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MixieAI’s path to thousands of likes per post
Snag Solutions
Jun 26, 2024
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4 min read
In an increasingly competitive gaming industry with limited users MixieAI was in need of a solution to gather the attention of their target customer by increasing their reach in Web3 gaming. They reached out to Snag to help them create an engaging platform aiming to cultivate a a tight knit community of AI + Gaming evangelists.
https://universe.mixie.ai/loyalty
What they were looking for
MixieAI was looking to scale authentic user engagement across their social media channels, with X as the main channel. Their primary goal was audience growth. X was filled with low effort ‘airdrop farmers’ who like and unlike posts, or comment gibberish, leading to fake engagement Many projects failed to gauge user quality negatively impacting their business.
Per Mixie’s request Snag created multiple applications to gauge user quality, including the use of AI in anti-sybil. The first method to guarantee a user completed their quest was tracking their click path when they enter the site (X in this case) and if the user doesn’t complete a specific action (i.e repost) then they are not awarded with points. The second being using the X API to track likes, reposts, and comments more frequently which allows for greater scale. Snag also set up anti-sybil guardrails and rules that would allow MixieAI to further count on the quality of their data and overall point program.
Additionally, the X API unlocked capabilities not available with tracking click path. Including ‘Pacmoon’ like quests, requested by many, to reward content creation by distributing an outsized number of points to posts liked by the main project’s X account. Another rule only available using the X API is rewarding the use of a specific hashtag across X.
Scaling Point Programs
As we built out loyalty for Mixie we invested in robust architecture to ensure the program scales with peak site traffic. Snag was able to provide MixieAI an all-in-one platform to create, manage, and delete rules based on what is & is not working to best optimize their program, and by building the program this way are now able to build new loyalty platforms in a matter of days.
Not only is it extremely easy to create new rules via the Snag backend but Mixie was able to access all loyalty user data via the Snag API. Due to the architecture of the Snag API we’re able to integrate additional platform data (i.e Zealy, Galxe, or any custom platform) as an additional layer to partner point programs to incentivize authentic contributors.
The Results
Only a week after program launch Mixie started seeing exponential growth. They had started off averaging <100 likes/post with 2k likes/post being the most recent benchmark. Mixie also created direct quests to follow their core team, and since K2 has seen a 6x increase in follower count (2k → 13k) and David, Mixie’s CIO, is sitting at 34.8k X followers.
Most importantly, these numbers are sustainable. Due to the anti-sybil guidelines and the intricate ruleset set up for Mixie they’ve been able to to cultivate an authentic user base of 80k+ by growing their audience >100% over 2 months.
Conclusion
MixieAI came to Snag looking for a solution to improve user engagement while targeting real users on a platform filled with bots and financially incentivized actors. We were able to provide a robust point program, with anti-sybil protections, that allowed for the engagement and scalability of Mixie’s community.
We’re excited to continue helping MixieAI incentivize real user growth over the coming seasons with upgrades to their loyalty program, from point spend to new ways to engage.
