The starting point should be

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The reasons behind them. Growth experiment process Next, we introduce the growth experiment process in detail. The growth experiment process consists of five steps: generating experimental ideas, prioritizing, experiment design, development and launch, analysis and application of results. Generating Experimental Ideas: The key to generating high-quality experimental ideas lies in three steps: clear goals, data insights, and hypothesis building. Clear goals: user needs and business problems, not personal subjective ideas. Goal setting needs to be specific, relevant, and progress can be measured. Data Insights:




After building a sound growth model, collect actual data to identify problem areas. Use quantitative data to uncover the symptoms of a problem and explore its root causes through
Combined with industry best practices, experimental Hong Kong Phone Number hypotheses are refined. Construct a hypothesis: Record it according to a structured experimental hypothesis template, for example: "If [specific change] is implemented, [target metric] is expected to increase by X% because of [underlying reason based on data]." Such a template can help clarify Express expectations and rationale for the experiment. Prioritization: Faced with the many experimental ideas proposed by the team, we must not only pursue efficiency to promote rapid growth, but also consider the limited resources. qualitative analysis such as user interviews





To do this, prioritizing experimental ideas is crucial. It is recommended to use tools such as ROI (return on investment) or ICE scoring model (expected impact, success probability Confidence, implementation ease) to assist evaluation. However, it is important to realize that prioritization is not an exact science, it is only a relative quantitative assessment tool. In practical applications, accuracy should not be excessively pursued, but should be guided by this and flexibly adjusted according to the actual situation. Experimental design: The key to successful experimental design lies in the following three core elements: Select experimental metrics: Choosing appropriate metrics is the