What is propensity modelling?
Propensity modelling is a statistical technique used to predict the likelihood of an event occurring. It is used in a variety of fields, such as marketing, insurance, and credit scoring.
Why use propensity modelling?
There are many reasons why you might want to use propensity modelling. Some of the most common reasons include:
To identify potential customers: By understanding who is most likely to buy your product or use your service, you can target your marketing efforts more effectively.
To assess risk: Propensity modelling can be used to assess the risk of an event occurring, such as defaulting on a loan. This information can be used to make decisions about whether to offer a loan, and at what interest rate.
To improve customer retention: By understanding which customers are most likely to leave, you can take steps to retain them. For example, you might offer them a loyalty discount or a free trial of a new product.
How does propensity modelling work?
There are many different ways to build a propensity model. The most common approach is to use logistic regression. This involves building a statistical model that predicts the probability of an event occurring (the “propensity”) based on a set of features or characteristics.
For example, suppose we want to build a model that predicts the likelihood of a customer buying a product. We would start by collecting data on past customers, including their demographics (age, gender, location, etc.), purchase history (what products they’ve bought and when), and other information (such as whether they’ve responded to marketing campaigns).
We would then use this data to train a logistic regression model. The model would learn to predict the probability of a purchase based on the customer’s characteristics. Finally, we would apply the model to new customers in order to predict their likelihood of buying the product.
What are the benefits of propensity modelling?
There are many benefits of using propensity modelling. Some of the most important benefits include:
Improved decision making: Propensity modelling can help you make better decisions by providing insights that would otherwise be unavailable. For example, you might use a model to decide which customers to target with a new marketing campaign.
Reduced costs: By making better decisions, you can reduce your costs and increase your profits. For example, if you use a model to target your marketing efforts more effectively, you will save money on advertising.
Improved customer satisfaction: By understanding your customers better, you can provide them with a better experience. For example, if you use a model to identify which customers are at risk of leaving, you can take steps to retain them. This will lead to happier customers and improved customer satisfaction.