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A PM’s guide to choosing business metrics for AI/ML projects
As product managers working on AI/ML projects, we often find ourselves caught between model metrics like accuracy or F1 scores and actual business impact. While data scientists focus on improving model performance, our job is to ensure these improvements translate into tangible business results.
The key difference is simple: model metrics tell you how well your model performs technically, while business metrics show the actual value it creates. For example, a model with 99% accuracy might still fail to deliver business value if it’s solving the wrong problem.
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Product management fundamentals
How to create products that people need
- Addressing user pain points vs solving user problems better
- Product manager skills: evolution of a PM role and its transformation
- Product metrics, growth metrics, and added value metrics
Product analytics: tools and hints
How data and analytics help product managers make the right decisions
- How to tell if your product has proven its value and achieved product/market fit
- The chances of a new mobile app being successful today: a study
- Сreating marketing analytics dashboards the right way: a guide
Professions and skills
Essential skills and knowledge for product managers and analysts
- Jobs to Be Done: comparing different frameworks
- The product manager’s guide to statistical analysis
- A product manager’s ultimate guide to data visualization
Unit economics
Calculating unit economics, LTV, ROI, ARPU, and other crucial metrics the right way
- How to calculate customer Lifetime Value. The do’s and don’ts of LTV calculation
- Guide to ARPU: formula, calculation example, LTV vs ARPU
- How to calculate unit economics for your business
Product testing and experiments
How to use A/B test and experiments to benefit the product
- Experiments where you make your product worse—the most underrated product manager tool
- Why your A/B tests take longer than they should
- Peeking problem—the fatal mistake in A/B testing and experimentation
AI/ML in product development
How to use AI/ML to add value to products
- A PM’s guide to choosing business metrics for AI/ML projects
- Error analysis for LLM applications
- Advanced methods for improving product quality with LLM: fine-tuning
Switching to product management
Insights on transitioning to product management
- How to get product management experience before you have the job title
- Side projects to enter product management: tips and expert insights
- How to move from product analytics to product management?
Product success stories
What makes great products great?
- The values and principles of Wise. Key ideas from the Breakout Growth Podcast by Sean Ellis
- How Revolut Trading was built. The importance of industry expertise and the balance of conservative and new approaches
- How the “Slack vs Microsoft Teams” race evolves as the world switches to remote work