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Company focus

hipages

What caused the sudden 50% increase in customer support tickets related to hipages's tradie matching algorithm last month?

Prepared by NextSprints

15 mins
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Data Analysis Problem Solving Strategic Thinking Home Services Online Marketplaces Tech Platforms User Experience Root Cause Analysis Marketplace Optimization Customer Support Algorithm Improvement
Product Management Root Cause Analysis Question: Investigating sudden increase in support tickets for hipages tradie matching algorithm

Introduction

The sudden 50% increase in customer support tickets related to hipages's tradie matching algorithm last month is a critical issue that demands immediate attention. This surge in support requests indicates potential problems with the core functionality of our platform, which could significantly impact user satisfaction and retention. I'll approach this analysis systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Framework overview

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

Step 1

Clarifying Questions (3 minutes)

  • Given the abrupt nature of the increase, I'm wondering if there were any recent changes to the matching algorithm. Have there been any updates or tweaks to the algorithm in the past 1-2 months?

Why it matters: Recent changes could directly correlate with the increase in support tickets. Expected answer: Yes, there was a minor update to improve match accuracy. Impact on approach: If confirmed, we'd focus on analyzing the specific changes made.

  • Considering user segments, I'm curious about the distribution of these support tickets. Are they coming predominantly from tradies, homeowners, or is it evenly split?

Why it matters: This helps identify if the issue is affecting a specific user group more than others. Expected answer: The majority are from homeowners. Impact on approach: We'd prioritize investigating the homeowner experience and potential mismatches.

  • Looking at the timing, I'm thinking about seasonal factors. Has there been any unusual spike in demand for certain types of jobs that might be straining the system?

Why it matters: Seasonal trends could explain increased load on the system, leading to matching issues. Expected answer: There's been a 30% increase in renovation projects due to a government grant. Impact on approach: We'd need to consider scalability and how the algorithm handles demand surges.

  • Regarding the nature of the support tickets, I'm wondering about the specific complaints. What are the top 3 issues being reported by users?

Why it matters: This helps pinpoint whether the problem is with match quality, response time, or other factors. Expected answer: Complaints about mismatched skills, long wait times, and lack of responses from tradies. Impact on approach: We'd focus on these specific areas in our analysis and solution development.

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Updated Jan 22, 2025