All of the code you must predict the probability of a buyer buying your product
Propensity fashions are a robust software of machine studying in advertising. These fashions use historic examples of buyer behaviour to make predictions about future behaviour. The predictions generated by the propensity mannequin are generally used to grasp the probability of a buyer buying a selected product or taking on a particular supply inside a given timeframe.
In essence, propensity fashions are examples of the machine studying approach often called classification. What makes propensity fashions distinctive is the issue assertion they clear up and the way the output must be crafted to be used in advertising.
The output of a propensity mannequin is a chance rating describing the expected probability of the specified buyer behaviour. This rating can be utilized to create buyer segments or rank clients for elevated personalisation and focusing on of recent merchandise or affords.
On this article, I’ll present an end-to-end sensible tutorial describing methods to construct a propensity mannequin prepared to be used by a advertising crew.
That is the primary in a sequence of hands-on Python tutorials I’ll be writing…
