Inventory Management on eBay | Leverage Historical Sales Data for Smarter Stocking

Enhanced Prompt: Optimizing eBay Inventory Management with Historical Sales Data

Objective:
To enhance eBay inventory management by leveraging historical sales data and analytics to make informed decisions, improve efficiency, and maximize profitability. The goal is to achieve a balance between reducing holding costs, improving inventory accuracy, and increasing conversion rates.

Deliverables:

1. A comprehensive inventory management strategy that integrates historical sales data and analytics.
2. A detailed analysis of eBay-specific KPIs, including inventory accuracy, sales forecasting, fill rate, and days sales of inventory (DSI).
3. A actionable plan for implementing machine learning models to predict demand and optimize stocking levels.
4. A roadmap for integrating historical sales data with eBay's inventory management tools, such as Google Analytics and Seller Central.
5. A set of best practices for data validation, cleansing, and visualization to ensure the accuracy and reliability of historical sales data.

Industry Best Practices and Standards:

• Utilize machine learning models, such as linear regression and time series forecasting, to analyze historical sales data.
• Implement ABC analysis to prioritize inventory management based on sales volume and profitability.
• Use tools like Python, SQL, and AWS for data processing and analysis.
• Ensure compliance with eBay's policies and guidelines for inventory management and data privacy.

Technical Considerations:

• Data sources: Historical sales data from eBay's Seller Central, Google Analytics, and third-party tools like Google BigQuery.
• Integration: Use APIs to connect historical sales data with inventory management tools and analytics platforms.
• Data cleansing: Implement automated processes for identifying and correcting anomalies