In today’s expansive digital landscape, marketers have access to seemingly endless amounts of data - but are they using that data to its full extent? For many organizations - the answer is no.
Marketers must now shift their strategy to include methods and tools that allow them to draw insightful conclusions across vast amounts of data. Without the ability to make connections between marketing efforts on different channels, marketers can miss out on key marketing intelligence that can be used to drive future campaign success.
Media mix modeling (MMM), sometimes referred to as marketing mix modeling, is an analysis technique that allows marketers to measure the impact of their marketing and advertising campaigns to determine how various elements contribute to their goal, which is often to drive conversions. The insights derived from media mix modeling allow marketers to refine their campaigns based on a variety of factors, ranging from consumer trends to external influencers, to ultimately create an ideal campaign that will drive engagement and sales.
MMM uses aggregate data;As such, it is able to evaluate a wider range of channels, both traditional and digital. Additionally, MMM allows marketers to factor in external influencers such as seasonality, promotions, etc.
The statistical analysis performed by media mix modeling uses multi-linear regression to determine the relationship between the dependent variable, such as sales or engagements, and the independent variables, such as ad spend across channels. For example, MMM can use both linear and non-linear regression methods to determine how increased marketing spend on magazine ads affected overall sales. To get the most robust and accurate visibility into marketing impact, several models should be evaluated.
It is important for organizations leveraging MMM to be discerning when selecting which data they would like to measure and what they are able to measure. Data quality cannot be overlooked, so organizations will need to spend time aggregating and cleansing data from internal databases, third-party sources, or both. Media mix models often use two to three years’ worth of data that allow it to factor in items such as seasonality.
The outcome allows marketers to assign numerical value to the impact of campaigns across various media channels toward achieving their ultimate goal – engagement, conversion, etc. The collection of these insights allows marketers to determine the ROI of their efforts, allocate future spend, and create sales forecasts.
Across industries, an organization’s MMM ratio is comprised of three essential components:
A good way to understand what media mix modeling measures is to understand why it was created.
MMM came into popular use in the 1960-70s when the marketing landscape was more simplified than it is today. Kraft was an early user of this type of analysis. As they launched Jell-O, they were able to choose between three or four television networks and magazine advertising to promote the new product.
The approach of traditional MMM allowed them to see if they advertised at different levels, in different parts of the country, at different times of the year - how would sales be affected in those regions. For example, they could advertise Jell-O in ten cities over ten weeks to see if sales increased.
This is MMM in its simplest form, allowing marketers to get high-level insights into campaign effectiveness. Today as fragmentation has exploded in all of the ways we consume media, MMM data is more often compared to insights from more flexible, granular models.
Both media mix modeling and data-driven marketing attribution models, such as multi-touch attribution, are used to determine the impact of marketing tactics on a business objective. However, it is important to remember that MMM does not examine user-level engagements, such as impressions, clicks, etc. This need for person-level data is why data-driven attribution has become pervasive in marketing. Media mix modeling exclusively measures the impact marketing efforts have on meeting objectives, without factoring in the customer journey.
Data-driven attribution refers to various attribution models that track engagements throughout the customer journey. These insights allow marketers to understand which tactics have the greatest impact as consumers move down the sales funnel. Marketing attribution models typically evaluate performance after a few months at the conclusion of a campaign.
As previously mentioned, MMM provides high-level insights into specific marketing tactics, over a longer period of time. This allows marketers to understand trends such as seasonality, weather, holidays, brand equity, etc. MMM typically analyzes two to three years’ worth of historical data to identify patterns in campaign effectiveness.
Each of these models have uses in modern marketing, but they also both have blind spots. Data-driven attribution models can have limited visibility into offline conversions, and largely focus on digital marketing platforms, where MMM can measure both. However, the lack of person-level insight offered by MMM makes it less well suited for customizing campaigns to specific consumer desires.
Now, producing ads that do not have an individual in mind can not only reduce marketing ROI, but hurt brand perception in the eyes of the consumer. As a result, the aggregate insights that MMM provides, which do not delve to the consumer level, do not help marketers to customize messaging to meet consumer demands.
While some believe that Media Mix Modeling is broken, it still has a place in modern marketing, especially when used alongside more consumer-centric models. MMM is still a simple way to get high-level answers. This analysis can be done infrequently to keep the organizations aware of broad trends and patterns that have occurred over many years.
However, MMM should not be the primary approach to managing improvements in your marketing plan, as it is not the best tool to understand how different types of people and messages drive returns. As a result, marketers should not spend a lot on MMM, and should conduct this analysis once or twice a year. This will provide a historic, high-level diagnostic view on marketing contribution and outside factors interacting with marketing over a long period of time.
For media mix modeling to be effective today, it must be aggregated with additional marketing measurements in order to provide a unified marketing measurement. This will give marketers insight into both historical data and person-level engagements with various touchpoints, allowing them to see a more holistic view of the success of their marketing efforts.
To do this, marketing teams should leverage a marketing performance solution that distills big data into actionable insights, for dynamic, in-campaign optimizations. A unified measurement platform that allows marketers to leverage MMM data alongside analysis from other models should incorporate the following features: