Why has Great Learning's Data Science course enrollment dropped by 30% in the past quarter?

Introduction

Great Learning's Data Science course enrollment has dropped by 30% in the past quarter, signaling a significant challenge for the product team. This analysis will systematically identify, validate, and address the root cause of this enrollment decline, considering both immediate and long-term implications.

I'll approach this issue by first clarifying the context, then ruling out external factors before diving deep into product understanding, metric breakdown, and hypothesis formation. We'll then conduct a thorough root cause analysis, propose validation methods, and outline a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be seasonal factors at play. Has this 30% drop been compared to the same quarter last year?

Why it matters: Seasonal trends could explain the fluctuation and impact our approach. Expected answer: Yes, it's been compared and is still significant. Impact on approach: If seasonal, we'd focus on year-over-year comparisons rather than quarter-over-quarter.

• Considering user segments, I'm curious about the enrollment drop distribution. Is the 30% decline uniform across all user segments or concentrated in specific groups?

Why it matters: Identifying affected segments helps pinpoint targeted solutions. Expected answer: The decline is more pronounced in mid-career professionals. Impact on approach: We'd investigate factors specific to this demographic if confirmed.

• Thinking about recent changes, have there been any significant updates to the course content, pricing, or marketing strategies in the last quarter?

Why it matters: Recent changes could directly impact enrollment numbers. Expected answer: A new pricing structure was implemented two months ago. Impact on approach: We'd analyze the pricing change's impact on different user segments.

• Regarding data accuracy, has there been any change in how enrollments are tracked or reported in the last quarter?

Why it matters: Ensures we're comparing apples to apples in our data analysis. Expected answer: No changes in tracking or reporting methods. Impact on approach: If changed, we'd need to recalibrate our baseline metrics.