A predictive model that connects upstream data quality conditions to downstream chargeback outcomes — proving that chargebacks aren't random, they're the scheduled consequence of specific, fixable data deficiencies. Quantifies the preventable portion and produces a capital-allocation roadmap ranking root causes by prevention value.
Takes a brand's chargeback history, product data, and EDI records and:
- Harmonizes opaque, retailer-specific chargeback reason codes into uniform root-cause archetypes across retailers
- Reconstructs data-quality state at shipment time (not today's state) to correctly attribute chargebacks to their upstream causes
- Trains an interpretable model scoring chargeback probability per shipment, with SHAP-style attribution so every risk score names the specific data condition driving it
- Scores upcoming purchase orders to flag high-exposure shipments before they leave the dock
- Produces a ranked remediation roadmap: root causes ordered by prevention value, with dollar estimates
TBD — stack and entry point to be settled in planning.
TBD — see PLAN.md and DECISIONS.md for current direction.
chargeback-prediction-model/
├── src/ # Source code
├── tests/ # Tests
├── CLAUDE.md # Project context for Claude Code
├── PLAN.md # Current work arc
├── HANDOFF.md # Session-to-session continuity
├── DECISIONS.md # Durable architectural choices
├── FAILURES.md # What didn't work and why
└── portfolio_project_brief_chargeback_prediction.md # Project brief
Part of the Lailara LLC analytics portfolio. Bridges the Product Data Health Audit (finds the data problems) and Retailer Deduction Recovery (disputes chargebacks after arrival) by proving the causal link and quantifying the prevention opportunity.
Built by Lailara LLC — data hygiene and analytics consulting for specialty food brands scaling into national retail.