Customer Prediction Models for an eBook Store
Built regression and classification models on 16,519 customer records to predict monthly spend and subscription likelihood for an eBook store.
Context
Framed as a customer analytics case study for an eBook store, focused on helping non-technical stakeholders understand who is most likely to subscribe and spend more.
Key insight
The strongest value comes from turning model output into targeting decisions, not just reporting accuracy scores in isolation.
Business impact
Built customer spend and subscription models using PyCaret and gradient boosting so marketing and sales teams could prioritize higher-propensity customer segments with more confidence.
Problem
The business needed a clearer way to identify customers likely to subscribe and estimate monthly value before spending budget on broad outreach.
Methods
Prepared customer features, compared multiple regression and classification approaches, and selected models that balanced predictive performance with stakeholder interpretability.
Findings
The final workflow highlighted which customers were more likely to subscribe and spend more, giving the team a more targeted lens for campaign planning and customer prioritization.
- Python
- PyCaret
- LightGBM
- Gradient Boosting
- Feature Engineering