Build an analytics application that helps online sellers identify why products are returned and which operational changes might reduce preventable returns.
Allow users to import order records, product identifiers, return reasons, delivery information, product descriptions, and refund outcomes.
Create:
A return-rate breakdown by product, category, supplier, and fulfillment method when data permits.
A classifier that groups inconsistent return descriptions into editable categories.
A comparison between advertised product characteristics and customer-reported expectations.
A detector for unusually high return rates, accounting for sample size.
A financial impact calculator for refunds, shipping, handling, and recoverable inventory.
A root-cause investigation board linking each hypothesis to supporting records.
A what-if simulator for changes to product descriptions, packaging, quality checks, or fulfillment.
A prioritized action plan with measurable validation criteria.
Distinguish correlation from causation and avoid treating small samples as conclusive. Preserve original return comments, show evidence behind each finding, and do not fabricate missing costs or outcomes.
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