The terms “Marketing Mix Modeling” and “Media Mix Modeling”, both called MMM or 3M, are often used interchangeably. However, they refer to two different concepts with different objectives and use cases. In a nutshell, Media Mix Modeling measures the effect of specific media channels with the aim of optimizing the media mix and reallocating the budget, whereas Marketing Mix Modeling measures the impact of all brand-related marketing activities and external factors on a specific target key performance indicator (KPI), often sales or ROI, to optimize the overall marketing strategy.
Are you still feeling confused? No worries! In this article, we will point out the differences between these two concepts, look at their similarities, and help you decide, which one of the two approaches - or maybe both - could be the right one for you.
In this article we will answer the following questions:
Marketing and Media Mix Models are types of statistical analysis involving multiple influencing factors, developed by data scientists to estimate the impact of numerous drivers on the target KPI. Both models are important tools for marketers that help measure the impact of their activities, reallocate budgets, and predict future performance.
The two approaches differ mainly in terms of the scope of influencing factors taken into account and the addressed target KPIs, i.e. the purpose of modeling. The time horizon and the level of detail also vary.
Media Mix Modeling uses data-driven advertising insights to measure the impact of individual media channels such as display, TV, radio, social media, OoH, and so on. The model shows the relation between the media performance such as the number of contacts, gross rating points, impressions or budget, and the brand development, e.g. ad or brand awareness, to generate clustered insights about what's working in a media campaign and what’s not. Media Mix Models analyze historical campaign data to predict future results. It allows marketers, especially media planners, to better understand how their media mix affects their marketing KPIs. This approach helps optimize the media mix, reallocate budgets between the different media channels, and predict the outcome of advertising campaigns.
Marketing Mix Modeling, on the other hand, has the purpose to figure out the effectiveness of each element, or ‘ingredient’, in a company’s marketing strategy. It evaluates the performance of all available marketing levers, not only the media - as media mix modeling does - but also all kinds of promotions, product launches, price changes, and other drivers. It's a more holistic approach that takes into account various external factors like weather, seasonality, or competitors' activity. By understanding how different elements interact with each other, marketers can allocate resources in a way that produces the maximum impact. Often the key target in marketing mix modeling is sales or ROI. You could learn more about Marketing Mix Modeling here.
When comparing the approaches, it becomes clear that Media Mix Modeling has a more specific scope - i.e. optimizing the overall media mix and advertising campaigns in particular, whereas Marketing Mix Modeling takes a broader range of influencing factors into account. Media Mix Models could deliver recommendations for immediate adjustments in ongoing advertising campaigns, whereas Marketing Mix Models can help optimize the overall marketing strategy. However, as Marketing Mix Models include media channels as influencing factors, it could be argued that marketing mix modeling is an expanded version of Media Mix Modeling. In the end, it depends on the target KPI that the marketers want to maximize.
To set up either a Media or Marketing Mix Model, historical data is needed. The accuracy of the results heavily depends on the data quality and completeness of the data inputs. If the data is inaccurate or incomplete, the output of the analysis might be distorted. The length of the data inputs is also important for the accuracy of the model. Depending on the granularity of the model and whether the data is daily, weekly, or monthly, an average of one to three years of data may be sufficient.
To be able to allocate budgets most efficiently and maximize the impact of advertising campaigns, the following data is needed for Media Mix Modeling:
To be able to identify trends and create insights about business performance, the following data is needed for Marketing Mix Modeling:
As more factors are included in Marketing Mix Modeling, more data sources have to be processed and analyzed, which makes the modeling more complex and the development more time-consuming. But in the long run, Marketing Mix Modeling could be used for forecasting the impact on different KPIs.
If you would like to learn how to set up Marketing Mix Modeling in more detail, you could read our article about it here.
A typical use case for Media Mix Modeling is a media agency that wants to know how to optimize its media plans towards a certain KPI. Let's assume we are a company selling ice cream. We have a brand new product “vegan ice cream” and brief our media agency to plan a campaign to increase brand awareness for this new product. As we already have experience with media campaigns for other ice cream products in the last 4 years, the media agency could use this data to predict the impact of the new campaign. It must be mentioned that accurate results can only be provided for the channels that have already been used by the brand in the past. For example, we have done Social Media, Display, TV, and Out of Home campaigns but no Radio. Therefore we are unable to forecast the uplift in brand awareness that could be achieved with radio spots. After processing the data and combining it with information from some external sources like weather data and competitor sales of vegan ice cream, we can proceed with media mix modeling to predict the optimal media mix and budget split between Social Media, Display, TV, and Out of Home. During the campaign, the media agency gathers intermediate results and can use the model to optimize the media mix and use the allocated budget most effectively. In our example, we see that we should invest mostly in the summer months and shift the budget toward social media. The insights of Media Mix Modeling could help media planners develop different scenarios taking into account the ad stock effect, competitors' advertising activities, or external factors like the weather.
Marketing Mix Models are often used by marketing departments within companies to optimize their overall marketing strategy and mix. If we were a company selling electric bikes, Marketing Mix Modeling could help us decide which marketing channels have the greatest impact on our sales revenue, taking into account that, for example, in summer, and for Christmas people are buying more bikes than during other seasons, or considering our new promotion with special prices or the actual inflation rate. Similar to Media Mix Modeling, we have to collect media data from our campaigns over at least the last 3 years as well as sales data, information about promotions, etc. Assuming we did a great job here, we could process the data and enrich it with external information. As mentioned in this example, seasonality, inflation, and competitors’ activities are the factors that may have a significant impact on sales. We could now use the marketing mix model to allocate our marketing budget toward the most promising marketing channels. In the long run, it will be possible to analyze which influencing factors have a high impact on sales. In our example, we get higher sales if we do a promotion in combination with a TV campaign right before Christmas.
As advertising usually comprises a tangible share of marketing activities, Media Mix Modeling could be an important part of a holistic Marketing Mix Modeling process aimed at optimized budget allocation and media mix. As Media Mix Modeling is less complex, companies could start with it as the initial step and in the long run include more drivers into the model for a more comprehensive view of marketing activities.
Both Media and Marketing Mix Modeling are powerful tools for measuring and optimizing the performance of marketing activities. With efficient Media Mix Modeling, marketers can gain valuable insights about their advertising campaigns and make informed decisions based on the available data. With Marketing Mix Modeling, marketers go one step further, taking multiple marketing and sales activities as well as external factors into account. The potential impact of both modeling approaches is huge, and they are must-have tools for any marketers who want to maximize the value of their marketing efforts. The decision on which approach to choose depends on many factors, including the company’s goals, the data available, the current expertise as well as the existing resources.