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Скачать или смотреть The Lean Startup Framework The Essential Build, Measure, Learn Cycle

  • AINexLayer
  • 2025-12-16
  • 20
The Lean Startup Framework  The Essential Build, Measure, Learn Cycle
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Описание к видео The Lean Startup Framework The Essential Build, Measure, Learn Cycle

This lecture introduces the Lean Startup Framework, which is considered the de facto way that startups approach business opportunities today. Originating from the book The Lean Startup by Eric Ries, this framework chronicles his experiences in business during the tech boom and 2000s startups. It serves as the startup or small business version of the concept of lean manufacturing, popularized by Toyota.
The framework is built around the concept that initial business ideas often rely on untested assumptions. It is the "godfather of validation," shifting the previous approach where businesses simply created a plan, raised money, and pursued the idea. The core goal is to save resources and avoid spending months or millions of dollars pursuing something that people might not want to buy.
The Lean Startup Framework is a system of running rapid experiments to gain information directly from customers. The cycle is known as Build, Measure, Learn. You build something, measure customer interactions and reactions, learn from that information, and then rebuild or restart the process.
We will cover core concepts that treat testing business ideas like a scientific experiment, including:
• Hypothesis: Condensing your business idea and its underlying assumptions into one or two sentences.
• Validated Learning: Gathering information about your business idea from a real, live experiment, rather than just conjecture or personal beliefs.
• Pivot: Making a significant change to your original idea when validated learning reveals that the initial hypothesis or business model is not viable or lacks a real opportunity. Famous companies like Twitter used pivots after realizing their initial products were not desired.
• Minimum Criteria for Success: Establishing the subjective bar needed to judge whether your hypothesis or test has been proved true or successful.

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