Native Reddit Mentions for a Crypto Exchange

Results of the Reddit Marketing

There’s really only one metric that matters here: how many times AI assistants name the brand when answering user queries. Results are anything but instant (a thread first needs to get indexed, start ranking, and only then start showing up in AI outputs), so we ran our control measurement 3.5 months after launch. Citation count grew 4.4×, and 92% of the 125 placements survived moderation on financial subreddits.

Who is Our Client?

Our client is a crypto exchange serving retail users in the UK. The product is standard for the space: spot trading, GBP deposits and withdrawals tied to UK banks. The company sees its main edge in payments – GBP transfers go through fast, and the exchange has managed to build relationships with banks that typically block crypto transactions outright. 

Client acquisition was a different story. Advertising crypto products to a UK audience requires FCA registration, and most platforms either reject these ads outright or flag the account for manual review. Paid traffic was essentially off the table as a growth channel. That left organic search and AI search, and that’s exactly where the client decided to focus, since the brand wasn’t showing up at all in AI answers to questions about choosing a crypto exchange in the UK.

What Did We Agree to Do?

The project started with 25 mentions. Since the client was trying this kind of service for the first time and wanted to see whether placements in UK crypto subreddits could even survive moderation, they went with a $525 starter package. That worked for us too in a niche with strict moderation; it’s the cheapest way to test a channel on a real product. The plan was locked in as follows:

• Establish a baseline for the brand’s AI citation rate across 100 prompts before starting work.
• Place native, link-free mentions in specific threads matching those baseline prompts.
• Prioritize evergreen threads that keep ranking and getting cited for months.
• Select subreddits for a UK audience, factoring in each financial community’s moderation rules.
• Report on every placement, with an extended 3-week replacement guarantee and a follow-up measurement against the same prompts.

Three weeks in, the client got a report on the 25 placements, saw the comments were alive and pulling organic replies, and by week four upgraded to a 100-mention package. Total project scope grew to 125 placements, but the task list above stayed exactly the same, just at a larger scale.

How Did We Measure AI Citations?

Before the first placement went live, we established a baseline. Without it, there’d be no way to prove results by the end of the project. Here’s the methodology:

1. We compiled 100 prompts that a UK user would actually type: which exchange is best in the UK, where’s it cheapest to withdraw GBP, which one doesn’t get blocked by my bank.
2. We added a list of 4 competitors to track their standing and the client’s presence relative to them.
3. We ran every prompt through ChatGPT, Perplexity, Google AI Overviews, and Gemini separately, since each pulls from different sources and returns different results.

That worked out to 400 checks per measurement round. Each prompt was run multiple times. AI systems are non-deterministic, and a single response proves nothing: a brand can show up in one list by chance and disappear on the next run. At baseline, we counted 18 brand mentions.

Our Step-by-Step Process

1. A 25-Mention Test

The first stage cost $525 and delivered 25 native mentions. We counted a placement as successful if the comment survived moderation and pulled organic replies.

The real value of this test was what it revealed about the channel itself. In under two weeks, it became clear which communities would let a product mention through, which ones auto-removed it, and just how actively British users engage in crypto discussions.

2. Selecting Subreddits  

Out of a shortlist of 17 communities, we kept 10. We filtered them based on moderation rules – the main thing that sets financial subreddits apart from any other kind. Some communities cap how often products can be mentioned, some auto-remove anything that reads as vendor talk, and a few had rules that new accounts can’t mention commercial services at all. 

3.  Matching Threads to Baseline Prompts

By this point, we already had 100 prompts and 10 communities, so the task came down to matching one to the other. For each prompt cluster, we looked for threads where people were actually discussing that exact question: withdrawal fees in GBP, which banks don’t block crypto transfers, which exchange is more convenient for a UK user.

AI systems don’t cite websites or profiles. They cite specific discussions. So getting into the right thread matters far more than just having a presence in the community. A thread with a thousand upvotes that’s off-topic makes for a nice screenshot in a report and does absolutely nothing toward the actual goal. That’s why we deliberately went after less popular but intent-accurate discussions instead.

4. Focus on Evergreen Threads with a UK GEO

Fresh threads look appealing. It is an active discussion, a comment seen by hundreds of people within hours. But a week later, that thread is forgotten. For AI visibility, that’s pointless, because these systems need time to index a thread and start citing it, and by then newer discussions have usually already lost relevance. An evergreen thread works the opposite way: it keeps pulling in readers for years and stays a source AI systems lean on when answering questions. Those are the discussions we prioritized. 

A lot of people assume that targeting the UK market means only posting in UK-specific subreddits. In practice, on Reddit, geography is defined by the topic of conversation, not the sub’s label. No one from the US is going to jump into a discussion about UK bank withdrawal limits or GBP transfer blocks. The topic filters the audience on its own. So we had no problem posting in global communities too, establishing the UK context right within the comment itself.

5. Getting Thread Lists Approved

After the test package, the client also wanted to see the process. We selected threads using the criteria from the previous steps and sent them to the client for sign-off. This added a step between selection and placement and stretched out the timeline a bit. But it also removed the client’s fear of the brand showing up in a conversation it had no business being part of. In finance and crypto, that kind of caution is normal. A reputational misstep costs a lot more than any time saved by skipping the review.

6. Reporting, Guarantee, and Replacements 

For every placement, we logged the thread URL, a direct link to the comment, a screenshot, and its current status. The client could open any row at any time and see exactly how the comment looked in the discussion.

The client opted for the extended 3-week guarantee instead of the standard 1-week one, so we ran our follow-up check on day 21. Usually, if a placement survives seven days, the odds of it getting pulled after that drop sharply. Moderators either act fast or don’t act at all, and auto-moderation kicks in within the first few hours. Over the course of the project, we replaced 10 mentions out of 125. At the follow-up check, 92% of placements were still live.

Project Takeaways

A month and a half after the last placement went live, we ran the measurement again: the same 100 prompts, the same four systems, no changes to the wording. The brand’s citation count grew from 18 to 79, and 23 of the new citations link directly back to threads we worked on. Reddit promotion worked exactly the way we’d expect it to in the crypto niche: no links, no ranking promises, just a verified before-and-after.

Nik Zakharchenko, CBDO of NeedMyLink

Reddit stopped being just a forum the moment AI systems started leaning on it for product recommendations. That changes the whole logic of our work. It used to be about traffic from a thread. Now it's about whether that discussion gets cited when a user asks their question. We still can't guarantee a brand lands in any one specific answer, because AI is unpredictable. But we can guarantee the brand will be present in the pool these answers are getting pulled from.