⚠️ CRITICAL CONTEXT: Staff vs Dedicated On-Camera Talent
Important Distinction: Henry, John, and Elizabeth are Tami Media STAFF (the company that owns ZOOKA), not dedicated on-camera talent. They have full-time responsibilities in editing, production, and operations.
Implication for Recommendations:
- Henry (+297.6% lift): Exceptional performer, but his primary value is behind-the-scenes. He can be a RECURRING GUEST, not core cast.
- John (+54.3% lift): Strong performer! But already has full-time staff roles. Use as RECURRING GUEST for special episodes.
- Elizabeth (-19.1% lift): Negative lift. Already has full-time staff roles. Occasional guest appearances only.
This means: Your ACTUAL dedicated on-camera talent is currently just Ade (-12.7% lift) and Semiloore (+4.7% lift). You need to find 3-5 more DEDICATED on-camera talents who can commit to 80% of videos.
❌ Why Your Original Cast Analysis Was Wrong
User's Brilliant Question:
"What if we are doing the cast analysis wrong? What if the views are low because the topic the talent participated in is not good?"
This question changed everything.
The original analysis looked at raw averages:
| Cast Member | Original Avg Views | Original Verdict | The Problem |
|---|---|---|---|
| Ade | 1,492 | ✅ Core | Appeared in 10 Holiday videos (9,936 baseline) |
| Elizabeth | 1,401 | ✅ Core | Appeared in 12 Holiday videos (9,936 baseline) |
| Henry | 1,547 | ⭐ Star Potential | Appeared in 3 Holiday videos + mostly Guess/React |
🚨 The Fatal Flaw:
If Ade gets 1,492 avg but appears in Holiday videos (9,936 baseline), he's performing 85% BELOW topic potential.
If Henry gets 1,547 avg but appears in Guess/React videos (374 baseline), he's performing 313% ABOVE topic potential.
Raw averages hide who's actually lifting performance vs riding good topics.
✅ The Multi-Factor Methodology
We rebuilt the entire analysis from scratch with 3 new systems:
1️⃣ Topic/Format Classification System
Analyzed 182 long-form video titles (2+ min) to automatically classify content into 10 categories with performance baselines:
Best performing (12 videos)
Strong performance (35 videos)
At channel average (44 videos)
Below average (6 videos)
28% below channel (4 videos)
30% below average (47 videos)
Poor performance (6 videos)
37% below average (52 videos!)
38% below average (42 videos)
49% below average (13 videos)
💡 KEY INSIGHT:
Holiday content performs 6.6X BETTER than Guess content (2,130 vs 325). Nigerian Culture (775 avg, 35 videos) is your second-best topic with solid volume. Topic choice matters MORE than anything else.
2️⃣ Title Quality Scoring System
Scored all titles 0-10 based on 7 factors:
🤯 THE PARADOX WE DISCOVERED:
Low Quality Titles (0-3 score): 3,016 avg views
High Quality Titles (7-10 score): 687 avg views
Wait, what?! "Low quality" titles perform 4.4X better than "high quality" titles!
The Truth: TOPIC matters 100X more than title optimization tricks. A simple title on a Holiday video (9,936 baseline) beats an "optimized" title on a Guess video (378 baseline) every time.
3️⃣ Cast Lift Factor Analysis
The Breakthrough Metric: For each video, we calculate:
Lift Factor = ((Actual Views - Topic Baseline) / Topic Baseline) × 100
Example:
- React video baseline: 330 views
- Henry appears in React video → 1,030 views
- Henry's lift: ((1,030 - 330) / 330) × 100 = +211.9%
- Translation: Henry makes React videos perform 3X better!
- Holiday video baseline: 9,936 views
- Ade appears in Holiday video → 6,756 views
- Ade's lift: ((6,756 - 9,936) / 9,936) × 100 = -32.0%
- Translation: Ade makes Holiday videos perform 32% WORSE!
✅ THIS IS THE TRUTH WE WERE LOOKING FOR!
Lift Factor reveals who's actually carrying content vs who's being carried by good topics. It controls for confounding variables and shows TRUE talent impact.
🎯 Cast Lift Factor Results: The Truth Revealed
HENRY 👑
AVERAGE LIFT ACROSS ALL TOPICS (LONG-FORM)
🔥 WHY HENRY IS YOUR SUPERSTAR:
- Makes bad topics good: His Guess/React videos perform 4X above topic baseline
- Makes good topics great: His Holiday videos hit 2K+ (vs 2,130 baseline = at/above target)
- Consistent uplift: +297.6% lift = Makes content perform nearly 4X better!
- Reliable sample: 13 long-form videos, ALL show strong performance
- BUT: Tami Media staff - primary value is behind-the-scenes (editing/production)
💎 STRATEGIC VALUE:
Henry is exceptionally talented on-camera with the highest lift factor. However, he's Tami Media STAFF with full-time behind-the-scenes responsibilities. Recommendation: Use as RECURRING GUEST (10-20% of videos) for high-impact special episodes where his +297.6% lift maximizes views. Don't rely on him as core cast due to staff commitments.
SEMILOORE
AVERAGE LIFT ACROSS ALL TOPICS (LONG-FORM)
🤔 THE SURPRISE FINDING:
Semiloore's raw average (416) is below channel (-19% vs 516). Could be seen as underperforming.
BUT: Her +4.7% lift factor reveals she's actually SLIGHTLY POSITIVE. Why the disconnect?
Answer: Semiloore appears heavily in low-performing formats. When you control for topic quality, she actually lifts performance by ~5%. She's being dragged down by format choices, not her own performance. When you put her in better topics, she performs at or slightly above baseline.
💡 STRATEGIC DECISION:
KEEP Semiloore as CORE DEDICATED TALENT - She's your ONLY current dedicated on-camera talent. Positive +4.7% lift, 63 long-form videos (reliable sample), audience familiarity. STOP putting her in low-performing formats (Guess, Game, Relationship). Focus on Holiday, Nigerian Culture, and Music topics where her lift maximizes.
JOHN 🌟
AVERAGE LIFT ACROSS ALL TOPICS (LONG-FORM)
🎉 THE BIG SURPRISE:
John's +54.3% lift is STRONG! When shorts are excluded, he's actually a significant positive performer.
What changed? Including shorts showed negative lift. Long-form only reveals John makes content perform 54% BETTER than topic baseline.
Explanation: John likely appeared in many underperforming shorts that dragged his average down. His long-form performance is consistently strong across topics.
💡 STRATEGIC RECOMMENDATION:
Use John as RECURRING GUEST - His +54.3% lift is excellent! However, he's Tami Media STAFF with full-time behind-the-scenes responsibilities (editing, production, operations).
Optimal use: Feature in 10-20% of long-form videos (2-3x per month), especially Holiday and Nigerian Culture topics where his lift maximizes views. Don't rely on him as core cast due to staff commitments, but definitely use him more than Elizabeth!
ELIZABETH
AVERAGE LIFT ACROSS ALL TOPICS (LONG-FORM)
⚠️ THE REALITY:
Elizabeth's -19.1% lift with 68 long-form videos (large sample) shows she consistently drags content performance down by ~19% regardless of topic.
Not catastrophic, but measurably negative. When you put her in Holiday videos (2,130 baseline), they underperform. Henry/John in the same topics significantly outperform.
This is costing you ~20% of potential views on every video she's in.
💡 RECOMMENDATION:
Focus on behind-the-scenes role. Elizabeth is Tami Media STAFF with primary value in production, editing, or operations. Her -19.1% lift suggests on-camera isn't her strength.
Strategic use: Occasional guest appearances (1x/month max) in specific niche content only (e.g., fashion/beauty topics if she has expertise). Otherwise, leverage her staff contributions behind-the-scenes where she adds value.
ADE
AVERAGE LIFT ACROSS ALL TOPICS (LONG-FORM)
⚠️ THE REALITY:
Ade's -12.7% lift with 76 long-form videos (large sample) shows he consistently underperforms topic potential by ~13%.
What changed? Shorts analysis showed -32% lift. Long-form reveals less negative performance, but still measurably below topic baseline.
Explanation: Ade's raw average (560) is above channel average (+8.5%), but he appears in better-than-average topics. When you control for topic quality, he consistently underperforms by 13%.
💡 STRATEGIC OPTIONS:
Option 1: Transition to behind-the-scenes role - Focus on strategy, operations, business development, partnerships. Use his experience and screen time to mentor NEW dedicated talents.
Option 2: Targeted improvement - Give him a 10-video test in specific formats where he might excel (e.g., Nigerian Culture solo content). If lift improves to neutral/positive, continue. If still negative, transition off-camera.
Recommendation: As a dedicated on-camera talent with -12.7% lift, Ade is underperforming. You need talents with +10% or higher lift to scale. Consider transition or targeted improvement plan.
🎯 Your REVISED Core 5 Strategy
✅ CONFIRMED DEDICATED ON-CAMERA TALENT
1. SEMILOORE (+4.0% lift) - ONLY DEDICATED TALENT
Neutral performer with 75-video familiarity and proven commitment. Your ONLY current dedicated on-camera talent. Stop casting her in "Guess" format. Focus on Holiday/Special episodes and Gender Debate topics where her lift might be higher.
📋 RECURRING GUESTS (Tami Media Staff)
Henry (+211.9% lift): Tami Media staff with primary behind-the-scenes responsibilities. Use as RECURRING GUEST (10-20% of videos) for high-impact episodes. His +211.9% lift makes him valuable, but can't commit to 80% of videos due to staff duties.
John (-18.9% lift): Tami Media staff. Occasional guest appearances (1-2x/month max) in specific formats where he performs better.
Elizabeth (-28.9% lift): Tami Media staff. Focus on behind-the-scenes roles. Guest appearances only if specific niche content (fashion/beauty) shows improved lift.
🔍 URGENT: FIND 4 MORE DEDICATED ON-CAMERA TALENTS
Option 1: Test Extended Cast
- @tonerobaba6: 1,875 avg, 6 videos - shows promise but need more data
- Run lift factor analysis: Put him in 3-5 more videos across different topics, calculate lift
- Decision point: If lift factor > +10%, promote to core. If negative, keep as guest.
Option 2: Launch Talent Search
- Open casting call: Nigerian youth aged 18-25, camera-ready, personality-driven
- Test protocol: Each candidate in 3 videos (1 Holiday, 1 Debate, 1 Game format)
- Selection criteria: Lift factor > 0% minimum, > +20% ideal
- Timeline: Find 3 new core cast by January 2026
Option 3: Recruit From Outside
- Look for: Micro-influencers (5K-50K followers) with proven Nigerian youth engagement
- Value prop: Production support, network effect, talent development program
- Same test protocol: 3-5 videos, calculate lift factor before core commitment
⚠️ TRANSITION OFF-CAMERA
Ade (-32% lift, 79 videos)
Immediate transition to behind-the-scenes role. Focus on strategy, operations, business development.
Elizabeth (-29% lift, 71 videos)
Transition to production, editing, or operations. Alternative: ONE final 3-video test in specific niche.
John (-19% lift, 22 videos)
Transition to occasional guest (1-2x/month max). Focus on behind-the-scenes where he adds value (editing/production).
💎 THE BOTTOM LINE
REALITY CHECK: You currently have ONLY 1 dedicated on-camera talent (Semiloore, +4% lift). You need to find 4 more DEDICATED talents who can commit to 80% of videos.
Selection Criteria for New Talent:
- Minimum +10% lift factor across multiple topics (proven improvement over baseline)
- Minimum 5 videos of data before core commitment (statistical reliability)
- Full-time commitment to on-camera role (not staff with competing priorities)
- Chemistry with Semiloore (your current dedicated talent needs strong co-stars)
- Nigerian youth authenticity (audience connection is non-negotiable)
Staff Context: Henry/John/Elizabeth are valuable Tami Media staff. Use them as RECURRING GUESTS, not core cast. Their primary value is behind-the-scenes (editing, production, operations). This is not about being "mean" — it's about building a channel that can reach 50K subscribers by Dec 31, 2025.
📋 Implementation Roadmap
30-Day Action Plan: Cast Restructuring
Week 1: The Conversation (Oct 14-20)
- Day 1-2: Leadership team reviews this multi-factor analysis report
- Day 3-4: Have honest conversations with Ade, Elizabeth, John about findings and new roles
- Day 5-7: Define new behind-the-scenes responsibilities for transitioning cast
Week 2: Talent Search Launch (Oct 21-27)
- Day 8-9: Create casting call announcement + criteria (Nigerian youth, 18-25, camera-ready)
- Day 10-11: Post casting call on social media, reach out to micro-influencers
- Day 12-14: Review applications, schedule auditions for top 10 candidates
Week 3: Testing Phase (Oct 28 - Nov 3)
- Day 15-17: Film test videos with top 5 candidates (each in 1 Holiday, 1 Debate, 1 Game)
- Day 18-19: Test @tonerobaba6 in 3 more videos across different topics
- Day 20-21: Publish first batch of test videos, start collecting performance data
Week 4: Analysis & Decision (Nov 4-10)
- Day 22-24: Calculate lift factors for all test candidates after 5-7 days of data
- Day 25-27: Select top 3 performers with +10% or higher lift factors
- Day 28-30: Announce new Core 5: Henry, Semiloore, + 3 new members
🎯 THE TAKEAWAY
This multi-factor analysis revealed what raw averages were hiding:
- Henry (+211.9% lift): Tami Media staff — use as RECURRING GUEST for high-impact episodes
- Semiloore (+4% lift): Your ONLY dedicated on-camera talent currently
- Ade (-32% lift): Most screen time but worst performance — transition to behind-the-scenes
- John/Elizabeth (staff, negative lift): Occasional guests only, focus on staff duties
The path to 50K subscribers: Find 4 more DEDICATED on-camera talents with +10% or higher lift factors. Topic matters most, but talent amplifies or kills topic potential. Build your Core 5 around proven lift factors and full-time commitment, not raw averages, staff convenience, or personal relationships.