Creating a marketing mix model for a better marketing budget analytics corner data recovery mac free

In an earlier post on time series, I explained how a time series data set has to be examined to see how the data can best be incorporated within an advanced analytics model. There are a number of advanced models, but probably the one most being rediscovered now is the marketing mix model, a regression analysis of several marketing channels, assigning sales or market share as dependent variables.

Marketing mix modeling has gained importance because of the rise in the number of media channels that can trigger customer activity. When I launched my consultancy in 2009, analytics was designed to examine website activity from a home computer. Today that activity can come from almost anywhere. A mobile device or networked consumer goods like cars or refrigerators can be a starting or intermediary point for a customer to make a purchase or research a brand online.


The attribution activity can now be represented as data, so a marketing mix model aims to blend that data into a regression so that marketers can better estimate which activity on a set of channels drive sales.

As I mentioned in the time series post, libraries carry the functions needed for the model. In this case, the libraries that can help model are ones that address wrangling and regression creation. For a regression you can use the base R functions cor() for the correlation, sum() to summarize results, and lm() for the regression on your data. You may want a visualization library like ggplot2 to provide more visualization options .

Wrangling is just another term for organizing data. A number of data types can go into a marketing mix model, much of which represent retail activity, such as an organization’s events and sponsorships, new client accounts, sales by product line, economic data representing macroeconomic forces for each market, and competitor advertising spend. You can pick according to what you are interested in modeling, but there should be uniformity in data type where possible.

The sales activity in the equation explains how a one unit increase in a given channel would increase the sales (or market share) by a given coefficient (B1 , B2 , B3 , etc). You can then evaluate the calculated sales against your budget and real world constraints in mind, creating a contribution table that compares the sales lift from a given channel when the other channels are held constant. That comparison quantifies the effects of different advertising mediums.

There are some limitations to this analysis, however. Marketing mix models emphasize immediate channel responses to advertising media, overlooking long-term brand recognition as a sales factor. This limitation may once have seemed philosophical, but as brand image becomes linked to digital channels, especially on social media, brands are seing their brand equity – and sales – rapidly impacted by real-life events, as discussed here.

Nevertheless, the marketing mix model does provide solid answers for high-level attribution of marketing channels to a business model. With better data and improved analysis models from data science languages like R and Python, marketers can use the model to dial in their marketing budget to the right channels, and improve revenue outcomes.

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