What’s the True First Metric for Success in the AI Era?

In the AI era, individuals, brands, and companies that help users capture the most beautiful moments will become winners.

When "Lean Data Analysis" was first published a decade ago, the modern internet was still nascent; iPhone was new, startups rarely disclosed metrics, and investors didn’t know what to ask. Choosing the first key metric was controversial.

Ten years later, the book’s core principles remain vital. It asks: what are the right metrics? Why should a startup test its most risky, uncertain hypotheses? Why must the whole organization continuously optimize as if it were the most important thing in the universe?

Good analysis is like natural selection—every system needs feedback loops, and startups are no exception. The book completes the Build‑Measure‑Learn loop introduced by Eric Ries, a simple mental model that separates high‑growth tech organizations from losing angel investors.

Although timeless analysis practices endure, the world has changed dramatically. New jargon such as “engaged minutes,” “residual customer value,” and “freemium conversion” now exists. Investors and executives know that without data, startups are merely guessing.

Today, data‑driven metrics are even more crucial. Technology permeates every aspect of life, and the first key metric for society should be time that people do not regret spending. In an age of cheap content and AI‑assisted work, those who help people find their best moments will win.

The nature of work has also shifted: delivery apps, subscription services, and one‑stop shop tools handle tasks once done by companies themselves. With fewer obstacles, entrepreneurs rely on trusted advice and community as a sustainable competitive advantage. Thus, a possible first key metric is how many people will share your message with strangers.

The book mainly discusses first‑level metrics such as conversion and churn. Automation and AI now make testing creative ideas easier, pushing startups to focus on second‑ and third‑level metrics.

Examples of metric hierarchies:

Conversion rate – “Did we sell more products?” → “Do these sales bring us closer to business goals?” → “Are those goals suitable for our company?”

Subscription rate – “How many people signed up?” → “How loyal are they?” → “Did we attract the people most beneficial for growth?”

Metrics alone are insufficient; founders must also build organizations they can be proud of, that users love and recommend.

We hope the fundamental principles of "Lean Data Analysis" guide your projects toward better outcomes. Hundreds of founders have reported that the book saved or redirected their ventures, emphasizing that the true first metric is the ability to use data to build a better organization faster.