How LuckyRev Utilizes MMM Results to Measure Success
The data science team at LuckyRev developed our own Media Mix Model - this statistical model represents the causal relationship between net sales & each paid media channel’s spend. We revisit this quarterly to inform decisions on where to invest ad budget.
The Question
How do we analyze MMM results and use them to complement/validate data points elsewhere (like MTA, platform, post-purchase survey results)? How do we make actionable inferences from this data?
LuckyRev’s Approach
There is no one-size-fits-all approach to data measurement in modern-day marketing. It is important to leverage different data points to understand the impact of your marketing channels.
For a 7-figure client, we look at their Triple Whale Total Impact data (which factors in 1st party survey data), channel platform data, LuckyMMM, and responses to the clients post-purchase survey.
In this example, we are trying to measure the impact of YouTube on new customer acquisition. Our MMM results back up other data points (MTA attribution and post-purchase survey results) that indicate that YouTube likely has a higher impact on new customer acquisition than the Google Ads platform is attributing.
These results allow our team to confidently increase budget to YouTube by 25% going into 2025.
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