Introduction
For aspiring Product Managers aiming for top-tier tech companies like those in FAANG, mastering the interview process is crucial. A frequently encountered question involves prioritizing technical debt over new features. This blog post will guide you through answering this question effectively, utilizing structured frameworks as recommended by ‘Decode and Conquer: Answers to Product Management Interviews.’
Detailed Guide on Framework Application
Picking the Right Framework
When choosing a framework to address this question, the RICE scoring model (Reach, Impact, Confidence, Effort) combines qualitative and quantitative analysis, making it well-suited for prioritization decisions.
Step-by-step Application of the RICE Framework
When evaluating whether to prioritize technical debt or new features, use the RICE framework to systematically assess and compare your options:
- Reach: Estimate how many users will be affected by addressing tech debt versus implementing a new feature. Consider user complaints, support tickets, or potential user growth.
- Impact: Consider the positive effect on users if tech debt is reduced (like performance improvements) versus the value a new feature would provide. Quantify impact using metrics like conversion rate, user satisfaction, or retention rate.
- Confidence: Assess the level of certainty in your reach and impact estimations as high, medium, or low. Confidence could be based on past data, user research, or expertise of the development team.
- Effort: Evaluate the amount of work required to address the tech debt or to add the new feature in work-hours or sprints. Use team insights for the most accurate estimation.
Hypothetical Example
Imagine your company has a significant piece of legacy code that causes frequent crashes. Applying the RICE framework:
- Reach: 25% of the user base experiences crashes monthly.
- Impact: Fixing the issue would improve retention by an estimated 10%.
- Confidence: High, based on crash logs and user feedback.
- Effort: Estimated at 2 sprints to refactor the problematic code.
Contrast this against a new feature that’s expected to bring a 5% growth in user acquisition with high confidence but requires 4 sprints. The tech debt’s Reach, high Impact, and lower Effort might make it the priority.
Fact Checks and Approximations
Use data from industry benchmarks, or historical data from your company as a baseline for your approximations. For instance, if the industry standard for crash-free sessions is 99.9%, and your product is at 98.5%, this fact supports prioritizing tech debt.
Communication Tips
Use clarity and conciseness to articulate your reasoning. Support your answer with data when possible and acknowledge uncertainty by providing high-confidence estimations. Tailor your communication to the interviewer’s technical background, avoiding jargon if they’re non-technical.
Conclusion
Prioritizing technical debt is a complex but common task for Product Managers. The RICE framework offers a structured approach to make informed decisions. By using this framework and the strategies discussed, candidates can demonstrate their analytical capabilities and showcase the importance they place on both short-term performance and long-term product health. Practice applying these frameworks and strategies to other tech prioritization scenarios to hone your interview skills.