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Predictions Using Advanced Statistical Functions in Adobe Analytics | AEM Community Blog Seeding

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Predictions Using Advanced Statistical Functions in Adobe Analytics by Vishnu Pillai

Abstract

In this article, we would look at a very simple demonstration of how we can use the linear regression model to predict the estimated revenue based on the number of texts sent out in
Adobe Analytics and would compare the calculations using sklearn.

In this digital age, businesses are collecting huge amounts of data to gain insights about their customers, serve them with relevant content, and to optimize their business. One key part of optimization is to make the best use of the marketing spend on the most influential channels based on the impact each channel/touchpoint has on a conversion.
One way to align the marketing spend for different campaigns is to get their impact on conversion and try adjusting the spend for all channels such that the conversions are high. Businesses use multiple statistical approaches to attain this level of optimization. Adobe Analytics helps us with some out of the box metrics which makes the life easier for a marketer to use these statistical functions. Lets see how this works.

What are linear regression models?
Linear regression models are used to show or predict the relationship between two variables or factors. Linear regression is widely used in the industry today for making predictions of a value based on the change in input factors.

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Predictions Using Advanced Statistical Functions in Adobe Analytics

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