Customer Loyalty

Machine Learning | Regression Models

Project Goals

The goal of the project was to boost our client’s growth by predicting customer loyalty. Based on historical sales data, we used machine learning to identify patterns for diverse users and identified features are the most impactful.


Solution Design

We pre-processed the data and trained multiple models such as XGBoost, Support Vector Machine, Random Forest, and Logistic Regression. Based on the metrics AUC-ROC and log loss, XGBoost was selected as the model that best identified the features that strengthen customer’s loyalty to the brand.


Outcomes

From a binary classification (loyal or not loyal), we switched into a multi-class approach that better reflects the customer’s degree of loyalty. Finally, we provided quantified potential gains of investment (7% to 14% increase) if managers follow proposed suggestions on the features we identified.


Next Steps

We would develop and deploy an end-to-end solution in a production environment. This includes an integration of the dataset and model outputs into a dashboard, generating faster insights targeting multiple personas.


We deliver actionable insights by empowering your organization with AI so you can focus on business growth.

We offer the leading edge end-to-end AI solutions, enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications. With our solutions, our partners can become leaders in their respective domains by becoming more innovative in serving their customers, collaborating among all key stakeholders in extracting business value and reducing costs.