FAQ    Marketing Measurement     What is Marketing/Media Mix (MMM) Modeling with Examples?

What is Marketing/Media Mix (MMM) Modeling with Examples?

Marketing Mix Modeling (MMM) in marketing is statistical analysis using multivariate regressions on conversions/sales with various marketing tactics and spend as variables to predict sales. The regression provides contributions of each variable which are then used to predict conversions and sales with different inputs or marketing mix.

How does marketing mix modeling work?

Specifically, Marketing Mix Modeling (MMM) (sometimes also called Media Mix Modeling) collects aggregated data across marketing and non-marketing factors over a multi-year historical period. Additionally, MMM models factor in external influences such as seasonality, economic data, weather and promotions. That data is used to develop a demand model which quantifies the historical contribution of each marketing and non-marketing input to a business outcome.

Marketing Mix Modeling Example

A clothing brand wants to know how each of the media channels contribute to sales. Over time, the brand has collected sales data and media spend for each channel for the same time frames. Based on many different points in time, the brand can run a multivariate test. The analysis shows for a change in media spend what expected sales will be. Because it’s based on historical data, the brand is only getting correlation, and not necessarily causation.

What are Limitations of Marketing Mix Modeling

MMM typically estimates marketing impact on historical business outcomes at the channel level probabilistically, and can be subject to the correlation vs. causation dilemma. For forward-looking projections MMM relies on a number of assumptions for non-marketing factors as well the assumption that channel level media mix, cost, and response does not diverge with the historic data that is the basis for the demand model.

While well built models based on high-quality data can overcome the correlation vs. causation dilemma to provide channel lift and forecasts, the limitation on degrees of freedom and challenges with overspecified models means that they cannot be used to inform tactical decision making at the sub-channel level. Because models rely on multiple years of historical data to determine an average read for marketing inputs, they are challenged in teasing out dynamic changes to marketing channels and/or business changes in recent periods.

What are the Advantages of Marketing Mix Modeling?

The main advantage is that you can use 2 to 3 years of historical data that is most likely readily available if you’re an established brand. MMM also provides a high-level analysis across the entire media portfolio delivering strategic long-term planning insights into your non-addressable and addressable media. But again, you are only seeing correlations and not causation. The other advantage of MMM is its ability to model non-media exogenous variables such as macro-economy influences (like COVID-19), competitive influences, seasonality, promotions and other trends. An alternative, and in some cases better, approach to understand each marketing mix contribution is to run incrementality testing.

Here are some pros and cons of Incrementality Testing vs MMM vs MTA. With Measured, you can easily run incrementality measurement and testing on 70+ media publisher platform. Utilizing our API integrations with media publisher platforms, you get a cross-channel view of your marketing mix in less than 24 hours. Learn more and ask for a demo here.

Is MMM a Fit for me?


Compare Measured vs. Other Multi-Touch Attribution (MTA) & Media Mix Modeling (MMM) Platforms

Measured

Measurement – Other

Measured Advantage

Incrementality

Platforms

MTA

MMM

General

Neutral & Independent

Trusted Measurement

Measurement

Causal Incremental Contribution

Productized Experiments

Scale Testing

Identify Saturation Curves

Granular Insights

Future Proof

Comprehensive & Cross Channel

Depth of Measurement

Walled Garden Support

Comprehensive

Transparent

Transparency = Trust

Decisions

Tactical Decisions

Daily & Weekly Insights

Strategic Planning

Bottom Up Forecasting

Timely Insights

On Time, Reliable

Data Management

Purpose Built for Marketing Analytics

Analytics Ready

Data Quality

Reconciled to Source of Truth Platforms

Author

Nick Stoltz - COO

Expert in cross-channel measurement strategy and adoption.

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.

 

What is cross-platform attribution (or cross-channel attribution) and why is it difficult?

The goal of cross-platform attribution in marketing is to gain clarity on the interplay and contribution of influence that each channel/tactic/campaign has on driving conversions over and above baseline sales.

It’s a task that has proved to be very difficult for many reasons including but not limited to:

  • Walled gardens are typically inaccessible to third-party tracking of impressions
  • Identity resolution across media platforms is quite low
  • Cross-device tracking is difficult and match rates are extremely low
  • Instrumenting a tracking infrastructure by a third party measurement provider has proved to be fraught with breakage and data leakage
  • It is extremely time consuming to implement without the help of a partner

Video: Landing a source of truth cross-channel media reporting dashboard

 

 

What are some cross-channel attribution tools?

MTA – collects individual, or user-level data, for trackable addressable media and conversion events in order to determine the impact of each media event to the desired conversion at the customer level. By summing the impact of each addressable media touchpoint on each customers’ likelihood to convert, MTA quantifies the total media channel lift provided by addressable media. MTA does not account for the impact of non-addressable media, and furthermore much addressable media is either non-trackable or lost due to the innumerable challenges of tracking data at the user level.

Incrementality Measurement – Incrementality in marketing refers to the incremental benefit produced per unit of input stimulation. Incrementality is the lift in desired outcome (awareness, web visits, conversion, subscriptions, revenue, profitability) provided by marketing activity.

Incrementality in marketing is especially needed for channels where ad impressions such as display, Facebook, social, or even TV are hard to measure. To measure incrementality, the audience is broken out into test groups (exposed to the ads) and a control group (suppressed from seeing the ads).

MMM – MMM is a top down (aggregate marketing data) and very artistic statistical exercise where one or more models (e.g. econometric, multi-linear regression) are leveraged to extract key information and insights by deriving information from multiple sources of marketing, economic, weather and financial data. MMM is also a high-touch consultative approach that is very manual with little to no automated data inputs, whereas MTA and Incrementality, when deployed properly, is a very automated approach leveraging preconfigured connectors that extract the required marketing data, across many channels, on regular cadence. (It’s important to note that MTA can take 6 months or more to deploy, whereas Incrementality can be up and running with reporting in 4-6 weeks.) See this article for more on why always-on automated experimentation is the future of marketing measurement.

Author

Trevor Testwuide - CEO

Expert in business strategy and marketing measurement.

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.