APM Case Studies · YouTube · Two Product Proposals

YouTube
Product Cases

Two zero-to-one product opportunities hiding in plain sight — one on the supply side, one buried in user behaviour.

Case 03
Fix Shorts Monetization
Creator economy · Marketplace thinking
Case 04
Watch Later Graveyard
Behaviour gap · Funnel thinking
03

Case Study

Fix YouTube Shorts Monetization — Hybrid Revenue Model

Problem Space
Angle: Creator economy — the supply side is churn-risk
Signal this demonstrates: Marketplace thinking — supply-side focus. You understand that a two-sided platform dies if creators leave.
The core problem
3–5x
Shorts creators earn 3–5x less per 1,000 views than long-form creators. Ad revenue sharing on Shorts is structurally broken — ads appear between videos, not within them, diluting attribution.
Root causes
Ad inventory is shared across all Shorts in the feed — no per-creator attribution model
No native fan funding mechanism (tips, memberships) built into Shorts UI
Short format = low watch time = low Premium revenue pool allocation
Top Shorts creators migrate to TikTok Creator Fund or Reels Bonus programmes
AK
Aditya K., 22
Full-time Shorts creator · 800K subscribers
"My Shorts get 10M views a month but I earn less than a part-time job. I'm seriously considering moving to TikTok full-time."
Predictable incomeFan connectionGrowth tools
MS
Meera S., 19
Daily Shorts viewer · 2.5hr avg watch time
"I follow three creators I love on Shorts. I'd happily tip them a few dollars if there was a way to do it right there in the feed."
Low-friction supportCreator access
The solution — hybrid revenue model
1

Shorts ad pool rebalancing

Allocate a dedicated per-creator ad pool based on qualified watch time (>50% video completion). Creators get transparent RPM dashboards — no more black-box pooling.

Revenue attribution
2

In-feed fan funding

Super Thanks button embedded directly in the Shorts player UI. One-tap tip ($0.99–$50) via Google Pay. 70% to creator. Visible heart reaction drives social proof.

Fan funding
3

Shorts memberships lite

$1.99/month "Shorts Pass" per creator — unlocks exclusive Shorts content, early drops, badge on comments. Lower barrier than standard channel memberships.

Recurring revenue
4

Creator revenue dashboard

Unified earnings view — ad pool RPM, Super Thanks totals, membership MRR, projected month end. Weekly payout option to improve cash flow predictability.

Creator experience
North star metric
Creator 30/60-day retention
% of Shorts creators still active at day 30 and 60 post-launch
Target RPM uplift
+120%
Average Shorts RPM within 6 months
Fan funding conversion
4.5%
Of Shorts viewers who use Super Thanks at least once
Chose
Hybrid model (ads + fan funding)
Multiple revenue streams reduce creator dependency on any single mechanism. Fan funding aligns platform success with creator success.
Passed
Flat bonus per-view fund
TikTok's Creator Fund approach. Unsustainable at scale, doesn't reward quality over volume, and has been widely criticised by top creators.
Chose
In-feed Super Thanks (native)
Zero friction — one tap in the existing Shorts UI. Keeps the monetization moment inside the platform loop, not a redirect to Patreon.
Passed
External tip link in bio
Sends users off-platform. Higher churn risk for both creator and viewer. No data capture for YouTube.
Guardrails — must not break
Viewer experience must not degrade
Fan funding prompts must be opt-in, non-intrusive. No pop-ups mid-scroll — only creator-initiated or post-video moment.
Creator churn must not increase in long-form
Long-form RPM cannot be cannibalised. Shorts ad pool must be additive, not redistributed from long-form budgets.
Go-to-market roadmap
M1–M2
Closed beta — 500 top Shorts creators
Invite highest-RPM creators. Validate attribution model, payout accuracy, and Super Thanks UX. Gate on creator NPS > 50 before wider rollout.
M3–M4
Shorts Partner Programme expansion
Open to creators with 1K+ subscribers and 10M Shorts views in 90 days. Launch creator dashboard and weekly payout option.
M5–M6
Shorts memberships lite — public launch
Open $1.99 Shorts Pass to all eligible creators. A/B test badge designs and exclusive content formats for conversion optimisation.
M7+
Cross-format creator bundling
Allow Shorts membership to unlock long-form perks too — driving creators to produce across both formats and increasing total platform engagement.
04

Case Study

Reduce the Watch Later Graveyard — Smart Resurfacing Engine

Problem Space
Angle: Behaviour gap — saved does not equal consumed. You think in funnels.
Signal this demonstrates: Funnel thinking — you identified a leaky conversion step (Save → Watch) and built a system to fix it with behavioral nudges and intelligent resurfacing.
The behaviour gap
<15%
Fewer than 15% of videos saved to Watch Later are ever played. The list becomes a graveyard — a psychological burden that users eventually stop engaging with entirely.
Root causes
Save is instant gratification — the intent to watch is satisfied by the act of saving
Watch Later list has no prioritisation, expiry, or context — it becomes overwhelming
Algorithm never resurfaces saved content — it competes with fresh recommendation feed
No time-awareness — a "save for later" at 10pm never appears at the user's actual later
RV
Ravi V., 31
Casual viewer · Saves 8–10 videos/week
"I save things all the time thinking I'll watch them on the weekend. Then I open Watch Later and there's 200 videos — I just close it and scroll the home feed instead."
Manageable queueRight moment nudge
SL
Sophie L., 26
Power user · Watch Later = 400+ videos
"My Watch Later is basically my graveyard. I know there's great stuff in there but I can't face finding it. I'd love if YouTube just told me what to watch from it."
Smart curationContext-awareReduce friction
The solution — smart resurfacing + time-based nudges
1

Time-aware resurfacing

ML model learns when each user typically watches long vs. short content. Resurfaces a saved video at the optimal watch moment — weekend mornings for long-form, commute hours for short saves.

Contextual nudge
2

Smart queue — "Watch This Week"

Auto-curated 5-video digest from your Watch Later list, refreshed every Monday. Picks based on recency of save, content freshness (news/trending), and predicted enjoyment score.

Digest format
3

Expiry nudges + auto-archive

Saves older than 30 days get a soft "still want to watch?" prompt. No action = auto-archive (not delete). Archive clears visual clutter while preserving user intent — recoverable anytime.

Queue hygiene
4

Home feed "From your saves" shelf

A persistent shelf on the home feed surfaces 2–3 contextually relevant saves alongside the fresh feed. Normalises watching saved content as part of the regular session flow.

Discovery shelf
User journey — from save to watch
#
Touchpoint
User experience
Outcome
1
Saves video to Watch Later
Scrolling feed, saves a 22-min documentary — intent captured
Save recorded
2
Monday "Watch This Week" digest
Push notification — 5 curated saves appear at top of home feed
Resurfaced
3
Context-aware nudge fires
Saturday 10am — "You usually watch long videos now. Here's one you saved."
Nudged
4
User watches the video
Taps from shelf — auto-removed from Watch Later on 80% completion
Watched
5
Post-watch recommendation
"More from your saves" shelf surfaces next — builds habit loop
Habit loop
North star metric
Save → Watch %
Target: 15% → 38% within 90 days of feature launch
Watch Later engagement
+3x
Weekly Watch Later sessions per active user
Digest open rate
42%
Target for "Watch This Week" Monday notification
Chose
Contextual resurfacing (ML-driven)
Puts the right video in front of the user at the moment they're most likely to watch it. Works passively — no extra user action required.
Passed
User-managed priority tags
High friction — requires users to organise their saves manually. Addresses the symptom, not the root cause. Heavy users won't do it; light users don't need it.
Chose
Soft archive (not delete)
Reduces list overwhelm without destroying user intent. Recoverable archive respects the original save action — critical for trust and feature adoption.
Passed
Auto-delete after 30 days
Aggressive. High risk of user backlash and support tickets. Destroys trust with power users who curate large lists intentionally.
Guardrails — must not break
Watch Later list integrity
Auto-archive must be reversible and clearly communicated. Users must never feel their saves were deleted without consent.
Notification fatigue
Resurfacing nudges capped at 2 per week max. Users can opt down to weekly digest only — never daily pressure to consume their queue.
Go-to-market roadmap
M1–M2
A/B test — "From your saves" home shelf
Lowest-risk change. Test shelf position and number of cards. Measure Save → Watch conversion lift vs. control group. Target: +8% conversion in test cohort.
M3
Launch "Watch This Week" digest notification
Roll out to 10% of users with 10+ saves. Measure open rate, tap-through, and watch completion. Iterate on ML model curation quality based on explicit feedback (thumbs up/down on digest).
M4–M5
Time-aware resurfacing engine — public beta
Deploy contextual nudge system. Users can set preferred "watch time" windows or let the model infer. Pair with soft archive for lists exceeding 50 videos.
M6+
Full rollout + creator integration
Surface creator analytics on which of their videos are frequently saved-but-not-watched. Creators can publish a "watch now" push to their saved-video audience — new engagement lever.
Combined success targets
from ~3–5x gap
↑
+120% RPM
Shorts creator earnings
Day-30 creator churn
↓
-35%
Creator retention uplift
from <15%
↑
38%
Save → Watch conversion
Watch Later sessions
↑
3x
Weekly engagement
Two problems hiding in plain sight — one killing your supply side, one wasting your most intentional users.
Marketplace thinking Funnel thinking Supply-side focus Behaviour gap Creator retention Save → Watch