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YouTube’s recommendation algorithm prioritizes click-through rate and audience retention over subscriber count. The platform tests new videos with a small sample audience; if engagement is high, it expands the reach to larger groups.2:16 Conversely, if viewers do not click or watch for a significant duration, the algorithm stops recommending the content.3:03 Subscribers do not guarantee views, as the system primarily serves content to users based on their recent viewing history rather than channel subscriptions.3:39 This creates a challenge for creators who produce varied content, as inconsistent topics confuse the algorithm and limit audience growth.12:05

Financial data from the channel Reversing Entropy shows that consistent, topic-specific content significantly increases revenue compared to sporadic, mixed-subject uploads.12:52 Over eight years, the channel earned 1,164 euros in estimated revenue, with nearly half of that total generated in the most recent two months of focused production.14:08 After accounting for taxes and platform deductions, the actual profit remains lower than the cost of production equipment.15:49

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