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:
Discover the Difference between Frequentist and Bayesian Regression in Marketing Mix Modeling. Learn how they impact your marketing strategies
Read this article to know how you can get Insights through Pooled Regression in Marketing Mix Modeling with MASS Analytics.
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.
In the previous part of this MMM Guide series, we explored one of the corner stones of Marketing Mix Modeling, which is Regression Analysis. In this article we cover some of the ways in which you can ensure the robustness of your model and reliability of your results.
