This approach is something I learned from reading Avinash Kaushik's blog. It’s a simple but powerful concept on how someone should approach a solution. The idea actually comes from the world of math.
Let's look at some examples. These are problems or requests that are like local maxima and the solutions that act as global maxima.
A stakeholder request about a change in a product feature.
Develop a proper user experience that incorporated the request of the stakeholder while ensures optimal UX.
A user complaint about lack of rewards.
Develop a loyalty system that provide rewards to loyal users by incorporating 3rd party partnerships (that subsidize the cost of reward in exchange of promo campaigns).
User complaints that can’t find what he wants easily.
Develop personalized mechanisms to provide relevant options that increase the generated revenue too.
A GDPR guidance.
Develop a solution that is GDPR compliant but does not affect (or affect a little) the existing user flow & experience.
An analysis of how to increase KPI X by 5% in feature .
An analysis if feature A provides incremental revenue and how much.
Top product managers (PMs) aim for global maxima. It's tougher, as you can see, but it's the only way to give your customers a complete and well-thought-out experience. It's not just about the product. You have to work well with the Marketing, Sales, and Operations teams too. They help you reach the global maximum for your project.
A good PM can make a great product, but a great PM can do that and also get everyone in the company to work together to make the product even better.
Moreover, when we talk about analysis, focusing on global maxima is crucial. Most of the time, analysts look at small changes in features:
- How can we get 5% more people to use feature A?
- Why did feature B lose 2% engagement yesterday?
- Why did transactions go up by 3% last week?
Global maxima is about the big picture. They're not useful for making small changes but are great for understanding if a feature is actually helping the business:
- Does feature A make us more money?
- What happens if we get rid of feature B?
- Are we gaining loyal users?
Many PMs know their numbers, but few understand if their product is really going in the right direction.
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