READ MORE ABOUT ANTIQUES
⚖️ The Broken Reality of YouTube Creator Support
My Experience with the Suppression of the Antiques Arena Channel
https://www.youtube.com/@AntiquesArena
Does YouTube Shadow Ban Creator Accounts? My Recent Experience Below
Gender data shows 100% male on videos with visible female commenters.
Recommendations cluster my content with irrelevant videos or my own uploads.
Strong CTR (4–7%) and ~50% retention, but very low impressions.
Incognito searches put me ~page 300 for “car boot sale.”
Support has not escalated; I’m requesting an engineering audit + SAR.
I’m a long-time YouTube creator who’s had to ask a difficult question: does YouTube shadow ban certain creators?
Over the past few years, I’ve made a few videos that touched on topics some might call “controversial” — opinions about the state of the country, the economy, and how these changes affect the antiques trade.
I won’t repeat those subjects here for fear that even this article could be quietly suppressed too.
But what followed was a dramatic collapse in my channel’s visibility — despite strong engagement, loyal subscribers, and years of consistent uploads.
I can’t say for certain whether this is a case of shadow banning or a deeper algorithmic failure — perhaps a combination of both.
What I can do is present the facts, my data, and my full correspondence with YouTube Partner Support, so you can draw your own conclusion about what really happened.
🔹 Introduction
My name is Walter, and I’ve been running the Antiques Arena YouTube channel for over eight years.
I make real-world antique haul videos — showing the finds, what I paid, what they’re worth, and the stories behind them.
No scripts, no hype — just honest content from car boot sales, flea markets, and antique fairs.
Over time, I built a loyal following of over 41,500 subscribers, a dedicated community of collectors and resellers who enjoy the educational side of antiques.
But in recent years — especially since lockdown, when I became more vocal about certain issues (including shop closures, antique laws, and the ongoing tool theft epidemic) — my channel has experienced an alarming decline in visibility.
Despite strong engagement and healthy video metrics, my reach collapsed.
YouTube’s analytics stopped behaving logically, and YouTube Partner Support repeatedly refused to investigate.
This article documents what has happened, how I proved the faults, and how YouTube has failed to act.
🔹 Timeline Overview
- I first started noticing data inconsistencies during lockdown, around 2020–2021.
- Since then, visibility has continued to drop with each year.
- I’ve reported these issues to YouTube Partner Support multiple times.
- I’ve dealt mostly with two representatives — Alvin and Paul — both of whom have ignored clear, screenshot-verified proof of system errors.
- Despite numerous correspondences, as far as I know, no technical escalation has ever taken place.
🔹 Proven Analytical Errors
1️⃣ Gender Reporting Fault
YouTube Analytics has shown several of my videos as having a 100% male audience.
That’s not just unlikely — it’s impossible.
These same videos have numerous female commenters and members, clearly visible publicly.
This is not an algorithmic “quirk.”
It’s a data integrity error, and it directly affects how YouTube categorizes my audience for recommendations and discovery.
If the system believes my audience is 100% male, it will not recommend my videos to the large percentage of female collectors who actually watch and engage.
(e.g., “£1,500 at Malvern Flea Market – Quick Recap & Georgian Glass Haul”) https://youtu.be/aCjFivNmt68
2️⃣ Search & Discovery Visibility Failure
My channel has over 500 videos with the phrase “Car Boot Sale” in the title, description, or tags — it’s my niche.
Yet, when searching “Car Boot Sale” on YouTube, my first appearance is typically around position 300 — behind dozens of smaller or unrelated channels.
Even when using incognito mode (no login, no cookies, no personalization), the results are the same.
That means this isn’t about viewer history or preferences — it’s an indexing fault.
The system doesn’t recognize my channel as being part of the “Car Boot Sale” or “Antiques” niche, even though that’s my entire content base.
I repeated this test using multiple related search terms — “Antiques,” “Antique Tips,” “Antique Haul,” and others — all producing the same result.
To assist YouTube Creator Support, I even recorded a full-screen capture video of one of these searches, where it took almost 10 minutes of scrolling before a single one of my videos appeared.
That kind of result simply should not happen for a long-standing channel built entirely on those topics.
3️⃣ Misclassified Recommendations
Another major issue lies in how my videos are being recommended by YouTube’s system.
Analytics clearly show that my uploads are being suggested alongside irrelevant content — including things like family vlogs, gaming videos, and even yacht tours — none of which have any connection to antiques, car boot sales, or reselling.
Even worse, the system is promoting my own older videos as the main traffic sources for my new uploads. That means YouTube’s algorithm isn’t helping me reach new audiences at all — it’s simply recycling my existing viewers.
This is not how the recommendation system is supposed to work. It demonstrates a clear failure in YouTube’s content graph clustering — the process used to connect videos with related creators and audiences.
These images perfectly illustrate the problem: Not a single car boot sale or antique-related video promoting my channel other than my own videos.
Three out of five recommended videos belong to my own channel, and the other two are totally unrelated to antiques or reselling.
This proves that YouTube’s recommendation engine is misclassifying my content, misdirecting discovery traffic, and ultimately preventing natural channel growth. This screenshot came from this video. https://youtu.be/XonmcSFL8vo
4️⃣ Suppressed Impressions Despite Strong Metrics
Many of my videos perform well by YouTube’s own engagement standards:
- CTR between 4% and 7%
- Average view duration is around 50%
- Consistent likes, comments, and new subscribers
Yet, these videos receive fewer than 4% of my subscribers in impressions — meaning even my loyal audience isn’t being shown my uploads.
Example:
Video: “This English Pottery Brand Is Making a Comeback!”
- CTR: 5.9%
- Average view duration: 45%
- Total Impressions: 4.9K (after 48 hours)
- Views: ~340
With over 41.5K subscribers, it should typically generate several thousand impressions if delivery is operating normally..
5️⃣ High Retention, Low Reach (Case Study 2)
Video: “Car Boot Sale Haul: How to Score Big on a Budget!”
- Published: June 2024
- Average view duration: 7:33 (50.3%)
- CTR: 4.1%
- Total Views: ~730 after more than a year
- Impressions: ~13K
That’s textbook evidence of a visibility restriction — the content performs well but is never properly tested.
🔹 Search Data Breakdown
Over the past 28 days, my YouTube search terms analytics have shown the following:
- “Antiques Arena” – 3.6%
- “Chinese porcelain” – 0.5%
- “Carnival glass” – 0.3%
- “Royal Doulton figurines,” “flea market,” “silver ring,” etc. – 0.1% each
This means nearly all my search traffic comes from people who already know me — not new viewers discovering antique content.
That’s not how a healthy channel functions.
Normally, branded search (people typing your name) should be 1–2%, and the rest should be topic-based discovery.
Here, the numbers are inverted — proving that the channel has been de-indexed or mislinked from its actual niche.
🔹 Attempts to Resolve
I’ve contacted YouTube Partner Support numerous times.
Each time, I’ve provided clear data, screenshots, and examples.
Their responses have all been scripted and dismissive — generic explanations about “viewer preferences” and “algorithm behavior.”
No one has ever acknowledged the specific evidence.
When I asked for the chat transcript from my original support session, they ignored it.
When I filed a Subject Access Request (SAR) under UK GDPR, asking for all data and metadata related to my channel — including profiling and automated decision-making data — they still failed to respond, they now have a 30-day legal timeframe in which to answer.
There are around 20 emails back and forth with videos and images, so I can’t provide that; however, if you want to see the final complaint letter I sent to the YouTube partner program, it will be at the end of the article due to the size of the detailed complaint.
🔹 Technical Explanation (in Plain English)
YouTube’s system builds audience and recommendation links in stages:
1️⃣ Analytics data (demographics, watch behavior, interest tags)
2️⃣ Audience clustering (grouping with similar creators)
3️⃣ Recommendation testing (testing new uploads with likely viewers)
4️⃣ Distribution expansion (if testing performs well)
If the data in stage 1 is wrong — for example, saying 100% male audience — every later stage misfires.
Your videos get tested to the wrong people, they don’t click, and then the system assumes your content is weak.
That creates a feedback loop that traps the channel in under-distribution no matter how good the performance actually is.
🔹 The Human Impact
I’ve spent more than eight years building Antiques Arena.
Hundreds of videos, tens of thousands of subscribers, and a real community.
Yet I now find myself fighting a system that seems unwilling to acknowledge an obvious fault.
Each video takes time, money, and energy to create.
When the system fails, it’s not just numbers — it’s income lost and motivation crushed.
I’ve done everything YouTube asks of creators — good thumbnails, strong retention, consistent uploads, engaging content — but none of it matters if the system is broken behind the scenes.
🔹 Why This Matters to Every Creator
This isn’t just about my channel.
If YouTube’s data systems can misclassify one channel, they can misclassify thousands.
Without a proper escalation path, creators are left helpless — told “everything is working as intended” even when the evidence clearly proves otherwise.
Creators deserve transparency:
- The ability to request an engineering review when proven data inconsistencies exist.
- Honest answers, not scripts.
- Respect for data access rights under GDPR.
🔹 Evidence Archive
All screenshots and analytics included here come directly from YouTube Studio (Sept–Oct 2025).
📸 Insert compilation of:
- Audience gender screenshot
- Search terms screenshots
- Video analytics (Car Boot Sale, Pottery Brand)
- SAR email proof
All stored safely for reference and potential data audit.
🔹 Conclusion
I’m not asking for favoritism — only fairness.
My work speaks for itself, and my analytics prove the problem isn’t my content.
The system has misclassified Antiques Arena, corrupted my data, and suppressed my reach.
YouTube Partner Support has failed to act or escalate, even when presented with irrefutable evidence.
After months of silence, I’ve taken my case public — because this affects not only me, but every creator who relies on YouTube’s platform to share their work.
If you care about transparency, accuracy, and fair treatment for creators, please share this article and help raise awareness.
🔹 What Would Resolve This
After reviewing all available analytics, correspondence, and supporting evidence, I believe the following actions would fairly and transparently resolve this situation:
1️⃣ A Technical / Engineering Audit
A formal review by YouTube’s data integrity or engineering team to verify that my channel’s analytics, audience clustering, and discovery indexing are functioning correctly.
This includes:
- Validation of audience demographic data (e.g., the 100% male reporting fault).
- Review of content graph clustering to ensure the channel is linked to the correct niche (antiques and reselling).
- Examination of search indexing weights and visibility limits.
- Confirmation that no internal flags, labels, or limitations have been incorrectly applied.
2️⃣ Data Accuracy Confirmation
Written confirmation that the analytics data driving recommendations, impressions, and search ranking is accurate and complete — or, if errors are found, that they will be corrected and re-synced to the recommendation engine.
3️⃣ Transparent Communication
An acknowledgment from YouTube that my case has been properly escalated to a technical team, with a summary of findings and any actions taken.
Creators should not be left with generic replies when clear, reproducible faults exist.
4️⃣ Reindexing and Audience Realignment
If the audit confirms misclassification, YouTube should refresh the channel’s content graph so that my videos are again tested to the appropriate audiences — collectors, antique enthusiasts, and resellers — rather than irrelevant categories.
🔹 Overall Conclusion
This case is not about creative performance — it’s about data accuracy and fair visibility.
My videos meet or exceed the engagement standards that YouTube itself identifies as positive signals: strong retention, high CTR, and active viewer interaction.
Yet those same videos consistently fail to reach even a fraction of my existing subscribers or new viewers within my niche.
That is not a sign of weak content; it is a sign of systemic failure.
For eight years, I have contributed authentic, educational content to the antiques community, building trust and a loyal audience.
I’ve followed every best practice YouTube recommends — and when the data began to fail, I documented everything in good faith and sought help through official channels.
Instead of being heard, I was met with dismissive, scripted responses and no action.
All I’m asking for now is transparency, accountability, and fairness — a proper technical investigation to ensure my channel is being treated equally within the system.
If a fault exists, it can be fixed, but ignoring the evidence only damages the trust between creators and the platform that depends on them.
YouTube’s strength has always been its creators.
All I’m asking is for the system to work as intended — with accurate data, proper visibility, and equal opportunity for all.
🔹 Action Steps I’m Taking to Move Forward
Despite the frustration of being ignored by YouTube and Google, I’ve decided not to give up — but to take proactive steps to secure my future as a creator.
I’ve now opened accounts on Patreon and Rumble, where I’ll be gradually transferring my full video library from YouTube. These platforms give me greater control over my content and ensure that my work remains visible to those who want to see it.
In addition, I’ve launched membership options on both YouTube and Patreon, offering exclusive educational videos and behind-the-scenes insights for supporters.
I’m also preparing a paid promotional campaign on YouTube itself — to ensure my channel reaches real viewers through advertising, rather than relying on the algorithm. My goal is to build an audience that genuinely values what I create.
Memberships start from just £1 per week, a small but meaningful investment from viewers who believe in my work. This approach means my audience will consist of people who are not just watching — but who are engaged, invested, and genuinely interested in antiques and the world I share.
These steps mark a new chapter — one where community, transparency, and creative independence come before algorithms.
Below is my letter of complaint that I sent to the YouTube creator program.
To: Google Creator Program / YouTube Creator Relations
Subject: Formal Complaint – Ongoing Unresolved Channel Issues and Failure of YouTube Partner Support
Dear Google Creator Relations Team,
This complaint concerns long-term visibility and analytics faults on my YouTube channel, “Antiques Arena.” Verified evidence shows incorrect demographic reporting (100 % male audience despite clear female engagement), mis-targeted recommendations, and suppressed impressions despite strong engagement metrics. After months of communication with Partner Support and no technical escalation, I’m requesting a formal engineering review to verify my channel’s data integrity and audience classification.
My name is Walter, creator of the YouTube channel “Antiques Arena” (approximately 41,500 subscribers).
I am writing to file a formal complaint regarding the ongoing failure of the YouTube Partner Support team to properly investigate or resolve a serious technical issue affecting my channel’s visibility, analytics accuracy, and overall performance.
Over the past several months, I have submitted extensive evidence — including screenshots, performance metrics, and recorded demonstrations — showing clear data inconsistencies and possible system faults.
These include:
- Proven analytics reporting errors, such as videos showing 100% male audience despite numerous visible female commenters and members.
- Severely limited impressions and reach, even on videos with high click-through rates, strong retention, and active engagement.
- Irrelevant recommendation sources (e.g., lifestyle and yacht-tour videos) instead of other antique or reselling creators, suggesting a content classification error.
Despite providing detailed, verifiable data, YouTube Partner Support (mainly through representatives Alvin and Paul) have repeatedly responded with generic scripted messages about algorithm behavior, without ever addressing the specific, provable evidence I’ve provided.
No technical review or escalation to an engineering team has ever been confirmed, and requests for a full transcript of communications have been ignored.
After eight years of consistent effort, hundreds of uploads, and a growing community, I now feel completely shut down by a system that is clearly malfunctioning — or at the very least, not operating as intended for my channel.
I have done everything possible to comply with YouTube’s guidance: my retention, engagement, and CTR are strong; the only failing metrics are impressions and reach, both of which are outside my control.
I am therefore formally requesting that Google’s Creator Program or a higher-level YouTube technical team:
- Review the evidence document attached to this complaint.
- Examine the communication history between myself and YouTube Partner Support.
- Conduct an internal review or data integrity audit on my channel’s analytics and recommendation behavior.
I am confident that a review by an engineering or data team will confirm that these issues are not the result of normal algorithmic behavior, but rather a combination of misclassification and reporting faults.
Thank you for your time, understanding, and for taking this complaint seriously.
I have invested years into building this platform and community, and I am simply asking for a fair and accurate system to work with.
Sincerely,
Walter
Antiques Arena
📧 [Your Email Address]
📺 https://www.youtube.com/@AntiquesArena
🔹 SECTION 1: Verifiable Data Errors (Provable via Screenshots / Video Evidence)
1) Gender Analytics Misreporting (undisputable, screenshot-verifiable)
- Multiple videos report 100% male audience in YouTube Analytics.
- Those same videos have female commenters and members (e.g., @karencrean6278, @katielorraine2298, @meganhulatt6779, @VenusDeMilitant, etc.).
- This proves viewer demographic data is inaccurate, which directly affects targeting and recommendation systems.
- YouTube Support has not addressed this inconsistency, repeatedly giving general algorithm explanations instead.
Impact (in plain English):
If the system “thinks” my audience is all male, it won’t show my videos to a huge group of women who actually watch and comment. That’s a data classification fault, not a creative problem.
Example:
📺 “£1,500 at Malvern Flea Market – Quick Recap & Georgian Glass Haul”
👉 https://youtu.be/aCjFivNmt68
🔹 SECTION 2: Search & Discovery Indexing Failures
2) Keyword Misranking Despite a Large Catalog
- I have 500+ videos containing “Car Boot Sale” in title/description/tags/transcript.
- In YouTube Search for “Car Boot Sale,” my first appearance is ~position 300.
- Smaller channels (some <1k subs, <100 videos) show up many times before any of my uploads.
- This cannot be hand-waved away by “viewer preference” or “freshness bias,” because my channel has a deep, long-running library built exactly on that topic.
3) Suppression on Related Keywords
- “Antique Haul” (I have 21 videos): first appearance ≈ position 230.
- Even unrelated antique-niche channels rank above mine.
Impact:
The channel is de-prioritized in search indexing. My videos are rarely tested to general audiences for their own keywords. They tend to appear only when users already know (or type) exact titles.
Control Test: Incognito & No-Cookies Search Results
To rule out personalized viewing data or watch-history influence, I performed multiple search tests using incognito mode and with cookies fully rejected.
Despite this clean, unbiased setup, my channel still fails to appear for the core search term “Car Boot Sale” until roughly page 300 — even though I have over 500 videos optimized with that phrase in titles, descriptions, tags, and transcripts.
These incognito tests confirm that search visibility problems are not caused by viewer personalization, login history, or watch patterns.
They instead indicate a system-level indexing or relevance-weight error, meaning YouTube’s discovery engine is not properly linking my channel to the topic it has been built around.
In other words, even a completely neutral user (with no history) receives search results that systematically exclude my videos — proving this is not algorithmic preference but a technical misclassification fault.
🔹 SECTION 3: Analytics Errors, Distribution & Visibility Anomalies
To see if the suppression shows up in hard data, I analyzed 25–29 September 2025 (5 days with no new uploads). This is a clean, unbiased sample of existing content.
Performance Overview (25–29 Sept 2025)
- Views: 1,861
- Watch Time: 102.7 hours
- Subscribers Gained: +123
- Estimated Revenue: £8.68
Traffic Source Breakdown
- YouTube Advertising: 35.9%
- Browse Features: 19.3%
- External: 16.5%
- YouTube Search: 13.0% (critically low for a 41.5k-subscriber channel)
- Suggested Videos: 6.7%
Top Search Terms
- “antiques arena” – 3.0%
- “porthcawl fair” – 0.9%
- “chinese porcelain” – 0.4%
- “how to polish silver” – 0.4%
- “how to remove tarnish from silver” – 0.4%
Interpretation (simple):
Most search traffic is from people who already know me by name. Topic discovery (generic antique/reselling searches) is only ~1.2%—abnormally low for a channel with 1,000+ antique videos. That shows a major limitation in organic discovery.
5) Suppressed Impressions Despite Strong Metrics
Example video: This English Pottery Brand Is Making a Comeback!
- CTR: 4–7% (strong)
- Average View Duration: ~50% on a 15-minute video (strong)
- Engagement: multiple comments/likes
- Total Impressions: ~1.5k–3.7k
- Views: ~340
- Channel Subscribers: 41.5k
Plain-English reality:
With 41.5k subs, bell notifications alone should create 3–5k impressions minimum. This video capped at ~1.5k—that’s <4% of subscribers even seeing the thumbnail.
Conclusion: That’s not normal for a healthy channel and shows restricted visibility even before wider testing.
6) Subscriber vs. Non-Subscriber Ratio
Across multiple uploads:
- 82% of views = existing subscribers.
- 18% = external (Google Search, Facebook, direct links).
That 18% isn’t YouTube discovery—it’s manual traffic. There’s no meaningful Browse/Suggested testing.
Impact:
Videos are mainly shown to current subscribers only. That’s algorithmic under-distribution or misclassification.
7) Non-Relevant & Self-Referencing Recommendation Sources
Analytics show my videos being recommended by:
- Family vlogs, yacht tours, misc lifestyle content (not my niche).
- Antique/reselling channels—the correct niche—are absent.
- In snapshots, two of the recommending videos were my own uploads (self-reference).
- In the same snapshots, two other recommending videos were irrelevant to antiques/reselling.
Impact (simple):
The channel is mis-clustered inside YouTube’s content graph (treated like general lifestyle instead of Antiques/Reselling). That blocks discovery to the right audience.
In practice, this looks like:
“We’ll show your new upload to people already watching your channel, but we won’t test it to anyone else.”
Impact of Irrelevant Recommendations (Negative Feedback Loop)
Because my videos are frequently recommended alongside unrelated content such as family vlogs, lifestyle videos, or luxury-yacht tours, the people shown my thumbnails often have no interest in antiques or reselling.
When these viewers see the thumbnail and skip it instantly, YouTube records that as a “not interested” signal, which then lowers the video’s click-through rate and watch-time averages within that testing group.
This creates a self-reinforcing cycle:
- The wrong audience sees the video →
- They don’t click or watch →
- The system reads that as poor performance →
- It stops further distribution.
In reality, the issue isn’t the content — it’s that the algorithm is showing it to the wrong audience cluster in the first place.
This directly explains why high-retention, high-CTR videos still stall below 1 K views despite strong viewer satisfaction once people actually watch them.
8) Additional Supporting Evidence
Example 1 – Gender Data Mismatch (analytics integrity fault)
📺 £1,500 at Malvern Flea Market – Quick Recap & Georgian Glass Haul
👉 https://youtu.be/aCjFivNmt68
Analytics show 100% male viewers, while multiple female users are actively commenting.
This data integrity issue can also misdirect recommendations.
Example 2 – 50% Retention, Strong CTR, Minimal Reach (system under-distribution)
📺 Car Boot Sale Haul: How to Score Big on a Budget!
👉 https://youtu.be/PfAX4Lgt7yo
- Published: 25 June 2024
- Length: 15 minutes
- Average View Duration: 7:33 (50.3% retention)
- CTR: 4.1%
- Total Views: ~730 (after 15 months)
- Watch Time: ~91 hours
Why this matters:
With ~50% retention and >4% CTR, YouTube’s own guidance suggests continued testing across Browse/Suggested.
Instead, the video stalled under 1k views, showing no wider distribution. That mirrors the channel-level limitation.
Section 3 Summary:
- Multiple systemic faults: analytics mismatch, mis-clustering, and suppressed reach.
- Several videos merit ongoing testing but don’t receive it.
- The evidence points to a technical/algorithmic issue, not content quality.
Notification & Subscriber Delivery Issue (Updated Evidence)
While one recent upload (This English Pottery Brand Is Making a Comeback!) eventually reached around 4.9 K impressions within 48 hours, this only occurred after I highlighted the visibility problem to YouTube Partner Support.
Even then, impressions remain disproportionately low compared with historical videos that have stronger metrics.
For example, Car Boot Sale Haul: How to Score Big on a Budget! (50.3 % retention, 4.1 % CTR) received only 13 K impressions in more than a year — despite meeting or exceeding YouTube’s own engagement benchmarks.
This contrast demonstrates that the problem isn’t isolated to one upload but persists across older, high-performing videos.
It also suggests that even when the system does deliver impressions, it may not sustain visibility or test to the correct audience group.
Systemic Suppression Across Multiple High-Performing Videos
The performance pattern is consistent across numerous uploads: videos with excellent retention, CTR, and engagement all fail to reach normal visibility levels.
For example:
- 📺 This English Pottery Brand Is Making a Comeback! → 45 % average view duration, 5.9 % CTR, ~4.9 K impressions in the first 48 hours.
- 📺 Car Boot Sale Haul: How to Score Big on a Budget! → 50.3 % retention, 4.1 % CTR, only 13 K impressions after more than a year.
Both videos maintain strong engagement, positive viewer feedback, and watch-time above typical benchmarks. Yet neither received broad testing through “Browse” or “Suggested.”
This shows that the limitation isn’t content performance — it’s a systemic distribution cap applied regardless of results.
In effect, even when a video clearly satisfies YouTube’s own engagement criteria for expansion, the system fails to widen exposure.
The consistency of this behavior across multiple uploads rules out coincidence or short-term fluctuation — it demonstrates a repeatable, platform-level suppression pattern.
🔹 SECTION 4: Control-Period Recap (25–29 Sept 2025)
(Selected because no uploads → removes new-content bias.)
- Views: 1,861
- Watch time: 102.7 hours
- Subscribers gained: +123
- Revenue: £8.68
Traffic Sources:
- Ads: 35.9%
- Browse: 19.3%
- External: 16.5%
- YouTube Search: 13% (critically low)
- Suggested: 6.7%
Top searches:
- “Antiques Arena” – 3.0% (own name)
- “Porthcawl Fair” – 0.9%
- “Chinese porcelain,” “how to polish silver,” “remove tarnish” – 0.4% each
Impact:
Only ~1.2% of traffic is true keyword discovery. My videos are found by name, not by topic.
🔹 SECTION 5: Audience Engagement vs Reach Discrepancy
- Videos with high engagement (CTR 4–7%, ~50% retention, frequent comments) still get very low impressions (<2k).
- Benchmarks: CTR >4% = high; Retention >40% = high → these should trigger more testing, not less.
- Instead, reach shrinks after 24–48 hours → looks like an artificial cap or a misfiring feedback loop.
Impact:
Content works when shown, but the system doesn’t expand distribution. That’s a clustering/indexing problem, not a creative one.
🔹 SECTION 6: Previous Attempts to Resolve
- Multiple communications with YouTube Partner Support (Alvin & Paul).
- Despite repeated technical evidence:
- Replies remained scripted.
- No engineering/data audit was initiated.
- Escalation refused with “systems working as intended.”
- Replies remained scripted.
- I formally requested:
- Full transcript of communications,
- Confirmation of any technical checks performed,
- Escalation to Engineering / Google Creator Support.
- Full transcript of communications,
- Both requests ignored.
“Over 20 messages were exchanged with Partner Support across several weeks, yet no technical escalation or follow-up was ever confirmed.”
Data Flow Breakdown – How One Error Creates a Chain Reaction
YouTube’s analytics and recommendation engines are interlinked:
demographic data → audience grouping → recommendation testing → search weighting → overall distribution.
When the analytics data itself is wrong, every later stage inherits that error.
1️⃣ Faulty Audience Data → Wrong Audience Cluster
Example: Analytics show 100 % male viewers when many female viewers actively engage.
The system therefore builds an all-male “interest profile” for the channel and begins testing videos primarily to that skewed audience.
2️⃣ Wrong Cluster → Irrelevant Recommendations
Because the channel is now grouped with unrelated male-dominated categories (e.g., lifestyle or sports), its videos appear beside content such as yacht tours or gadget reviews instead of antiques/reselling creators.
3️⃣ Irrelevant Recommendations → Negative Feedback
Viewers in those categories ignore or skip the thumbnail, lowering CTR and retention inside the test group.
The system interprets that as “low performance” and halts distribution.
4️⃣ Suppressed Distribution → No New Data to Correct the Error
With fewer impressions, YouTube collects little new audience data, so the system never realigns to the correct demographic.
The loop repeats — the more it mis-targets, the more it “proves” its own mistake.
This is why even high-CTR, high-retention videos don’t expand: the algorithm is drawing conclusions from faulty upstream data, not real viewer interest.
It’s not a creative failure — it’s a data-integrity cascade.
🔹 SECTION 7: Overall Diagnostic Summary
Provable Faults (data-based)
- Gender metric misreporting (analytics integrity error).
- Search visibility anomaly (indexing fault).
- Recommendation misalignment (content-graph clustering error).
- Impression suppression below subscriber baseline (distribution fault).
Consistent Patterns Across Evidence
- Excellent metrics → low reach.
- “Discovery” is mostly returning viewers or manual external sources.
- No discovery expansion despite strong engagement signals.
- Support dismissed technical evidence and refused escalation.
Conclusion (plain English)
- The channel is being misclassified inside YouTube’s content graph (grouped with irrelevant niches).
- Distribution is focused almost entirely on existing subscribers.
- Search requires exact titles, suggesting indexing errors.
- Broken analytics (e.g., gender) likely corrupt targeting, compounding the issue.
Handling of Case & Lack of Resolution
Despite months of communication and extensive evidence provided, I feel completely let down by how this case has been handled by YouTube Partner Support.
At no stage were my specific concerns addressed individually. Each response simply repeated general explanations about “algorithmic behavior,” even when I presented factual proof of analytics errors and misclassification.
Instead of investigating the data I provided — or escalating the issue for a technical review — I was repeatedly told that “everything is working as expected.” This has left me with no answers, no resolution, and the sense that my concerns were being dismissed rather than properly examined.
After eight years of consistent work on the platform, this experience has been extremely disappointing. I’ve built a community around antiques education and trusted that YouTube’s support process would take creator evidence seriously. Unfortunately, that hasn’t happened here.
Human Impact & Request for Engineering Audit
I’ve now spent more than eight years building the Antiques Arena channel — over a thousand videos, tens of thousands of subscribers, and a dedicated viewer community.
Yet, because of these system faults, my work is no longer being fairly represented or even shown to the people who actively want to see it.
Every metric that I can influence — watch time, retention, engagement, CTR — is strong.
The only failing metrics are the ones outside my control: impressions and reach.
Partner Support has repeatedly stated that “systems are working as intended,” but the evidence clearly shows data inconsistencies and systemic mis-classification.
What I’m requesting is not manual promotion or algorithmic favoritism — only a technical audit by an engineering or data-integrity team to verify that my analytics, audience classification, and discovery indexing are functioning correctly.
I’ve invested years of effort and built a loyal community around antiques education and resale culture.
All I’m asking for is a fair and accurate system that reflects that work — and confirmation that my channel’s data and classification are technically sound.
🔹 FINAL SUMMARY: Combined Impact and Root Cause Analysis
After reviewing all data, analytics, and correspondence, there are now three major, verifiable system faults that together explain the ongoing suppression and misclassification of my channel:
1. Analytics Data Integrity Fault (Gender Reporting Error)
Across multiple videos, YouTube Analytics reports a 100% male audience, even though there is visible engagement from female viewers and members on those exact videos.
This is a provable, screenshot-supported factual error — not opinion.
If the system’s audience classification is wrong at the data level, then the algorithm’s targeting, recommendations, and audience expansion logic are all built on false information.
In effect, the algorithm “thinks” my content appeals only to men, and therefore does not promote it to the large number of female antique enthusiasts who actually watch and comment.
2. Misaligned Recommendation & Clustering (Wrong Audience Grouping)
Analytics clearly show that my content is being recommended by irrelevant videos such as:
- Family vlogs
- Luxury yacht tours
- Lifestyle channels
At the same time, no other antique or reselling channels (the correct niche) appear in my “videos suggesting this video” data.
Even more concerning, some of the recommending videos are my own older uploads, which means YouTube is recycling my existing audience instead of finding new viewers.
This proves a mis-clustering error within YouTube’s content graph — my channel has been grouped with the wrong audience sector, so when the system “tests” my videos, it shows them to viewers who have no interest in antiques, collecting, or car boot sales.
That not only wastes impressions but also sends negative feedback into the algorithm, reinforcing the cycle of suppression.
3. Distribution Fault (Suppressed Reach Despite Strong Metrics)
Multiple uploads demonstrate the same measurable pattern:
- High CTR (4–7%)
- High Retention (~50%)
- Strong engagement (comments, likes, new subscribers)
Yet impressions remain extremely low — sometimes below 4% of my subscriber base, and often capping around 1.5–3k impressions total.
Even if every subscriber with notifications turned on were counted, these numbers show that most of my own audience never even sees a thumbnail, meaning videos are not reaching viewers who requested notifications, let alone wider audiences.
When combined with the faulty analytics and wrong audience clustering, this becomes a systemic distribution failure, not a content or engagement problem.
4. Overall Consequence
Because of these overlapping faults, my channel is now effectively trapped in a feedback loop where:
- The analytics data is inaccurate,
- The algorithmic audience is misidentified, and
- The distribution system only tests videos to irrelevant or existing viewers.
That results in:
- Low impressions despite strong performance metrics,
- Irrelevant recommendations, and
- No discovery to new audiences in the correct niche.
In Summary (Plain English):
If YouTube’s analytics says my viewers are all men when they’re not,
and if my videos are being recommended by yacht tours and family vlogs instead of other antique channels,
and if my subscribers aren’t even seeing new uploads —
then the system itself is using incorrect data to make incorrect decisions, which in turn suppresses reach, distorts targeting, and blocks organic growth.
In closing, I want to emphasize that this complaint does not come from frustration alone, but from years of genuine dedication to the YouTube platform and the community I’ve built there.
I have always valued YouTube’s mission of giving creators a fair voice and audience, but in this situation, I feel that trust has been undermined by a lack of meaningful support and transparency.
I am respectfully asking Google’s Creator Relations or Engineering teams to take a genuine, detailed look at the data and correspondence attached.
Even a single acknowledgement that these issues are being properly reviewed — rather than dismissed — would help restore my faith in the platform I’ve worked so hard to build.
Thank you for taking the time to consider this matter seriously and for giving creators like myself the chance to be heard.
Subject: Subject Access Request – YouTube Channel Data and Recommendation Profiling
Dear Google Data Protection Office,
Under Article 15 of the UK GDPR and the Data Protection Act 2018, I am formally requesting access to all personal data that Google LLC holds in connection with my YouTube account and channel, [Your Channel URL or Handle] (for example: youtube.com/@AntiquesArena).
This request includes, but is not limited to:
- All personal data and metadata associated with my YouTube channel, including uploaded videos, analytics, and audience profiling data.
- Any algorithmic or automated decision-making data affecting recommendation visibility, promotion, or restriction of my videos.
- Any demographic categorisation or audience segmentation data linked to my channel or its viewers.
- Any internal system flags, content labels, visibility limitations, or shadow restrictions applied to my channel or videos.
- Records of all automated or manual actions taken on my channel or content, including any that affect impressions, recommendations, or search discovery.
- Copies of any correspondence, case notes, or internal investigation logs relating to my previous support cases or escalation requests.
Please provide this information in a readable, structured, and electronic format (such as CSV, JSON, or PDF).
If any data has been withheld or redacted, please explain the reason for doing so under the applicable exemption.
You are required to respond to this request within one calendar month from receipt, as per the GDPR.
Yours faithfully,
AntiquesArena
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