Why has Globality's supplier matching algorithm seen a 15% decrease in accuracy over the past month?

Introduction

Globality's supplier matching algorithm accuracy has decreased by 15% over the past month, indicating a significant issue in the core functionality of the platform. This problem could have far-reaching consequences for both suppliers and clients using Globality's services. I'll approach this analysis systematically, focusing on identifying potential root causes, validating hypotheses, and developing a comprehensive solution strategy.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update to the algorithm. Has there been any significant change to the matching algorithm in the past 1-2 months?

Why it matters: Recent changes could directly impact accuracy. Expected answer: Yes, there was an update to improve efficiency. Impact on approach: If yes, we'd focus on the update's impact; if no, we'd look at other factors.

• Considering the scale of the decrease, I'm wondering about data quality. Have there been any changes in the data sources or data collection methods recently?

Why it matters: Data quality is crucial for algorithm accuracy. Expected answer: No significant changes in data sources. Impact on approach: If yes, we'd investigate data integrity; if no, we'd look at other factors.

• Given the specificity of the 15% decrease, I'm curious about the measurement methodology. Has the method for calculating accuracy changed in any way?

Why it matters: Ensures we're comparing apples to apples. Expected answer: No changes in measurement methodology. Impact on approach: If yes, we'd need to reassess the actual decrease; if no, we confirm the 15% is accurate.

• Thinking about external factors, have there been any significant changes in the types of suppliers or clients using the platform in the past month?

Why it matters: Changes in user base could affect algorithm performance. Expected answer: Some increase in new supplier categories. Impact on approach: If yes, we'd investigate how new users might be affecting accuracy; if no, we'd focus more on internal factors.