Why though?

• I've been known to binge YouTube so much that if watching videos burned calories, I'd be a Greek god by now.

• As a devoted user, and a creator on the side…I’ve been noticing something:- YouTube’s like-to-view ratio is often shockingly low.

• It’s not rare to see a video with views in the thousands but likes in the mere double digits, despite comments filled with praise (sometimes comments are more than likes)

• In fact, most videos struggle to hit even a 4% like-to-view rate, with 1–5% being average in many niches (Questions that likes are even doing anything at all?) (Source: Veefly Blog, Social Media Dashboard, Sidesmedia)

• As someone who empathizes deeply with people & creators, it stings. It makes me want to shout, “Come on..you love it, I can see it in the comments, just click the like button!”

• Now I know what you’re thinking: “Does any of this really matter? Likes don’t put money in creators’ pockets.” And you’d be right…likes alone don’t directly translate to dollars.

• But stick with me here: I believe there's real value in improving that ratio - for creators, viewers, YouTube itself, and even advertisers.

It hurts everybody… But how? (Research)

1) Creators

• Weak social proof = morale + momentum drop. A “thousand views, few likes” pattern makes videos look unloved, even when comments are positive—creators on Reddit regularly vent about inconsistent engagement and algorithm signals, which affects motivation and content choices. (Source: Reddit)

• Fewer quality signals to the reco system. Likes are one of several engagement signals; when they’re scarce, the system has less explicit positive feedback (even if watch time is okay). (Source: google help, buffer)

• Brand deals care about engagement, not just views. Many sponsor briefs and creator guides emphasize engagement metrics (likes/comments rate) alongside views; a low like rate weakens the pitch. (Source: zapier)

  
  

2) Viewers

• Feedback tools feel weak or ignored. Research found YouTube’s negative feedback tools (e.g., Dislike, “Don’t recommend”) barely curb unwanted recommendations (Dislike blocked ~12%; “Don’t recommend channel” ~43%), making viewers feel their input doesn’t matter. Low trust → less feedback given. (Source: Wired)

• View counts ≠ real engagement. If views autoplay or count loosely, viewers learn that “a view” isn’t the same as “I liked this,” so they don’t bother to signal, widening the views/likes gap. (Source: The Verge)

  
  

3) YouTube (the platform)

• Noisy signals = harder recommendation tuning. YouTube validates engagement to keep it “high quality” (human, not bots). But when users don’t explicitly like/dislike, the system leans more on implicit signals (watch time/retention) and loses nuance. Your project increases explicit, high-quality signals. (Source: Google help)

• Watch time dominates, but it’s not everything. Public guidance and creator education emphasize watch time/retention; explicit reactions still help model relevance. More authentic likes boost learning without over-relying on a single metric. (Source: Buffer)

• Perception gap. If public “views” are inflated or inconsistent across platforms, the like deficit makes content quality harder to communicate to users at a glance, hurting trust and satisfaction. (Source: The Verge)

4) Advertisers

• Quality targeting needs quality signals. Advertisers don’t buy “likes,” but they do benefit when the platform has richer engagement signals to refine who actually enjoys content (and adjacent content), improving ad relevance. (Source: Zapier)

• Skepticism about ‘views’. When “view” metrics are lax across the industry, advertisers value corroborating engagement signals; an anemic like rate alongside high views undermines perceived quality of placement. (Source: The Verge)

Problem Statement

On YouTube, the like-to-view ratio is disproportionately low. While videos often receive thousands of views and even hundreds of positive comments, likes remain a small fraction. This lack of engagement:

• Leaves creators feeling underappreciated despite positive reception.

• Limits YouTube’s algorithm from getting clear signals on what viewers actually value.

• Reduces the platform’s ability to surface relevant content effectively.

• Weakens advertiser confidence, since feedback loops are incomplete.

The challenge: How might we design a lightweight, non-intrusive way to nudge viewers into providing meaningful feedback (likes/dislikes) without harming the viewing experience?

My Proposal: Nudging for Impact

To address the imbalance between views and likes, I designed a system that nudges (not forces) users to share quick feedback while watching. The goal is to make appreciation effortless for viewers, valuable for creators, and insightful for YouTube’s recommendation engine.

⚠️ Note: This is a concept exploration, not a final solution yet. Real-world implementation would require discussion with tech leads, since it could affect core code and recommendation systems. Factors like algorithm complexity, user behavior, creator sentiment, business goals, and ethical concerns would need to be evaluated. Think of this as a UX problem-solving exercise that could evolve into a real solution.

1. Smart Like/Dislike Prompt

• A subtle in-video prompt appears after a viewer has watched a meaningful portion of the video (e.g., 40–60%).

• Clear options: 👍 Like | 👎 Dislike | ⏭ Later.

• The prompt can be dismissed after 3 seconds, ensuring it doesn’t feel forced.

2. Contextual Copywriting Nudges | Empathetic Copies

• “Show your support — 👍 if you loved it, 👎 if it’s not for you.”

• Creators value all feedback — every tap helps them grow.”

• “Want to see more (or less) of this? Tell us with 👍 or 👎.”

• “Tap to fine-tune your recommendations instantly.”

• “Help the community discover the best content, faster.”

• Your feedback customizes your feed

3. Delayed / End-of-Video Reminder

• If a user taps Later, a gentle reminder appears at the end of the video or before autoplaying the next one.

4. Adaptive Algorithm

• Nudges are not shown on every video.

• The system only triggers when the viewer has consistently skipped feedback.

• This prevents fatigue and keeps the feature feeling lightweight.

5. Optional Experimentation Layer

• Users can choose their Engagement Preference in settings: Never prompt me or Prompt me sometimes Adding this option respects user autonomy and addresses edge cases.

Alternative Solutions (Which can be tested)

• Creator-led prompts

• Tools for creators to schedule a like/dislike CTA inside the video (like “subscribe” cards).

• Creators know their audience timing better than YouTube.
  👉 Pro: Personalized, authentic.

  👉 Con: Depends on creator effort.

  
  

• Gamification

• “Streaks” or “support badges” for consistent liking.

• Viewers see progress (“You’ve supported 10 creators this week”).

• Could tie into perks → discounts, creator shoutouts, etc.

• Leaderboards of constant supporters.
  👉 Pro: Builds habit.

  👉 Con: Might over-incentivize meaningless likes.

  
  

• Alternative signals (beyond likes)

• Introduce “Quick Reactions” (👏 😂 😮 ❤️) → like Linkedin or facebook

• More expressive, easier to tap than “Like/Dislike.”

• YouTube gets richer signals, creators get emotional feedback.
  👉 Pro: Higher engagement, nuanced.

  👉 Con: Might dilute the importance of the Like button. or huge changes in the codebase and UI needed.
  
  

Impact Analysis

Short-Term Impact

• Creators → Notice higher like/dislike activity, giving them a clearer sense of audience response.

• Viewers → Feel empowered to give lightweight feedback without leaving a comment.

• YouTube → Gains richer feedback signals → better recommendations, higher retention.

Long-Term Impact

• Better Personalization → Algorithms learn faster from both positive and negative signals.

• Increased Creator Motivation → More visible appreciation encourages more uploads and quality improvement.

• Stronger Advertiser Confidence → More accurate targeting = better ROI for ads.

• Platform Stickiness → Users trust the feed more (“YouTube gets me”), leading to higher daily watch-time.

CLASSIC HAPPY USER HAPPY COMPANY PHYLOSHOPHY (picture)

📊 Potential KPIs to Measure Success

• Engagement Uplift → % increase in like/dislike actions per view.

• Feedback Coverage → Ratio of videos with at least some feedback vs. baseline.

• Creator Sentiment → Surveyed satisfaction with feedback quality (not just views).

• Recommendation Accuracy → Increase in “watched >30 seconds” sessions from recommended feed.

• Ad Relevance → Lower bounce rates on ads tied to video categories with richer feedback.

⚠️ Trade-offs & Risks

• Viewer Fatigue → Too many prompts may annoy users → must limit nudges to smart intervals.

• Dislike Sensitivity → Creators might feel demotivated by more visible dislikes (needs careful framing).

• Algorithm Overfitting → Heavy reliance on feedback may skew recommendations too quickly.

• Implementation Cost → Any UI prompt or algorithm tweak requires significant A/B testing by YouTube.

Reflections & Learnings

• Empathy first: I realized that even a “small” interaction like liking/disliking has a big emotional effect on creators and ripple effects on platforms + advertisers.

  
  

• Balance matters: Forcing feedback can backfire. The design challenge lies in nudging users without breaking flow, a thin line between helpful and annoying.

  
  

• Systemic awareness: I learned how a UX idea can quickly connect to algorithms, revenue models, and tech complexity, design never lives in isolation.

  
  

• Copywriting is design too: The words we use (“Show your support” vs. “Like this video”) can drastically change how users respond.

  
  

• My growth: This exercise sharpened my ability to spot subtle product gaps, think across stakeholders, and propose design-led improvements that feel practical.