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

github

twitter

discord

etherscan

website

Lending

BendDAO

# BendDAO Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for BendDAO measures the number of unique users who interact with BendDAO's lending protocol within any 7-day rolling window. This metric captures user engagement across core lending activities including deposits, borrowings, repayments, and withdrawals on the Ethereum blockchain. The calculation uses HyperLogLog data structures for efficient approximate counting of distinct users across time windows.

## 2. Data sources

- **BendDAO contract events**: Parsed `BToken_Mint`, `DebtToken_Mint`, `DebtToken_Burn`, and `BToken_Transfer` events from BendDAO's lending protocol contracts on Ethereum
- **Ethereum blockchain data**: Block timestamps and transaction hashes for event timing and deduplication
- **Contract metadata**: Specific BendDAO contract addresses including WETHGateway (`0x3b968d2d299b895a5fcf3bba7a64ad0f566e6f88`), bendWETH (`0xed1840223484483c0cb050e6fc344d1ebf0778a9`), and bendDAI (`0x9631c79bfd6123a5b53307b6cdfb35f97606f954`) tokens

## 3. Step-by-step calculation

### Component A: User activity identification
- **Purpose**: Identify and classify all users interacting with BendDAO's lending protocol
- **Logic**: Extract user addresses from specific contract events based on activity type. Depositors are identified from `BToken_Mint` events using the `from` parameter. Borrowers come from `DebtToken_Mint` events using the `from` parameter. Repayers are captured from `DebtToken_Burn` events using the `user` parameter. Withdrawers require special logic from `BToken_Transfer` events with two conditions: transfers to the WETHGateway contract from the bendWETH token, or burns (transfers to zero address) from the bendDAI token excluding transfers from a specific address (`0xf3ab1d58ce6b9e0d42b8958c918649305e1b1d26`)
- **Key fields**: `user_address` (extracted from event parameters), `user_type` (Depositor, Borrower, Repayer, Withdrawer), `block_timestamp`, `chain` (always Ethereum), `market_sector_id` and `business_segment_id` (both set to 'lending')

### Component B: Daily active user aggregation
- **Purpose**: Create daily snapshots of unique active users by activity type
- **Logic**: Group all user events by day using truncated block timestamps. Extract distinct user addresses for each combination of date, chain, user type, market sector, business segment, and product. This creates a foundation dataset where each row represents a unique user's activity on a specific day with their classification
- **Key fields**: `timestamp` (day-level), `timestamp_grain` (set to 'day'), `chain`, `user_type`, `product_id`, `user_dau` (the user address)

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user lists into HyperLogLog data structures for efficient distinct counting
- **Logic**: Initialize HyperLogLog sketches for each day's user set with precision parameter 15. This allows approximate distinct counting without storing individual user addresses, enabling efficient aggregation across time windows
- **Key fields**: `hll` (HyperLogLog sketch containing daily user set), maintaining all dimensional attributes from the DAU data

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for WAU calculation
- **Logic**: Generate a continuous datespine covering all dates from the first user activity to the current day. For each date in the datespine, create join relationships to the previous 6 days (7 days total including current day). This creates a comprehensive mapping where each target date connects to its 7-day lookback period
- **Key fields**: `timestamp` (target date for WAU calculation), `joinday` (dates within the 7-day window)

### Component E: Window aggregation
- **Purpose**: Aggregate unique users across each 7-day window
- **Logic**: Join the rolling window structure with daily HyperLogLog data to collect all user activity within each 7-day period. Merge HyperLogLog sketches from all days in each window and extract the final count of distinct users. This produces WAU values for each combination of date and dimensional attributes
- **Key fields**: `user_wau` (distinct user count from HLL extraction), `timestamp`, dimensional attributes (chain, user type, product)

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple analytical views of WAU data
- **Logic**: Generate separate breakdowns for different analytical perspectives. Project-level breakdown aggregates across all dimensions. Chain-level breakdown maintains chain granularity. User-type breakdown preserves user activity classification. Product-level breakdown maintains product granularity. Each breakdown unions together to create a comprehensive dimensional dataset
- **Key fields**: `dimensions` (breakdown type), dimensional attributes set to null where not relevant, `user_wau` values aggregated appropriately for each breakdown level

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with standardized schema
- **Time aggregation**: Daily WAU calculations with 7-day rolling windows, using day-level timestamp boundaries
- **Final calculation**: Extract distinct user counts from merged HyperLogLog sketches, providing approximate but highly accurate WAU values for each dimensional combination and date

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

Unique revenue-generating users over a rolling 7-day window.

BendDAOBEND

Active users (weekly)

Active users (weekly)

Competitive landscape