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 method was developed in econometrics for the consumer packaged goods industry and has become common with brand cross platform advertisers in the last years.
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 sporting goods).
Due to these requirements - MMM 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.
MMM does not work well for new product launches, as without historical data - there are simply too many unknown variables.
The granularity of insights MMM can provide does not make it an operational tool for a marketer. Since most marketers conduct multiple optimization tasks per week, while MMM would require a marketer to keep ad spend at certain level for the purpose of validating the MMM predictions.
MMM also requires substantial incrementality testing for calibration.