Market Mix Modelling (MMM) is an analytical approach that turns marketing and sales data into a quantity that can measure the impact of the marketing channels on the sales volume. This technique and empirical relation’s relationship, which can either be linear or non-linear is derived through regression between sales and marketing spends on each channel.

Using simulations, where the cost of each marketing channel is varied, multiple scenarios are generated and depending on the results; an effective marketing strategy is derived. Since multilinear regression is used, the equation can be given as follows:

Sales= β_0 + β_1*(Channel 1) + β_2*(Channel 2)

Where Sales represents Sales Volume, Channel 1 and Channel 2 are different marketing channels, β_0 represents the Base Sales, which is the sales volume, in the absence of any marketing campaigns, because of natural demand, brand loyalty, and awareness. On the other hand, β_1 and β_2 are the coefficients for Channel 1 and Channel 2, representing the contribution of each channel to the sales volume.

Our dataset will be an advertising dataset available on Kaggle. To illustrate how Multiple Linear Regression (MLR) is applied to this dataset, I will be focusing only on the implementation part of the Market Mix Modelling (MMM).

#multiple-linearregression #analytics #retail-marketing #data analytic