After creating your Marketing Mix Model, you need to provide the business with actionable insights they can use to optimize the performance of their brands. This is where Optimization comes in, as part of the deployment phase of the MMM workflow.
If you’re not confident yet with your model performance, check the previous article, from this series, on the modeling phase. Optimization can only give meaningful results if the previous steps were properly done.
Previously in the series, we explained the powerful uses of log-linear modeling. In this article, we learn about nested modeling, the advanced technique used to disentangle the impact of interactions between marketing channels.
When doing MMMs, you would ideally have each marketing variable influence sales separately: Search activity influences sales. TV influences sales etc. This assumes that there is no interaction between search and television. In real life, however, this is not the case.
Imagine you’re the CEO of an ice cream company.
You have the best-selling ice cream in the nation. Very tasty stuff!
One day, you notice your sales numbers dropping. You look at the possible suspects but you’re not seeing an obvious reason for the decrease.
You look for a way to understand exactly what’s driving your sales. Being the savvy CEO that you are, you commission a Marketing Mix Modeling project to get to the bottom of it.
Your marketing analytics team gets to work and comes up with an initial model about the sales of your beloved ice cream. It looks like this:
Two of the key concepts in Marketing Mix Modeling is the relationship between ROI and Contribution.
Because of diminishing returns, as the contribution increases, and the more the business spends on a channel, the less marginal contribution it will generate. This translates into a decreasing ROI.
Because of this dilemma, the MMM analyst must ensure that they achieve consistent and sustainable contribution while keeping an eye on the level of ROI.
