Recast vs Lightweight MMM

People often ask us about the differences between Recast’s next-generation MMM platform and Google’s open source MMM tool, Lightweight MMM.

In this blog post we do a compare-and-contrast between these tools – we’re obviously biased but will do our best to lay out the pros and cons of each tool as we see them.

There are two types of things to consider when evaluating an MMM solutions:

  • Operational considerations: how easy or difficult is it to validate and operationalize the results of the MMM?
  • Modeling considerations: what assumptions is the model making about how marketing works in the real world?

Both considerations are very important but feel free to jump around in this blog post depending on which type of consideration you’re most interested in.

Operationalizing MMM

Building a great MMM model is only part of the battle. What use is it to spend hundreds of hours of data scientist time building an MMM model if marketers aren’t able to actually take action off of the results?

The most critical part of a successful MMM implementation is getting marketers to actually use the results to drive improved performance in marketing efficiency.

Unfortunately, most MMM tools are designed for data scientists and not marketers and so an MMM implementation falls flat when it comes time to actually take action. Below, we discuss some of the differences between Recast and Lightweight MMM and how Recast focuses on making the results actually actionable.


Validation is critically important to a successful MMM project. Marketers need to trust the results of the model and know that when they are following the MMM recommendations that it will lead to improved performance for the business. 

At Recast, we believe that the best practice for validating an MMM model is through continuous back-testing. That is, we use a model run through a certain date (say, June 1st) to make predictions about data that the model hasn’t seen yet (say, from June 2nd to August 31). If the MMM has correctly estimated the true incrementality of the different marketing channels, then the MMM should be able to make accurate predictions about data it hasn’t seen yet.

While it is possible to validate the Lightweight MMM model via out of sample prediction, you need to build and operate the validation platform yourself instead of relying on Recast’s built-in validation tooling.

Data Science Resources

Lightweight MMM is developed as a python package and requires someone on your team who knows how to program python in order to operate it. Additionally, Lightweight MMM’s outputs are not marketer-friendly so you will need to invest substantial resources in taking the output from Lightweight MMM and turning that into reports that marketers are familiar with.

Recast is a full-service MMM platform designed for marketers. Our data science team is in charge of the initial model build and ongoing monitoring of data quality and model performance without needing any of your internal data science resources.

Tools for Marketers

Lightweight MMM generates detailed output for data scientists, but does not provide tools for budgeting, optimization, and planning for marketers. 

Marketers have specific requirements around how they want to use an MMM to build budgets, do forecasting, and do reporting and often building those interactive tools are beyond what a data science team wants to maintain.

Recast provides a best-in-class fully integrated reporting and planning tool that not only exposes the results of the MMM but also allows marketers to build budgets, run their own forecasts and track pacing against goals. Additionally, Recast is continuously updating and improving the tool based on feedback from customers and continuously rolling out those improvements to all users.


In order to be useful, MMM models must calculate incrementality of marketing channels. In order to actually be able to estimate true incrementality the model’s assumptions must match the reality of how marketing actually works as closely as possible. Of course, no model can capture all of the detail of the real world, but when you’re evaluating tools you should think hard about the assumptions the model is making and how that may bias results.

Time Series vs Static Performance

Lightweight MMM makes the assumption that channel performance is static which is to say that if a channel like Facebook has an ROI of 3.5x today it means that it has always had and always will have an ROI of 3.5x.

The problem is that no marketer believes that could possibly be true. Changes like the release of iOS14.5 and ATT can (and did!) change the performance of Facebook marketing. Similarly, seasonality can impact the performance of Facebook’s spend. So can changes in creative or marketing strategy. Recast is a time-series model which means that Recast estimates the incremental ROI for every channel for every day. 

As channel performance changes, Recast shows you how the ROI is evolving over time with creative and intrinsic channel changes. 


Lightweight MMM makes the assumption that seasonality is totally independent of paid media performance. This means that for businesses that are highly seasonal (maybe they have a peak sales period around Christmas), Lightweight MMM will assume that all of the increase in sales around that time is due to seasonality, and not paid media performance. The result of this is that Lightweight MMM will under-credit paid media spend during peak seasons and over-credit media spend during off-seasons.

Recast assumes that seasonality impacts both the organic part of the business as well as media and marketing effectiveness. If Facebook spend tends to be more performant in the summer than the winter, Recast will capture that underlying pattern and help you understand how you should optimize your marketing budget over time to get the best returns.

Promotional Activities

Lightweight MMM assumes that promotional activities are totally independent of marketing spend. So if you run a 20% off sale for Memorial day and have a spike in sales, Lightweight MMM will attribute all of that spike to the Memorial day promotion and none of it to your marketing spend leading up the promotion. Additionally, Lightweight MMM will assume that all of the sales during that “spike” of sales are purely incremental instead of considering the case where the promotion is pulling-forward or pulling-backward sales that you might have gotten anyway.

Recast actually estimates both the pull-forward and pull-backward effects of promotions (leading to the post-promotion hangover many are familiar with) as well as how those promotional events interact with marketing spend. Recast can recommend how you should scale your marketing spend leading into a promotion in order to maximize returns.

Demand Capture Channels

Certain channels like “branded search” do not work the same way as other marketing channels. Most brands cannot choose to simply scale up their branded search spend but instead are reliant on first generating more branded searches that they can then capture. Additionally, branded search activity tends to be highly correlated with sales so it’s easy for less sophisticated models to over-credit branded search returns.

Lightweight MMM treats branded search just like every other marketing channel, and is subject to over-crediting branded search and making nonsensical recommendations about how to scale into that channel.

Recast treats branded search as an intermediate outcome which is to say that Recast simultaneously models how other marketing activity drives branded search as well as how branded search subsequently drives incremental revenue. This avoids over-crediting branded search spend and yields much more realistic recommendations.

Incorporating Experimental / Lift Test Results

Lift tests and experiments are critically important for calibrating and improving the accuracy of results from an MMM model. However, there are two important features of lift tests that are important to keep in mind when including them in an MMM model:

  1. Lift test results are a snapshot in time. If you run a lift test from January 4 to January 28 then those lift test results apply to that period of time. By the time June rolls around, it’s unclear to what extent those lift test results should still influence our understanding of channel performance.
  2. Lift test results include uncertainty. Depending on the size of the experiment and how well powered it is, we might have smaller or larger confidence bounds around our estimate of incrementality. A good lift test will have results that look something like “we estimated the channel to have an incremental ROI of 2.4x with a standard error of 0.4”. Which means that really incremental ROI is estimated to be somewhere between 1.6x and 3.2x (95% confidence interval).

Correctly incorporating lift test results into an MMM means incorporating both the time-bounded nature of the test as well as the uncertainty into the modeling framework. 

Since Lightweight MMM does not allow marketing performance to change over time, Lightweight MMM is not able to correctly handle the case where lift tests show that channel performance has changed over time. A lift test from 9 months ago impacts the results just as much as a lift test last week.

However, Lightweight MMM is able to incorporate uncertainty in lift test results via the prior-setting functionality.

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