Original title: "Starkware Airdrop Analysis"

Original author: KERMAN KOHLI

Original translation: Frost, BlockBeats

Editor's note: Crypto researcher KERMAN KOHLI analyzes whether Starknet's airdrop was successful from the aspects of claiming and issuing Starknet airdrop tokens, data and time.

Following my previous article on Optimism's multiple airdrops, I wanted to take a look at Starknet's airdrop because I pulled data at the same time. I hope to explore the main differences in the token claim mechanism by studying the two token airdrops of Starknet and Optimism. The data is now about a month out of date, but considering that the airdrop was completed a few months ago, it is not too far from the actual number.

Claim vs. Grant Model

The main difference between the two approaches is that Optimism says “we will personally deliver the airdrop to your wallet”, while Starkware says “come to us to claim your airdrop”. In the case of the former, it is easier for the user and saves gas. My personal philosophy is that if you do this on a low-cost chain, then cost should not be an issue and there should be a claim button.

That being said, let’s take a look at the Starknet airdrop. Unfortunately, getting data is very difficult because:

Starknet’s analytics data after the airdrop does not publicly report details of claiming behavior.

Starknet does not have a standard EVM format address, which means I had to hack to get data available on the chain.

Anyway, here’s the official chart on how the airdrop was distributed:

Data Collection

To get the data I needed, I basically used:

0x06793d9e6ed7182978454c79270e5b14d2655204ba6565ce9b0aa8a3c3121025 as my airdrop acquisition address.

0x00ebc61c7ccf056f04886aac8fd9c87eb4a03d7fdc8a162d7015bec3144c3733 as my starting block hash.

0x04718f5a0fc34cc1af16a1cdee98ffb20c31f5cd61d6ab07201858f4287c938d as the contract to get the STRK balance from.

I had to go through a lot of for loops and byte programming to get some interesting snippets of the data I wanted.

Anyway, when extracting the data, I found that only 39.8% of people received the airdrop, and the rest of the users were basically used as marketing data - in a sense, this is also a good result! Some people may say this is bad, but if you can convey the information to the widest range of people without revealing everything, then you have found the sweet spot.

Analysis time

The approach I took was to extract all the addresses that had received the airdrop, and then write a script to query the balance of these addresses at the time (that is, when the script was run). By dividing the balance into "bins", I can see the amount of balance distributed in different "bins". But due to the limited data information, it is difficult to get a deeper understanding of these users. The limited data makes the entire analysis more challenging.

Without further explanation, here are the results! I set a threshold value no higher than 100 STRK, because the minimum airdrop amount is 111.1 STRK. Here’s how the different amounts are distributed:

StarkEx users: 111.1 STRK each

Open source developers: 111.1 STRK each

Starknet users: range from 500 to 10,000 STRK, with different multipliers

Starknet community members: range from 10,000 to 180,000 STRK

Starknet developers: 10,000 STRK each

Ethereum staking pool: 360 STRK per validator

Solo stakers: 1,800 STRK per validator, up to 3,200 STRK for higher risk validators

Ethereum developers: 1,800 STRK per person

Concierge guild members: 10,000 STRK per person

EIP authors: 2,000 STRK per person

Overall, this airdrop did not achieve a very good result! The retention rate of 13.5% is close to the industry average (which is not high). However, considering that ordinary GitHub users like me received 1,800 STRK, from a deeper perspective, this airdrop did much worse than we expected! Only 1.1% of users who received token allocations ended up retaining them. Let's look at some other metrics to help us judge whether this airdrop was successful.

A simple proxies metric is the price action of the token. Here is a chart of the price action of the STRK token over the past 3 months:

The price is down 50%, but the market as a whole is undergoing structural adjustments during the same period. Not great, but at least not down 90%.

Let’s look at it from another angle: TVL. At least our friends at DeFi Llama can help with that.

TVL rose to around $320 million and then fell to around $210 million, which is a pretty good retention rate. However, we don’t know how much Starknet paid to get these numbers. Luckily I have the numbers. That number is 67,078,250.942674.

If we assume an average token price of $1.50, we can re-express the equation as Starknet spent $100,617,376 to acquire about $300 million of TVL, or in other words, about $3 of STRK tokens can buy $1 of TVL

My next question is what the number of users is so that we can understand the CAC model for this equation. I re-plotted the chart above with a percentage of the number of users.

Okay, let’s start by giving Starknet a run for their money from here, considering only the “under 100 tokens” tier. They spent almost $100 million to acquire 519,282 users. That means they spent about $200 per user. If we recalculate using retained users (those holding>101 tokens), the burn per retained user would be $1,341.

This is lower than what we’ve seen in the Arbitrum airdrop and other airdrops where the retained CAC was in the thousands or even tens of thousands of dollars. While the Starknet airdrop wasn’t great from a retention perspective, from a CAC perspective it was pretty good relative to other airdrops I’ve seen. My paper is similar to what we saw in optimistic airdrops: allocating tokens based on diversified attribute criteria can reap rich rewards

End

Starknet has taken a relatively thoughtful approach to how to allocate large amounts of tokens to different groups. The data also clearly shows that they ensured diversity in allocation. This is also a common feature I observed in successful airdrops and failed airdrops.

So why don’t more projects take into account the diversity of user attributes when conducting airdrops? The reason is that collecting, analyzing data and drawing conclusions is a very difficult task - especially when the amount of data is large. However, Starknet used a relatively simple standard and still ensured diversity in allocation. In fact, with the right tools, allocation can be more targeted.

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