How to Build Your Product Team

Reference

• Pillars
  • User centric. The voice and advocate for the users. User interviews, customer development, persona development. What do they want to be?
  • Business centric. They have a product vision to achieve business objectives.
  • Data-informed decision making. SQL + Analytics + A/B Testing.
    • Influence without authority: persuade others with data, analyses, and storytelling.
  • Technology literate. They should at least know what tech to leverage to best execute their vision.
  • Cross-functional influence without authority
  • Team lead: Prioritize, unblock, ship
• Good PM vs Bad PM
  • Bad: Spends time on internal matters. Good: Spends time validating with customers.
  • Bad: “Product must be right”. Good: Balances getting it right vs getting it out.
  • Bad: Only thinks about high-level problems. Good: High-level thinking + attention to detail.
  • Bad: Throws specs over the wall. Good: Communicates shared understanding across the team.
  • Bad: Focused on inputs/outputs. Good: Focused on outcomes over outputs.
• Product Managers are not Project Managers
• Product Design Question
  • Test for precision questioning: Do they know how to dig deep? What are we going to solve?
  • Understanding the user. Trade-offs, etc.
  • Metrics for success.
  • See how they respond to feedback.
• Analytics Question
  • Market sizing
  • Business model
  • Ability to draw insights from data
• Past Experiences: Product or Feature that You’ve Shipped
  • Customer interaction
  • Practical product design methodology
  • How well they work with stakeholders
  • Conflict management
  • Prioritization and trade-offs
• Favorite product
  • Understanding the who, what, and why
  • Thought process for incremental improvements
  • Metrics for success
• Scaling
  • Small independent teams (two-pizza teams)
  • Own backlog, delivery, KPIs

Thing I Saw on LinkedIn

• Yi-Wei: Create a repeatable process that has the potential to continuously drive growth.
  • Have to know and think of your work as a series of small, continuous experiments to determine if a desired outcome is achieved.
  • This has to be falsifiable!
  • The key for these experiments is learning.
  • Experimental backlog.
• TradeGecko example:
  • Growth of leads were not proportional to content output.
  • Hypothesis: “We believe that by providing free tools on existing content, we can get higher yields”
    • +Engagement, -Bounce rate, but no impact to leads
    • Clear CTA with a downloadable: increased leads generated on the page by 10x.
    • Data-driven experiments give you confidence to double down.
  • All components of product, engineering, marketing, and sales need to work in harmony to create a desirable customer journey.