Why has Hyperscience's document classification accuracy rate dropped by 15% over the past month?

Introduction

The recent 15% drop in Hyperscience's document classification accuracy rate over the past month is a critical issue that demands immediate attention. This decline directly impacts the core value proposition of our product and could have far-reaching consequences for user satisfaction and retention. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term strategic solutions.

Step 1

Clarifying Questions (3 minutes)

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

Why it matters: Recent changes could directly impact accuracy. Expected answer: Yes, there was an update to improve processing speed. Impact on approach: If confirmed, we'd focus on the update's unintended consequences.

• Considering user segments, I'm curious about the distribution of this accuracy drop. Is the 15% decrease uniform across all document types and user groups, or is it more pronounced in specific areas?

Why it matters: Helps isolate the problem to specific use cases or document types. Expected answer: The drop is more significant in complex, multi-page documents. Impact on approach: We'd prioritize investigating the handling of complex documents.

• Given the importance of data quality, I'm wondering about our ground truth dataset. Have there been any changes to the dataset used for training or evaluating the classification model in the past month?

Why it matters: Changes in the evaluation dataset could affect accuracy measurements. Expected answer: No changes to the core dataset, but some new document types added. Impact on approach: We'd examine how new document types are integrated and evaluated.

• Thinking about external factors, I'm curious about any changes in user behavior or document submission patterns. Have we seen any significant shifts in the types or volumes of documents being processed recently?

Why it matters: Changes in input could affect overall accuracy if not properly handled. Expected answer: There's been a 20% increase in document volume from a new enterprise client. Impact on approach: We'd investigate how increased volume and potential new document types are impacting the system.