launches, as without historical data - there are simply too many unknown variables.

B2B2C brands (i.e. consumer goods, retail, consumer electronics) often must rely on Media Mix Modeling to analyze the effectiveness and impact of their paid marketing activities. 

Attempts to use a simple version of Media Mix Modeling are tested regularly by Advertising being turned off completely , allowing Advertisers to analyze sales activities with no Advertising in place - however, switching off all Advertising is extremely hurtful to most Advertisers as while doing so - competition may consume market share, thus, hurting long term brand equity for an Advertiser experimenting with turning off the lights across all marketing activities. 

INCRMNTAL provides an incrementality measurement platform, helping both brand and performance marketers unlock the value of their marketing spend. Our platform is not a replacement to media mix modeling, but works adjacently to it. 

If you want to learn more, visit INCRMNTAL or book a demo today!