How to Plan a Machine Learning Propensity Model

Authored by Ameex Technologies on 28 Mar 2022

We assume you already know about propensity model. We also assume you already know how much businesses depend on it. Yes, it's typical for organizations and their marketing departments to try to predict how certain categories of customers would behave in specific situations. However, the accuracy of outcomes predicting tools isn't always good. This is simply because they're built in a classic statistical manner. Propensity models should make use of machine learning technologies to help businesses realise the full potential of tailored marketing.

Bringing Propensity Modelling to Life Using Machine Learning

Wondering how to bring Propensity Modelling to life using Machine Learning? Here are the simple steps for it!

  • Map Out Your Strategy
  • Obtain Important Data
  • Get Data Ready for Propensity Modelling
  • Develop and Place Propensity Model to Test
  • Get a Propensity Model into Action

Map Out Your Strategy

Have you clearly defined your company’s objectives? Do your employees know it true and through? Defining your company objectives and choosing the insights you want to derive from them, in the end, is the first step towards constructing an efficient propensity model. Before the execution of any machine learning propensity model, the approach must be astutely mapped out. What will be the different milestones relating to this project? What are the deadlines that might impact your machine learning propensity model execution? You must have an answer to all of these questions.

Obtain Important Data

You should acquire important data from active and potential online visitors throughout this stage. The data source you use is determined by the propensity you wish to predict. Data from both first- and third-party sources can be integrated to avail more precise predictions.

The information of online visitors that you obtain from your company's websites and mobile applications are referred to as first-party data. The online visitor’s personal, demographic, and behavioral data are collected in this type of data.

Third-party data is the piece of information that a company can buy from vendors who do not have any direct relationships with the company's online visitors. Such data, which is generated and gathered by other parties, can provide a deeper understanding of audiences.

Get Data Ready for Propensity Modelling

The next stage is to double-check that the data for propensity modelling is consistent, accurate, and comprehensive. To accomplish this, you might take a variety of data preparation processes. Consider the following parameters to accurately estimate propensity scores and get a complete picture of who your consumers are in terms of age, gender, education and other factors. Also take into account the visitors’ browsing time, number of emails opened/clicked, purchases made, and services used in the past.

Develop and Place Propensity Model to Test

When the data is ready, you can start building and training propensity models with a variety of computational approaches. Logical regression, decision tree in machine learning, random forests, neural networks, and other forms of machine learning models are available to the experts.

Get a Propensity Model into Action

The final stage of propensity modelling entails applying the best-performing machine learning model or models you chose. Data scientists should, however, continue to update and improve the deployed models to guarantee that the accuracy of the results is maintained over time.

Creating and training a propensity model on your historical customer data is only the beginning. It will never be able to take the place of human being’s critical thinking. It's up to your team to figure out what to do with the data generated by the models and how to develop a business strategy. To acquire better insights and execute more focused and effective customization experiments, it is a good idea to combine propensity modelling with human knowledge.

We assisted a health brand with machine learning related goals and targets. Contact us now, if interested for your business.