What caused the sudden 30% decrease in transit agency adoption of Swiftly's on-time performance module last quarter?

Introduction

The sudden 30% decrease in transit agency adoption of Swiftly's on-time performance module last quarter is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for our product strategy.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into our product, user journey, and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose a comprehensive plan for validation and resolution.

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 on-time performance module in the last 3-6 months?

Why it matters: Product changes often impact adoption rates. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on the update's features and rollout process.

• Considering the specificity of the decrease, I'm wondering about our measurement accuracy. Has there been any change in how we measure or define "adoption" for this module?

Why it matters: Ensures we're addressing a real issue, not a measurement anomaly. Expected answer: No changes in measurement. Impact on approach: If yes, we'd need to reassess our metrics before proceeding.

• Given the magnitude of the decrease, I'm curious about user feedback. Have we seen any increase in support tickets or negative feedback specifically related to this module?

Why it matters: Direct user input can provide valuable insights into potential issues. Expected answer: Some increase in negative feedback. Impact on approach: If yes, we'd prioritize analyzing this feedback in our root cause analysis.

• Thinking about our user base, I'm wondering if this decrease is uniform across all transit agencies. Are there any patterns in terms of agency size, location, or type that correlate with this decrease?

Why it matters: Helps identify if the issue is universal or specific to certain segments. Expected answer: The decrease is more pronounced in smaller agencies. Impact on approach: If segmented, we'd focus on the most affected user groups first.