Twitter is one of those traffic sources used by almost all OnlyFans models: it has no strict rules, allows adult content, and has a large audience of users from Tier-1 countries.
Initially, we started working with Twitter to promote crypto-related content, and this was before it was renamed to "X." During that time, we weathered storms, bans, errors, and platform updates. Later, we decided to try driving traffic to OnlyFans and chose the only affiliate network specifically designed for this purpose — OnlyTraffic. This was especially important since we initially wanted to promote multiple models and scale our operation: collect as many clicks as possible and convert them into fans.
In this case study, I'll share our initial tests, scaling process, and show the approaches we used to bring in 20,000 fans and earn $6,055. You'll also find useful tips that will help you save money and avoid frustration.
First Tests
Since we planned to drive large volumes of traffic, we began our tests with mass account creation: we launched about 200-300 accounts simultaneously for publishing posts on Twitter.
This was a while ago, so we don't have much data left, but I can say that the results weren't very good: there were very few link clicks, and even fewer fans who subscribed to OnlyFans:
Everything worked like this: every 5-10 hours, 50 accounts would publish new posts with hashtags promoting a single model. We used non-nude content to prevent the accounts from getting banned immediately.
Accounts were getting banned approximately every 5-7 days, so we abandoned this approach almost immediately. After that, we began trying other methods to promote adult models.
First, we found RT-groups on Twitter, where models mutually retweet each other. Our initial manual tests showed what seemed to us at the time to be decent results, and we earned our first money on the platform:
We even managed to bring in 25-30 fans per day from a single account, but this was rare:
It was nice, but it wasn't enough. Gradually, we increased the number of accounts and models, and also created simple automation for actions in RT groups. The accounts grew, and the money started flowing in:
Scaling and Approaches We Used
After thoroughly testing and confirming that our strategy worked, we decided to scale up. The first step was rewriting our software to handle a larger number of accounts. We tracked and monitored all clicks to see which accounts performed better than others. And when everything was ready, we began hiring staff.
Among the ready-made solutions we adapted for our needs was Keitaro, which we used to cloak links with redirects to the desired traffic flow. At that time, we didn't see the point in creating landing pages, as we managed each account personally for a specific model.
The accounts rarely got banned, the clicks were good, and the conversion rate to fans was approximately 20%. Within a week, we achieved these kinds of results:
At that time, RT groups were at their peak effectiveness and delivered incredible results, but even there we found some nuances. We didn't notice any difference between groups where users had 10,000 followers versus 100,000 — the results were identical. But this is easy to explain: on Twitter, you can invite anyone to a group, there's no ban system, and you can't exclude users from the group, which means nobody controls the number of followers on accounts.
Regarding links: after about six months, we discovered that Twitter pays attention to domain names and may give less reach, or even hide posts from users' Home pages. So we started using a different domain, and this worked excellently.
Now I'll describe the strategies we used and show what results they produced.
First Strategy: 50-to-1
For this approach, you need one model account that will be considered the main account, and 50 assistant accounts. The main account doesn't participate in retweets with other participants, but only publishes posts.
The assistant accounts are in RT groups and send messages that look something like this:
👑hit my client👑
💣NOT ME💣
@mainLogin - тут указывается логин вашего мейн аккаунта
⛔ONLY MY CLIENT⛔
🍀add me to other ISO 10K+ groups pls
A single post like this could generate these kinds of impressions in just 20-30 minutes:
Second Strategy: No Name
This strategy works similarly to the first one, but targets anonymous models: photos and posts without showing faces, primarily with nude content. After clicking on the link, the user lands on a page through Keitaro. Since users typically click on the same link at least three times in a 24-hour period, Keitaro always changes the model offer for them and suggests subscribing. This approach increases conversion rates as it adapts to the user's preferences. We specifically created our own landing page for this purpose:
A single main account like this would bring in 100-150 fans daily across different models. It's also essential to clearly indicate what you're publishing. Here are the click statistics for two main accounts over a month:
The conversion rate from unique clicks, by traffic flow, to fans is about 5%-10%.
Third Strategy: Clickbait
We use this approach primarily on assistant accounts, but in rare cases on main accounts as well. After refining the landing page, we created posts where the image itself is a link, and if a user clicks on it, they're immediately directed to OnlyFans or a landing page with the model:
In just three hours, one such post generated 2,600 clicks on Twitter:
And here's what we achieved after tweaking the website settings. The post became interactive and generated more trust from visitors:
Tips: Bans, CAPTCHA, and Managing Your Main Account
The main challenges when working with these strategies on Twitter will be CAPTCHAs and bans. We've grown accustomed to bans since we launch 1,000 accounts at a time, which we register and warm up ourselves. But for most people, this will be a problem.
If you're operating only a few accounts simultaneously, you won't face CAPTCHA issues, but at larger volumes, you'll encounter them regularly. We created our own network to solve CAPTCHAs.
If you plan to run accounts with nude content, be prepared for your account to get restricted, and you won't be able to publish posts that are visible to everyone.
These posts will always be covered with a blur effect, and unauthorized users won't be able to see the account. For example, here's a post that doesn't contain explicit content, but is still restricted:
Interact with Users
Don't forget to respond to comments and direct messages: Twitter's algorithm monitors how you interact with followers, and some users who are waiting for you in DMs are ready to go to OnlyFans and spend their money there.
We automated our comment system to respond to other posts related to adult content: this significantly increases engagement with Twitter users and attracts attention. We used these types of provocative content for borderline commenting:
To find new ideas, set aside time to analyze other accounts and study their creative content. For example, here's what we found in just 2 hours of searching through adult accounts, and these posts practically beg to be clicked:
Launch Streams
Start streams on your main account, in its name: this increases engagement from both existing followers and new users. For example, we would stream audio without video: we extracted the audio track from adult content.
To launch a stream, you'll need OBS and a Twitter Premium account — this will give you access to streaming capabilities.
Here's the kind of statistics we get from our streams:
Now let's move on to the results we achieved while working with these strategies.
Results
Currently, our network consists of approximately 5,000 accounts, with a total audience of 8,000,000 users. We continue to work with these strategies, constantly improving our software and increasing the number of accounts.
To date, we've driven 20,043 fans to OnlyTraffic, earning $6,055 from them:
Use more creativity and worry less about bans, engage with your followers, and continue looking for new approaches that will bring you fans.
To drive traffic to OnlyTraffic at the same scale, you can purchase our software — the very same we used in this case study. It's designed for managing assistant accounts: the software interacts with accounts, boosts posts to the top, writes comments, publishes clickbait posts, and creates personalized landing pages.