Marketing 0

Regression Modeling for Simple Predictions
Regression modeling is the most fundamental yet useful tool for
predictive analytics. The model assesses the relationship between
independent variables (or explanatory data) and dependent variables
(or response data). Dependent variables are the results or outcomes
that marketers are trying to achieve, such as click and sales data. On
the other hand, independent variables are the data that influence the
results, such as campaign timing, advertising copy, or customer
demographics.
In regression analysis, marketers look for statistical equations that
explain the relationship between the dependent and independent
variables. In other words, marketers are trying to understand which
marketing actions have the most significant impact and drive the
best outcomes for the business.
The relative simplicity of regression compared to other modeling
techniques makes it the most popular. Regression analysis can be
used for many predictive marketing applications, such as building
the customer equity model, propensity model, churn detection
model, and product affinity model.
In general, regression modeling is carried out in several steps.
1. Gather the data for dependent and independent
variables.
For regression analysis, datasets for both dependent and
independent variables must be collected in unison and with
sufficient sampling. For instance, marketers can investigate the
impact of the digital banner color on the clickthrough rates by

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collecting a substantial enough sample of color and the
resulting click data.
2. Find the equation that explains the relationship