Via Transportation·Software Engineer·Onsite - Product Sense / Strategy·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a bizops role at Via Transportation and got hit with a classic ops/product hybrid question about reducing support ticket volume. Pretty standard case format but the rideshare context adds some wrinkles worth thinking through.

Questions Asked (1)

What steps would you take to reduce the volume of customer support tickets on a rideshare platform?

Root Cause AnalysisProduct Sense & IdeationProduct Analytics & Metrics

Author's notes

My first instinct was to jump straight into solutions, which is the wrong move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal and scope: reducing ticket volume while maintaining or improving customer satisfaction. Then, propose a data-driven approach: analyze ticket data to identify top drivers, prioritize high-impact root causes, and design engineering solutions (e.g., product improvements, automation, self-service) to address them. Finally, define success metrics and iterate.

Pro tip: Emphasize that not all tickets are bad—some indicate valuable feedback. Focus on reducing avoidable contacts (e.g., 'where is my ride?') through product improvements, while ensuring critical issues still reach support.

1. Clarify and Align on Goals

Confirm the objective: reduce ticket volume without harming customer experience. Ask about current metrics, ticket categories, and any constraints.

2. Analyze Ticket Data

Examine ticket data to identify top contact drivers (e.g., ride delays, payment issues, app bugs). Segment by user type, region, and time to find patterns.

3. Prioritize Root Causes

Use impact/effort matrix to prioritize root causes. Focus on high-volume, solvable issues that can be addressed via engineering (e.g., improved ETA accuracy, proactive notifications).

4. Design and Implement Solutions

Propose engineering solutions: product fixes (e.g., bug fixes), automation (e.g., chatbots for FAQs), self-service (e.g., in-app help center), and proactive communications (e.g., push notifications for delays).

5. Measure and Iterate

Define success metrics (e.g., ticket volume reduction, CSAT, resolution time). A/B test changes, monitor impact, and iterate based on results.

Key Points to Mention

Data-driven approach: use ticket categorization and analytics to find root causes.
Prioritization: focus on high-volume, high-impact issues first (e.g., 'where is my ride?' tickets).
Engineering solutions: product improvements (ETA accuracy, bug fixes), automation (chatbots, self-service), proactive notifications.
Balance: avoid reducing critical support access; measure CSAT alongside ticket volume.
Iterative process: A/B testing, monitoring, and continuous improvement.
Cross-functional collaboration: work with product, data, and support teams.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.