Marketing mix models (MMM) rely on statistical techniques, such as multiple linear regression, to analyze historical time series data and identify causal relationships between a company’s marketing mix (marketing strategies and tools chosen with the objective of leveraging the and business results sales, registrations, etc. The main objective of the is to estimate the marginal effect of the variables that make up the marketing mix, thus. The forecast is a secondary result of the elaboration of these models. The advantages of MMM over other techniques with similar objectives Privacy-friendly. Offline resilience At least to find out how many of the promises made by these same tools are actually fulfilled.
The answer to this question will
Thus. a question as crucial as the implementation of a marketing mix model is: what is the best tool to be used. The answer to this question Will be related not only. to the available tools. But also to the specificities of each company Chinese Europe B2C Cell Phone Number List knowledge of the technical team, time to implement. In this post we will talk about three free tools, provided by some of the main technology companies in the market. At the end of the reading . And that the questions to be asked when choosing one over the others are clearer is the MMM library Written in Python and authored by Google.
The technical details
It is an open source library available on GitHub and easy to install. The equation governing the model is in the form In this model, it is possible to estimate, separately . The effects of the baseline ( alpha ), trend, seasonality Different media channels, and other external factors relevant to the business. Macroeconomic data, holidays, brand EK Leads studies, competitors, etc.. . At the end of this postuses Bayesian statistics in model building. Basically, Bayesian models allow introducing into the model any initial knowledge about the probability that something will happen.
