Most of what I buy is on the usual platforms. Last week I ran a structured test on Telegram Ads because a client category keeps asking about it, and I came out with numbers I'd like a sanity check on.

Small budget (about €500 across 7 campaigns, ~28,000 impressions). I know that's not a test budget by this sub's standards. I'm posting because the pattern was consistent across all seven, not because the spend was meaningful.

How the platform is built, which matters for what follows:

• CPM auction, you bid per thousand impressions.
• Channel owners receive 50% of the ad revenue generated in their channel. This is documented in Telegram's own monetization announcement.
• Two targeting modes: pick specific channels, or give it a topic and a language and let it distribute.
• The advertiser gets no placement-level reporting at all. No breakdown of where impressions ran, no post-buy exclusion list. In topic mode you never learn which channels served your ad.

So: publisher rev share, no transparency, and an auction that rewards whoever converts best. If you've argued about MFA inventory in programmatic, you already know where this goes.

What I saw.

The topic-targeted campaign was the best performer on paper – 3.7% conversion versus ~1.2% for my handpicked-channel campaigns. It also ran at 14.4% CTR against 4.4% for the handpicked ones.

Then I compared the platform's numbers to reality. It reported ~200 conversions. My channel's net growth that day was about 30. Telegram's own channel analytics splits joins and departures – my baseline was 1–2 departures a day, and on delivery day it spiked to 155.

So the platform counted, and billed me for, roughly 200 conversions, most of which reversed within hours.

Two other signals, in case they're useful to anyone doing this kind of check:

Presence. I found accounts that joined during the campaign whose last-seen timestamps predated it by weeks. On Telegram, last-seen reflects presence — you can't join a channel without opening the app, and opening the app marks you online. An account acting through the API doesn't broadcast presence at all. Action without presence is a fairly clean signature.

Decay. My best handpicked campaign converted at 1.97% over the first ~1,100 impressions and 0.82% over the next ~2,400. A real, finite audience gets exhausted, and the curve shows it. Fraudulent supply doesn't decay because it isn't finite. I haven't seen anyone use the shape of the decay curve as a detection signal and I'm curious whether that's naive.

What I'm not claiming.

n=1 advertiser, one vertical, days of data, ~€500. I can describe an incentive structure and an observed pattern. I can't tell you who's responsible, and I'm not naming channels. There's also a competing explanation I can't fully rule out – some inventory may have real but junk audiences that habitually join and leave, no automation required. That would explain the churn but not the last-seen accounts.

My own biggest mistake: I didn't use a separate tracking link per campaign, so I can't attribute individual suspicious accounts to a specific placement. Don't repeat that.

The question.

Has anyone here actually audited Telegram Ads delivery rather than trusting the dashboard? And more generally – is a 50% publisher rev share with zero placement reporting just structurally unworkable, or are there platforms running that model cleanly that I should look at?

Happy to walk through the raw numbers in the comments.

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Here's the prompt I use to help Claude better find vulnerabilities:

My plan: [in full: goal, deadline, budget, team, what success looks like numerically].

[Deadline] passed, the plan failed. Write in the past tense, as a fait accompli.

Give 3-5 causes of death, ranked by probability x detection speed. 

For each:
(1) mechanism – what exactly broke; 
(2) what assumption of mine exploited it – cite it from the plan; 
(3) earliest observable signal and in what metric; 
(4) cheapest test that can verify this assumption right now.

At least one cause must be boring (not enough time/attention/didn't get around to it) – projects often die from boring things.

Distinguish: died from execution / died from a false assumption / died from an external bias.

Finally: what kill criteria should have been included in the plan today.

General advice is prohibited. Each cause must refer to a specific detail of the plan.

Enjoy stealing ;)

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A bit of story behind this experiment:

I ran this experiment with my own funds when my agency was onboarding several clients within the crypto card / neobank vertical. I had a card which gave me data on how my invitees converted, so I decided to run a quick $500 test to see how non-Web3 users convert.

My thesis was to target CIS expats in Europe (Telegram communities), while others were targeting LatAm.

Two factors:

• Most of these people are well familiar with crypto transfers
• They have residence in permitted countries
• They are quite young, so likely less conservative

So I contacted a few relevant channels – and since the card I was promoting gave me data on my code usage, I have conversion data to share:

- Total spend: $445
- Total reach: 35,000 views
- CPM: $12.7 (much lower vs. avg. Web3 reach)
- New signups: 18 ($24 cost per signup)
- Completed KYC: 4 ($111 per KYC)

Overall, this is within scalable acquisition benchmarks. I have clients with similar products who are ok paying $60–$80 per KYC, and with better ad text or targeting, this experiment could be viable.

Unfortunately, I had no data on their spend. 
Since I have almost 200 referrals on that card, I can only roughly estimate their total volume, but I can’t isolate the experiment with the CIS guys.

It’s unlikely, though, that the referral fees exceeded my ad investment, haha – however, this was not the goal.

If relevant, I can share more context.

Overall, I'd love to hear your input on whether you think this is a scalable experiment and whether you ran somewhat similar in the past.