Unclaimed Airdrops and Network Revenue

This is a technical note, intended for researchers and technical teams, especially those designing airdrops. This work uses the Hyperline blockchain analytics platform and the Octan algorithmic reputation score.

In our recent article, "Onchain PageRank as Predictor of Future Revenue," we described a surprising property of Arbitrum Airdrop #1: A small percentage of active addresses were eligible according to the airdrop targeting, but did not claim the airdrop. Despite being only 1.5% of active addresses, they contributed 10.2% of revenue in the year after the airdrop.

Designating this cohort as “unclaims”, as contrasted with eligible and claims, the following table illustrates the behavior. Claims and unclaims partition the eligible set.

Claims, unclaims, and eligible for the Arbitrum Airdrop #1. The population is the 2.5mm addresses active before the snapshot block 58642080. Revenue is computed between the first claim, in block 16890400, and a year later, block 103061189
Claims, unclaims, and eligible for the Arbitrum Airdrop #1. The population is the 2.5mm addresses active before the snapshot block 58642080. Revenue is computed between the first claim, in block 16890400, and a year later, block 103061189

The contribution of first year revenue from unclaims is larger than would be expected from the size of the cohort. In the following visualization, the entire width of the bar is the eligible cohort and the light blue bar is the unclaim cohort.

Outsized contribution of unclaims to revenue. The light blue bar is the unclaim cohort.
Outsized contribution of unclaims to revenue. The light blue bar is the unclaim cohort.

Note that the outsized contribution of the green bar to revenue is expected and desired. This implies that the airdrop claimants were contributing network participants, as desired. However, the unclaim cohort was even more efficient to revenue than the claiming cohort.

This is unexpected. Addresses which claimed the airdrop would reasonably be expected to be contribute more than addresses which did not claim the airdrop. The opposite is true.

Previous articles have applied a pagerank-based reputation score, similar to that published by Octan Network. These results show that the top 22.4% of addresses by pagerank are more revenue efficient than the 22.4% of addresses who claimed the airdrop. However, comparing the next 1.5% of addresses by pagerank to unclaims, the unclaims again demonstrate extreme revenue efficiency.

Comparing pagerank targeting for Arbitrum Airdrop #1. Pagerank targeting is more revenue efficient for the claim cohort. The unclaim cohort has an outsized impact on revenue.
Comparing pagerank targeting for Arbitrum Airdrop #1. Pagerank targeting is more revenue efficient for the claim cohort. The unclaim cohort has an outsized impact on revenue.

An interesting observation is that the unclaim cohort has many high outliers. A boxplot of the first year revenue illustrates:

Boxplot of claims vs unclaims first year revenue. Circles represent observations greater than Q3 + 1.5 IQR
Boxplot of claims vs unclaims first year revenue. Circles represent observations greater than Q3 + 1.5 IQR

Comparing the mean first year revenue to the median also illustrates the impact of outliers. The outliers cause the mean to be much higher for unclaims, while for the median, the reverse is the case.

Mean revenue, Claims vs Unclaims
Mean revenue, Claims vs Unclaims
Median revenue, claims vs unclaims
Median revenue, claims vs unclaims

Properties of the Unclaim Cohort

The highest value addresses in the unclaim cohort are shown below.

First year revenue from high-value addresses
First year revenue from high-value addresses

These addresses show bot-like behavior on Arbiscan. They have more transactions than would normally come from a human, and the transactions are more frequent and constant than a human would produce.

Arbiscan transactions for 0xbd620be125abf8b569b9a3cc132aad0bcf1ff0e7
Arbiscan transactions for 0xbd620be125abf8b569b9a3cc132aad0bcf1ff0e7

This, together with the outlier analysis in the previous section, provide evidence that the outsized impact of the unclaim cohort is due to bots included in that cohort.

Transactions per minute provides one useful criterion for differentiating bots from humans. Here, we can define tx/min as:

count of transactions / (final transaction time - first transaction time)

where the set of transactions is taken over the year following the airdrop.

If we exclude the top 25% of addresses with high tx/min, we find that the outsized impact of the unclaim cohort is greatly reduced:

Cohort comparison over addresses active before the airdrop snapshot, but with fewer than 8.4e-4 tx/min during the year following the airdrop. The unclaim cohort has a revenue efficiency more in line with expectations.
Cohort comparison over addresses active before the airdrop snapshot, but with fewer than 8.4e-4 tx/min during the year following the airdrop. The unclaim cohort has a revenue efficiency more in line with expectations.

Summary and Conclusion

The unclaim cohort shows a suprising contribution to first-year revenue. This is explained by bot-like behavior among the highest contributors in the cohort. Eliminating addresses with very high transaction rates causes the revenue contribution from this cohort to be inline with expectations.

Airdrop targeting is traditionally aimed at human users, and the bots are a confounding factor in comparing targeting methodologies. While this does not provide prescriptive guidance for eliminating bots from an airdrop or an onchain analysis, it does demonstrate that the outsized impact of the unclaim cohort is likely unimportant when comparing the historical airdrop to algorithmic pagerank targeting.