All the code you need to predict the likelihood of a customer purchasing your product
Propensity models are a powerful application of machine learning in marketing. These models use historical examples of customer behaviour to make predictions about future behaviour. The predictions generated by the propensity model are commonly used to understand the likelihood of a customer purchasing a particular product or taking up a specific offer within a given time frame.
In essence, propensity models are examples of the machine learning technique known as classification. What makes propensity models unique is the problem statement they solve and how the output needs to be crafted for use in marketing.
The output of a propensity model is a probability score describing the predicted likelihood of the desired customer behaviour. This score can be used to create customer segments or rank customers for increased personalisation and targeting of new products or offers.
In this article, I’ll provide an end-to-end practical tutorial describing how to build a propensity model ready for use by a marketing team.
This is the first in a series of hands-on Python tutorials I’ll be writing…
