ShopSphere AICV tracks how a single platform algorithm update rippled into a sharp rise in cancellations — and the TabTransformer model built to catch it early.
AICV compares the historical churn baseline against churn observed after the ShopSphere algorithm update, isolating the size of the shock.
The post-update churn rate increased substantially compared with the historical baseline, highlighting the business impact associated with the platform algorithm change.
Longitudinal synthetic customer data created to model ShopSphere customer behavior before and after the algorithm update.
A custom TabTransformer-style neural network was trained to classify customer churn using behavioral, engagement, visibility, pricing and satisfaction features.
A lightweight, client-side approximation of how the trained model weighs these features, running entirely in your browser. It mirrors the model's decision threshold but isn't a live call to the actual TabTransformer weights.
Calibrated so typical low-engagement, high-friction profiles land near the post-update churn rate, and healthy profiles land near the historical baseline.
The model supports retention prioritization by balancing the cost of unnecessary interventions against the potential value lost when a churner is missed.
Customers with elevated predicted churn probability can be prioritized for retention interventions. The 0.29 decision threshold allows the business to identify a broader group of potential churners while considering intervention costs.
Sorted by risk. Flag status updates live as you move the threshold above — click any column to re-sort.
| Customer | Engagement | Satisfaction | Visibility Δ | Price Δ | Churn risk | Status |
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This project uses a synthetic dataset created for academic demonstration. The observed relationship between the algorithm update and churn should therefore not be interpreted as causal evidence from a real commercial platform.
Model performance should be evaluated further on real-world customer data before deployment in an operational retention system.