B0rg🌱

These guys are so good, hackers started botting them to get them delisted. If you have 5figs+ in crypto they are a non brainer. 5 devices per lifetime licence, an absolute steal considering you can re-sell the NFT. Not paid promo, just a happy customer.

The attack (or a different one?) continues now on @X. Our pinned tweet has now been botted with 6,000~ fake likes. It's a shame because we pride ourselves in our 100% organic following and growth. We thought it could be a competitor, but we didn't consider this could be scammers angry about our growth trying to stop us. If you or anyone you know knows how to take down Google Chrome Store fake reviews and/or know someone on X who can help, please DM us.

Today is my 40th birthday. Here are all the things that I know at 40 which I wish I knew at 20. Starting with… 1. Your relationship with others is a direct reflection of your relationship with yourself. If you treat yourself poorly, then you will unconsciously seek out and tolerate others who treat you poorly. If you treat yourself with dignity and respect, then you will only tolerate others who treat you with dignity and respect. Get right with yourself. Get right with the world.

The Arbitrum airdrop pushed the ecosystem notably, leading to a 4x increase in user acquisition for 3 months. But these improvements have not lasted long and there was no improvement in user retention either. Here we analyse of the airdrop and present some ideas to improve it Airdrop criteria The eligibility for the Arbitrum airdrop was determined via a point-based system, where users could earn points based on their on-chain activity. Once they reached a specified threshold of points, users became eligible for the airdrop (check out the original nansen for further details) The analytics team identified what actually generates value on their chain. For that, they used a first-principles definition of activities, rewarding or penalizing different behaviors and collected on-chain data related to those activities to provide a data driven approach. Effect on user acquisition To understand the impact of the Arbitrum airdrop, we need to compare its results with similar projects, pre and post-airdrop. Originally, Optimism and Arbitrum had a comparable number of new users each month. This trend changed considerably at the time of the airdrop, when Arbitrum quadrupled its user acquisition, with numbers almost comparable to Polygon. This lasts for ~3 months, and then it slowly decays to match again the numbers of Optimism. Effect on user retention From the cohort analysis from Tokenterminal, we can analyze the percentage of users returning to the project each month segmented into pre-airdrop, during-airdrop, and post-airdrop periods. The data reveals the following: 1. During the months of the airdrop, many users returned to Arbitrum, boosting the retention rate. 2. However, the project couldn't maintain those figures post-airdrop. More importantly, the retention rate after the airdrop was considerably lower than before. 3. This behavior is not seen in Optimism, suggesting that its airdrop might have attracted or impacted lower quality users than usual. As a result, they used the network but only for a brief period. Thoughts Several questions emerge from this, but two, in particular, resonate with us: 1. Could a project prolong the effect of the acquisition boost over time by offering continuous rewards rather than a one-time airdrop? 2. Can a project maintain the retention rate observed during the airdrop by offering continuous rewards? 3. Is there a way of improving the commitment of users who benefit from the airdrop? The truth is that, given how the airdrop criteria were designed, the Arbitrum team successfully amplified activity on the platform during the airdrop period. Nevertheless, the airdrop results show that none of the airdrop criteria directly improve the likelihood of someone staying on the platform. Engagement & churn Modeling In contrast, modern web2 consumer companies diligently monitor their clientele and devise their churn models, an analysis that we ourselves have done in the past. This approach allows companies to predict which users are likely to leave the platform or which ones will depart regardless of any intervention. Perhaps, merging the analysis of the value added in the past with the probability of returning for a given user is the key to optimal incentivization. We could decrease the number of tokens distributed to users unlikely to contribute to the platform in the future. This more token-efficient approach might enable not just one, but perhaps three or four airdrops. This scenario is advantageous for everyone: 1. Users genuinely adding value to the protocol are rewarded over time, not just once. 2. The project extends the influence on user acquisition. 3. The retention rate remains stable post-airdrop, while still benefiting from the airdrop's initial surge. 4. The project creates a habit in users, increasing their propensity to return in the long run. In conclusion, even though the airdrop has been a success, there is room for improvement in terms of long term effects. Applying the traditional user modeling approach with machine learning can help. Hope you enjoyed this post, if you want to go in detail on the methodology, check out the original post on our site! PD: If you are a company looking to optimize your reward system, contact us!