Overcollateralized nodes custody and transfer funds between blockchains and receive burn & mint fees from traders (supply-side fees).

Ren is a non-custodial interoperability protocol built on Ethereum and other blockchains. Ren was founded by Tayiang Zhang & Loong Wang and launched in 2017. Ren is currently only in redeeming-mode, due to their exposure to the effects of Alameda Research's failure.

REN tokenholders own and govern the protocol. They do not currently take a cut of the total mint/burn fees paid by traders (revenue). Ren's investors include Polychain, FBG Capital, BlockVC, etc.

governance

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Bridges

# Ren Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the average amount of fees generated per daily active user on the Ren protocol. This metric answers the business question "How much revenue does each user generate on average?" and provides insight into user value and protocol monetization effectiveness. The calculation divides total daily fees by the number of daily active users, where both components are derived from the same underlying Ren bridge transaction events.

## 2. Data sources

The AFPU calculation uses several key data sources:
- **Ren bridge contract events**: Parsed `LogMint` and `LogBurn` events from Ren Gateway contracts deployed across seven blockchains (Ethereum, BSC, Polygon, Fantom, Avalanche, Arbitrum, and Optimism)
- **Transaction event logs**: Blockchain event logs containing user addresses, transfer amounts, timestamps, and transaction metadata from bridge operations
- **End-of-day token price feeds**: Daily closing prices for tokens involved in bridge transfers, used for USD conversion
- **Token metadata**: Contract addresses, decimal places, and symbol information for proper amount calculations
- **Alternative token address mappings**: Cross-chain token address mappings that redirect price lookups to Polygon-based tokens when primary price feeds are unavailable

## 3. Step-by-step calculation

### Component A: Daily active users extraction
- **Purpose**: Identify and count unique users interacting with Ren bridge contracts each day
- **Logic**: Parse `LogMint` and `LogBurn` events from Ren Gateway contracts across all supported chains. Extract user addresses from the `to` field (with slight variations in field naming across chains like `_to` vs `to`). Classify users as either "Depositor" (from `LogMint` events representing deposits/minting) or "Withdrawer" (from `LogBurn` events representing withdrawals/burning). Group events by day and count distinct user addresses using HyperLogLog aggregation for efficient computation across different dimensional breakdowns
- **Key fields**: `block_timestamp` for daily grouping, `to` or `_to` fields for user addresses, event type determines user classification

### Component B: Transfer volume extraction  
- **Purpose**: Capture the monetary value of bridge transfers for fee calculation
- **Logic**: Parse the same `LogMint` and `LogBurn` events used for user counting, but focus on extracting transfer amounts from the `amount` or `_amount` fields. Join with alternative token address mappings to redirect tokens to Polygon-based equivalents when original chain price feeds are unavailable. This mapping ensures consistent price resolution across all supported chains
- **Key fields**: `amount` or `_amount` fields containing transfer values, `account_address` for token contract identification, `block_timestamp` for timing

### Component C: Fee calculation with time-based rates
- **Purpose**: Calculate fees based on historically accurate percentage rates that varied over time
- **Logic**: Apply different fee percentages based on transfer type and date. Mint operations have fees ranging from 0.1% to 0.36% depending on the time period, while burn operations range from 0.07% to 0.15%. The fee structure changed multiple times between 2020-2022, with specific date ranges for each rate. Multiply transfer amounts by the applicable fee percentage for each transaction's timestamp and transfer type
- **Key fields**: `transfer_type` (Mint or Burn), `timestamp` for determining applicable fee rate, `transfer_amount` for fee base calculation

### Component D: USD conversion and aggregation
- **Purpose**: Convert fee amounts to USD and aggregate by day
- **Logic**: Apply decimal corrections based on token metadata to ensure proper unit representation. Convert token amounts to USD using end-of-day price feeds, utilizing alternative token addresses when primary feeds are unavailable. Sum all fees by day and chain to create daily fee totals
- **Key fields**: `token_address` for price lookup, `fees` amounts in native tokens, conversion to `fees` in USD

### Component E: User aggregation with HyperLogLog
- **Purpose**: Efficiently count distinct users across multiple dimensional breakdowns
- **Logic**: Use HyperLogLog functions to count distinct user addresses per day with breakdowns by project level, chain, user type (Depositor/Withdrawer), and product. The HLL aggregation allows for efficient computation while maintaining accuracy for large user sets. Generate separate aggregations for each dimensional level and combine them into a unified dataset
- **Key fields**: `user_address` for distinct counting, `chain` and `user_type` for dimensional breakdowns

### Final aggregation
- **Combining components**: Join daily fee totals with daily active user counts by matching on date and dimensional attributes. Both metrics are aggregated to the same daily grain and dimensional breakdowns
- **Currency conversion**: All fees are converted to USD using end-of-day price feeds with decimal corrections applied before aggregation
- **Time aggregation**: Both fees and user counts use daily time boundaries (timestamp truncated to day) with consistent timezone handling
- **Final calculation**: Divide total daily fees in USD by the count of daily active users to produce the average fees per user metric. Handle cases where user count is zero by returning null or zero values as appropriate

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

RenREN

Average fees per user (AFPU)

Average fees per user (AFPU)

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