Why has Seniorlink's Vela care management platform seen a 15% decrease in daily active users over the past month?

Introduction

The recent 15% decrease in daily active users (DAU) for Seniorlink's Vela care management platform is a concerning trend that requires immediate attention and a thorough root cause analysis. As we delve into this issue, we'll systematically examine potential factors contributing to this decline, formulate data-driven hypotheses, and develop a comprehensive plan to address the underlying causes.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal component. Has this 15% decrease been consistent across all user segments, or are certain groups more affected?

Why it matters: Understanding the distribution of the decline helps focus our investigation. Expected answer: The decline is more pronounced among family caregivers. Impact on approach: We'd prioritize investigating factors specific to family caregiver usage patterns.

• Considering recent product updates, I'm curious if any significant changes were implemented in the past 1-2 months. Were there any major feature releases or UI/UX modifications during this period?

Why it matters: Recent changes could directly impact user engagement. Expected answer: A new medication management feature was rolled out six weeks ago. Impact on approach: We'd examine the feature's adoption rate and any related user feedback.

• Thinking about external factors, have there been any changes in healthcare policies or reimbursement models that might affect how care providers use the platform?

Why it matters: Policy changes can significantly influence platform usage in healthcare. Expected answer: No major policy changes, but a competitor launched a similar product. Impact on approach: We'd analyze our competitive positioning and unique value proposition.

• Considering data integrity, I'm wondering if there have been any changes to how DAU is measured or any known issues with the analytics system?

Why it matters: Ensures we're working with accurate data and not chasing a phantom problem. Expected answer: No changes to measurement, but there was a brief outage in data collection. Impact on approach: We'd adjust our analysis to account for any data gaps or inconsistencies.