In Joyabuy’s shopping agent business, handling after-sales disputes directly impacts customer satisfaction. By utilizing preadsheets to systematically collect and organize dispute data—such as dispute types (product quality, mismatched descriptions, shipping damages, etc.), customer demands, resolution processes, and outcomes—we can leverage data analytics tools to identify high-frequency causes and patterns.

1. Data-Driven Dispute Analysis

Joyabuy’s spreadsheets help categorize after-sales disputes, enabling the discovery of recurring issues. For example:

• Identifying suppliers with inconsistent product quality.
• Pinpointing logistics providers with higher damage rates.

This structured approach allows us to take targeted actions, such as adjusting partnerships or improving quality checks.

2. Building a Dispute Case Database

By documenting resolved disputes in spreadsheets, Joyabuy creates a reference library for future cases. New disputes can be matched with similar past cases, accelerating resolution and ensuring consistent customer service. Automated tagging and templates further enhance efficiency.

3. Optimizing Supplier and Logistics Evaluations

Spreadsheet metrics evaluate supply chain reliability:

• Frequent dispute-prone suppliers receive stricter audits or replacements.
• Low-performing logistics partners are replaced to minimize shipping damages.

4. Enhancing Efficiency and Brand Trust

Data-backed decisions reduce dispute rates while speeding up resolutions. Proactive measures—curated suppliers, reliable shipping, and AI-powered recommendations from the case database—improve customer satisfaction and solidify Joyabuy’s reputation.