Regarding the Recent Algomint Incentive Reward Program
I am Herry Xu, the CTO of Algomint. We’re building asset bridges to Algorand for users to come and explore this super fast, low cost, and green blockchain.
There has been a lot of discussion in our social channels about an error in our user data regarding reward incentives and I wanted to speak directly about the issues we’ve faced and share how we’ve addressed them + the lessons learned.
Lifting the hood, here’s a look at what’s been happening behind the scenes here at Algomint.
First, a detailed look at the issues we’re here to address.
When calculating the eligible users for the first Twitter post, incorrect eligibility parameters were used to query data from our database resulting in inaccurate data. As it turns out, more people bridged than we had initially stated, which diminished the individual reward total. This was a disappointing outcome for our early supporters, who had expected to receive a higher number of $ALGO rewards than the actual.
Transparently — the data seemed a little odd when we saw the low user number, but this announcement was off the back of an AMA we organised where we had verbally offered a different estimation of the data. In our haste to correct a “mistake”, we made another.
Further — the design of the incentive reward has motivated a high number of users to use the product, yet it did not achieve its other goal of bringing in a high TVB to benefit all eligible users. We wanted to spread rewards in what was designed to be a ‘fair drop’ to as many users as possible. What we did not anticipate is how many users would bridge the minimum amount meaning lower TVB than expected and less rewards per user.
These mistakes were compounded further by our own operational constraints — we learned the hard way that we need to lean more heavily into automations to keep our team available to respond to sudden influxes of enquiries from a fast growing community.
So, here’s what we’ve learned.
First and foremost, we’ve learned that building trust is equally as important as building a great technical product.
One of our community members mentioned the importance of ‘cutting once, measuring twice’ — this resonated deeply with us. We know how much rewards mean to early supporters, and we certainly do not want to disappoint our community. We will always strive to apply a more thorough sense check going forward, ensuring all data is accurate before disclosing to the public.
We’ve learned that we need to prioritise the ease of data retrieval in product design, so that data availability won’t compromise our ability to deliver accurate information in a timely manner to our fellow team members and the community.
We’ve been reminded how important it is to establish productive channels of communication between our team and our community. We set up a Google Form (it is still live, and can be found here) to collect detailed feedback from anyone willing to provide it, and have spent considerable time reviewing the thoughtful responses. We’ve also added an additional timeslot to our team’s weekly strategy catch up to review community feedback to ensure that real-time analysis of in-market feedback remains a priority for us.
So, on to next steps — we’re incredibly grateful for the support of our community and we know how valued rewards are for those who participate, and we will be designing the next phase of the incentive program taking into consideration all the great learnings we’ve had so far.
We’re also available for anyone who wants to get in touch personally to share their experience with us, either through the support form of the Algomint website or speak with the admins of the Telegram group.
We want to express special gratitude towards those who filled in the feedback form — we read through every line and have learned so much about where we need to improve. We have also been extremely grateful to find out that the vast majority of the community are supportive and acknowledged our efforts while giving constructive feedback.
Thanks again for all of your support — we’re so passionate about the Algorand ecosystem and we’re determined to put our user’s needs at the centre of our efforts.