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Analysis and Optimization of Ponybuy's Purchasing Categories Using Spreadsheets

2025-04-22

As an e-commerce platform specializing in overseas purchasing services, Ponybuy relies on data-driven decisions to refine its product catalog. This article explores how to analyze Ponybuy's purchasing category data—such as sales share, growth rate, and profitability—using spreadsheets, and how to derive actionable optimization strategies.

1. Data Collection and Spreadsheet Analysis

To begin, we structure key metrics into a spreadsheet (e.g., Google Sheets or Microsoft Excel), including:

  • Category Sales Share:
  • Growth Rate (YoY/MoM):
  • Profit Contribution:
  • Inventory Turnover:

Example Table Structure:

Category Sales Share (%) Growth Rate (MoM) Profit Margin (%)
Skincare 35% +12% 45%
Electronics 20% -5% 15%

3. Category Optimization Strategy

Phase Out Low-Performance Categories:

  • Discontinue electronics with sub-10% profit margins unless they serve as customer acquisition tools.
  • Reduce inventory for slow-growing categories (e.g., traditional supplements).

Introduce High-Potential Categories:

  • Add niche subcategories (e.g., K-beauty "clean" skincare based on search trends).
  • Partner with trending brands in "DTC wellness" (e.g., menstrual care tech).

Dynamic Rebalancing:=IF(Growth_Rate<5%,"Review","Keep")) to flag categories needing quarterly reassessment.

4. Implementation and Monitoring

Create a dashboard to track KPIs post-optimization, such as:

  • Gross Merchandise Value (GMV) per category.
  • Customer retention rates for new categories.

Adjust inventory procurement based on real-time spreadsheet updates linked to POS systems.

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