Propensity fashions are a strong utility of machine studying in advertising and marketing. 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 specific product or taking over a selected provide inside a given timeframe.
In essence, propensity fashions are examples of the machine studying approach generally known as 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 and marketing.
The output of a propensity mannequin is a likelihood rating describing the expected probability of the specified buyer behaviour. This rating can be utilized to create buyer segments or rank prospects for elevated personalisation and concentrating on of latest merchandise or gives.
On this article, I’ll present an end-to-end sensible tutorial describing easy methods to construct a propensity mannequin prepared to be used by a advertising and marketing staff.
That is the primary in a sequence of hands-on Python tutorials I’ll be writing…