Lenders deposit funds into BendDAO and receive interest payments from borrowers (supply-side fees). BendDAO has other business lines in addition, however this dashboard only tracks the BendDAO lending protocol.

BendDAO is a non-custodial NFT lending protocol built on Ethereum. BendDAO was founded by an anonymous team and launched in 2021.

BEND tokenholders own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue).

governance

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Lending

BendDAO

# BendDAO Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for BendDAO measures the number of unique users who interact with BendDAO's v1 lending protocol within any 30-day rolling window. This metric captures user engagement across lending activities including deposits, borrows, repayments, and withdrawals on the Ethereum blockchain. The calculation uses HyperLogLog (HLL) sketches for efficient approximate counting of unique users over the rolling window periods.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Smart contract event logs**: Parsed Ethereum blockchain event logs from BendDAO's v1 lending protocol smart contracts
- **BToken events**: `Mint` events from BToken contracts tracking deposit activities and `Transfer` events tracking withdrawal activities
- **DebtToken events**: `Mint` events tracking borrowing activities and `Burn` events tracking repayment activities  
- **Transaction timestamps**: Ethereum blockchain timestamps for daily aggregation boundaries
- **Contract addresses**: Hardcoded smart contract addresses including WETHGateway contract (`0x3b968d2d299b895a5fcf3bba7a64ad0f566e6f88`) and specific token contract addresses for filtering legitimate withdrawal events

## 3. Step-by-step calculation

### Component A: User event identification
- **Purpose**: Identify and classify all user interactions with BendDAO's lending protocol
- **Logic**: Parse specific smart contract events from BendDAO v1 on Ethereum to extract user addresses and classify their activity types. For deposits, extract the `from` address from `BToken_Mint_event` logs and classify as "Depositor". For borrowing, extract the `from` address from `DebtToken_Mint_event` logs and classify as "Borrower". For repayments, extract the `user` address from `DebtToken_Burn_event` logs and classify as "Repayer". For withdrawals, apply special filtering logic to `BToken_Transfer_event` logs - count transfers to the WETHGateway contract address or burns to zero address while excluding specific system addresses to identify legitimate user withdrawals, classifying the `from` address as "Withdrawer"
- **Key fields**: `block_timestamp` for timing, `user_address` for unique user identification, `user_type` for activity classification, `chain` set to "ethereum", `market_sector_id` and `business_segment_id` both set to "lending"

### Component B: Daily active user aggregation  
- **Purpose**: Convert individual user events into daily unique active user counts
- **Logic**: Group the classified user events by day using timestamp truncation to daily boundaries. Create distinct counts of user addresses for each combination of date, chain, user type, and product. Apply chain availability filtering to ensure data quality and exclude periods with incomplete data coverage
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing the unique user address, dimensional fields for grouping including `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user data into efficient HyperLogLog sketches for approximate unique counting
- **Logic**: Transform the daily active user data into HyperLogLog sketches using precision level 15. Group by timestamp, chain, user type, and product to create HLL sketches that can be efficiently merged across time periods for rolling window calculations
- **Key fields**: `hll` containing the HyperLogLog sketch data, dimensional grouping fields preserved from daily aggregation

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each date to calculate MAU
- **Logic**: Generate a continuous datespine covering all relevant dates to avoid gaps in the time series. For each date in the datespine, create a 30-day lookback period including the current date (29 days prior plus current day). Join this expanded date range with the daily HLL sketch data to create windows containing all relevant daily sketches for each MAU calculation date
- **Key fields**: `timestamp` for the MAU calculation date, `joinday` for the dates within the 30-day window, HLL sketches and dimensional fields from the daily data

### Component E: MAU calculation by dimensions
- **Purpose**: Calculate unique user counts across the 30-day windows for different analytical breakdowns
- **Logic**: For each MAU date and dimensional breakdown, merge all HyperLogLog sketches within the 30-day window using HLL merge operations, then extract the final approximate unique count. Calculate four separate dimensional views: project-level MAU (no dimensional filters), chain-level MAU (grouped by chain), user-type-level MAU (grouped by user type like Depositor, Borrower), and product-level MAU (grouped by product). Each breakdown sets unused dimensional fields to null while preserving the relevant grouping dimension
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown level, relevant dimensional fields with null values for unused dimensions

### Final aggregation
- **Combining components**: Union all four dimensional MAU calculations (project-level, chain-level, user-type-level, product-level) into a single result set that provides multiple analytical perspectives on the same underlying user activity data
- **Time aggregation**: Each MAU value represents unique users across a rolling 30-day window ending on the specified timestamp, calculated daily to provide a continuous time series
- **Final calculation**: The final `user_mau` value represents the approximate count of unique user addresses who performed at least one lending activity (deposit, borrow, repayment, or withdrawal) within the 30-day rolling window, calculated using HyperLogLog sketch merging for computational efficiency while maintaining acceptable accuracy for large-scale user counting

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

Unique revenue-generating users over a rolling 30-day window.

BendDAOBEND

Active users (monthly)

Active users (monthly)

Competitive landscape