Data Smoothing Methods for Marketing Mix Models
Explore advanced data smoothing techniques in Marketing Mix Modeling with MASS Analytics. Learn how to enhance your marketing insights for better decision-making.
The Metrics You Need to Know in Marketing Mix Modeling
Dive in this article to know the metrics that you need to know in Marketing Mix Modeling. Get on the right path for your ROI Optimization.
Data Splitting in Marketing Mix Modeling
Discover Data Splitting in Marketing Mix Modeling. Learn how MassTer’s processors refine MMM variables for precise insights.
Calendar Variables in Marketing Mix Modeling
Discover the Calendar Variables in Marketing Mix Modeling. Learn about dummy, window, interval, trend, and periodic variables’ significance for accurate insights.
How to Predict Business Outcomes with Marketing Mix Modeling
Previously in our series, we explored how optimization works in the context of Marketing Mix Modeling. In this article, learn how to predict business outcomes, like sales’ forecast and media performance, with your MMM model!
Your manager developed different media investment scenarios, and they want to know which one is best to implement.
Using the Marketing Mix Model equation that you have created, you can predict the outcome that will be generated by implementing each of those scenarios. You will also be capable of comparing the outcome across all of them and advise your manager about the best plan to follow to grow the brand and help the business. That’s what prediction is all about!
Optimization for Marketing Mix Modeling: What You Need to Know
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.
