Final Grant Submission - Wallet Segmentation Analysis by FDD workstream of Gitcoin


A wallet segmentation analysis provided for Aave Grants DAO

The final presentation slides and documentation can be found in PDF and Google doc format on Github: GitHub - Fraud-Detection-and-Defense/aave-wallet-segmentation: A wallet segmentation analysis provided for Aave Grants DAO

Aave Grant Final Submission


Intro / TL;DR

The Fraud Detection & Defense workstream at Gitcoin applied and was approved for a grant to perform a wallet segmentation analysis for the Aave ecosystem. Using their unique understanding of onchain behaviors and analysis provided by 2 years defending Gitcoin from Sybil attacks, they put their team to work understanding the user groups & personas which interact with Aave contracts.

  • The analysis clearly identifies 13 user personas
  • The 13 personas are bucketed into 3 categories: Testers, Income, & Special Cases

Recommendations for insights

  • Improve targeting & copy for marketing & to focus on revenue generating users
  • Use to qualify participants in product research
  • Consider custom experiences for highest value user personas*

Potential to build on this work

  • Open source work can be built on by the community
  • Consider time based behavioral analysis and retention analysis
  • Create a system for identifying how this analysis is used to measure impact
  • Run a targeted campaign to incentivize qualitative feedback at scale

Data Discovery & Cleaning

  • Previous Aave grant had already documented all data sources
  • Contract calls: deposit, supply, borrow, repay, withdraw, redeemunderlying, liquidationcall, flashloan
  • Chains: Arbitrum, Avalanche, Polygon, Fantom, Optimism, Ethereum
  • Versions: 1,2,3
  • Exogenous data sources: Lens, Snapshot, Trustalabs, Gitcoin, Debank
  • Removed outliers, reduced sample size, merging of function calls
  • Profiling based on 114,915 wallets available on mainnet for data availability
  • Histograms: by chain, by event, by version
  • Scatterplot matrix of all variables endogenous & exogenous


aave_clustering_13_2D_map (1)

  • Non-linear dimensional reduction: t-SNE & UMAP
  • Manual parameter search looking for meaningful separation
  • Visual investigation of 3-Dimensional feature space
  • Plotting multiple 2 dimensional projections of UMAP space
  • Linking graphs with an interactive table to track cluster averages
  • Manual brushing to investigate pairs of clusters for cohesion, compactness, & quantitative distinctness
  • Reviewing the final cluster solution in 2D & 3D space

Results (Personas)


  • Quantitative review of the group mean vectors using color g* radient for examination
  • Parallel coordinate plots to visualize segments in multiple dimensions
  • Personas created based on behavioral observations
  • Review table of proportions, counts, and variables against exogenous variables

Special Cases

  • The Good Guys (18,512 | 16.11%)
  • The Liquidated (2,324 | 2.02%)
  • The Throwaway Accounts (3,191 | 2.78%)
  • The Potential Arbitragers (3,824 | 3.33%)


High Rollers

  • Without debt (6,553 | 5.7%)
  • With debt (7,016 | 6.11%)

Middle Class

  • High checking, low savings (10,174 | 8.85%)
  • High savings, low checking (9,477 | 8.25%)

Small Frys

  • Depositors on Ethereum (2,773 | 2.41%)
  • Degen Active Depositors (1,202 | 1.05%)
  • Debt Users (16,529 | 14.38%)


  • Ethereum Only (9,321 | 8.03%)’
  • Multichain (24,107 | 20.98%)

PS. I had to make a new account here because my old one was connected to the Gitcoin email address I no longer have.

1 Like