Marketing Mix Modeling
Do you ever feel in the dark asking yourself where your conversions are really coming from? Determining the impact of every marketing dollar spent is crucial for multichannel performance measurement. Find an optimal mix of marketing efforts to increase sales at given costs or identify directions for business growth.
What is Marketing Mix Modeling?
Marketing Mix Modeling (MMM) is an AI-powered statistical method of predicting business outcomes where marketing metrics, sales and spend data are used as regression variables in a multivariate model.
MMM aims to address various pain points that every marketer faces:
How does every channel in my marketing mix contribute to the revenue?
What would be the optimal mix of spend distribution to drive the maximum ROI?
How do I adjust my marketing mix considering uncontrollable market factors such as COVID or seasonality?
The regression models delivered by MMM tool is used to evaluate impact of every single channel and predict sales that would happen under different inputs in the marketing mix.
Seeing the full picture is critical when it comes to marketing portfolio evaluation.
MMM is an ultimate solution for companies seeking to optimize their budget allocation.
The benefits of MMM
If your brand has been in the market for at least two or three years and there is time series data available on your marketing activities during that period, your marketing strategy is likely to benefit from MMM. The biggest advantage of the model is an ability to deliver strategic, high-level insights over granular and isolated data sources. However, while the models are capable of incorporating your entire media mix, this might not be an ideal tool to handle day-to-day optimization. If you are looking for ways to optimize ongoing activities, then a Multi touch attribution model (MTA) might be the right alternative choice.
The process:
Defining business questions to be answered by the model (how to maximize sales at a given budget / how would a 10% spend decrease in a certain channel affect sales / which channel can be scaled next / etc)
Scoping key metrics of marketing media mix that drive revenue
Aggregating spend and revenue data over a multi-year historical period
Identifying additional macro- and organic factors that might have effect on sales such as seasonality or market trends
Using the data to develop quantify the effect of each independent channel on sales through a regression analysis
Interpreting model outcomes and discussing recommendations
Establishing model updates