Diagnosing Pricing Discrepancies

Requirements Clarification & Assessment

1. Understanding the Pricing Algorithm:

   • Algorithm Type: Identify the type of algorithm in use (e.g., neural network, decision tree, linear regression) to understand its strengths and limitations.
   • Parameters and Weights: Examine the parameters and weights assigned to factors like availability, demand, and logistics costs.
   • Data Inputs: Clarify the data sources, including historical pricing data, sales volume, demand trends, and logistics costs.

2. Identifying the Problem:

   • Underpricing Definition: Define what constitutes underpricing (e.g., compared to competitors, historical prices, or expected margins).
   • Affected Product: Identify the specific product(s) affected by underpricing and gather historical data on its pricing and sales.

3. Clarifying External Factors:

   • Market Dynamics: Consider factors like seasonality, economic changes, and competitor pricing that might impact demand and pricing.
   • Customer Feedback: Investigate product reviews or feedback that might influence demand.

4. Data Quality Assessment:

   • Data Completeness: Ensure all necessary data points are available and accurate.
   • Data Timeliness: Verify that the data used is up-to-date and relevant.