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Company focus

Intersection

What caused the sudden 30% decrease in daily active users for Intersection's IxNConnect WiFi service across Chicago transit stations last week?

Prepared by NextSprints

15 mins
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Data Analysis Problem Solving Strategic Thinking Smart Cities Public Transportation Telecommunications User Engagement Data Analysis Root Cause Analysis Transit Tech Public WiFi
Product Management Root Cause Analysis Question: Investigating sudden decrease in public WiFi usage across Chicago transit stations

Introduction

The sudden 30% decrease in daily active users for Intersection's IxNConnect WiFi service across Chicago transit stations last week 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 the service.

To tackle this problem, I'll follow a structured approach that covers issue identification, hypothesis generation, validation, and solution development. My goal is to uncover the underlying factors contributing to this significant drop in user engagement and propose actionable steps to rectify the situation.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the timing, I'm thinking this could be related to a recent system update. Has there been any significant change to the IxNConnect infrastructure or user interface in the past two weeks?

Why it matters: System changes often impact user behavior and could explain sudden drops in engagement. Expected answer: Yes, there was a minor update to the login process. Impact on approach: If confirmed, we'd focus on analyzing the update's impact on user experience.

  • Considering the specificity of the decrease, I'm curious about the data integrity. Can you confirm that the 30% decrease is consistent across all data sources and measurement tools?

Why it matters: Ensures we're addressing a real issue and not a data anomaly. Expected answer: The decrease is consistent across multiple data sources. Impact on approach: If inconsistent, we'd prioritize data reconciliation before further analysis.

  • Given the localized nature of the service, I'm wondering about external factors. Have there been any significant changes in Chicago's public transit operations or ridership in the past week?

Why it matters: External factors could explain changes in WiFi usage patterns. Expected answer: No major changes reported in transit operations. Impact on approach: If changes occurred, we'd consider their impact on WiFi usage patterns.

  • Thinking about user segments, I'm curious if this decrease is uniform across all user types. Can you provide a breakdown of the decrease by user segments (e.g., commuters, tourists, students)?

Why it matters: Helps identify if the issue affects specific user groups disproportionately. Expected answer: The decrease is more pronounced among commuter users. Impact on approach: We'd focus on understanding changes in commuter behavior or needs.

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Updated Jan 22, 2025