Lean Product Management
Though every organization and product are different, I try to apply the same Lean process to product development, by valuing the opportunity, our most important metrics, and agility above all else.
1. Identify the Product Opportunity
- Are we meeting our users’ needs currently?
- Can we provide greater value than our competitors?
- How are we able to monetize this opportunity?
- Are we capable of building an exceptional user experience?
- What is the core value proposition to our most-valued user?
2. Focus on the Most Impactful KPI
Align the product opportunity with the organization’s vision and strategy.
Define what success should look like.
Focus on the single KPI that will accomplish that success.
Communicate that metric to the team and stakeholders clearly.
Edit out ideas or existing features that do not relate to the chosen KPI.
3. Iterate with Low Cycle Times
Step 1. Ensure the team is cross-functional, or can be in the future.
Step 2. Co-locate the team, to quickly generate ideas and resolve differences.
Step 3. Create stories to map out features, and simple sketches to depict the experience.
Step 4. Start with the most critical features, but work small and rewrite often.
Validation at Any Level
When considering which product direction to go in, gut and intuition are valuable tools, but it’s also necessary to constantly keep them in check with hypotheses and data-driven insights.
Product Discovery Best for large-scale features or new product offerings.
A/B Testing Best for large-scale features or new product offerings.
While predominantly a technology concern, Continuous Delivery plays a major role in how I as a product manager can deliver value and quality to users, and gain competitive edge and analytics data sooner. I find that organizations that adopt it are more able to adapt to changing market and user landscapes.
A Typical Day with Continuous Delivery
- Shortened time-to-market
- Quickly released bug fixes
- Regular development cadence
- Faster delivery of value
- Earlier collection of analytics data
- Smaller batch size; minimized risk