Lenders deposit funds into BENQI and receive interest payments from borrowers (supply-side fees).

BENQI is a non-custodial lending protocol built on Avalanche. BENQI was founded by an international team of software developers and launched in 2021.

 QI tokenholders own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue). BENQI’s investors include Dragonfly Capital, Mechanism Capital, Spartan, etc.

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Lending

BENQI

# BENQI Active users (monthly) Methodology

## 1. Overview

The BENQI Monthly Active Users (MAU) metric measures the number of unique users who interacted with the BENQI lending protocol within any 30-day rolling window. This metric answers the business question "How many unique users are actively engaging with BENQI's lending services each month?" The calculation uses a probabilistic counting approach with HyperLogLog data structures to efficiently aggregate daily active users over rolling 30-day periods, providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

The methodology relies on the following raw data sources:
- **Avalanche blockchain events**: Parsed event logs from the BENQI v1 lending protocol smart contracts
- **Lending activity events**: Four specific event types capturing core lending activities - `Market_Borrow_event`, `Market_Mint_event`, `Market_Redeem_event`, and `Market_RepayBorrow_event`
- **Transaction metadata**: Block timestamps and transaction hashes from Avalanche blockchain
- **User wallet addresses**: Ethereum-style addresses extracted from lending event parameters
- **Protocol metadata**: Chain identifiers, market sector classifications, and business segment information

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify all unique users and their lending activities from blockchain events
- **Logic**: Extract user addresses from four types of BENQI lending events on Avalanche. Each event type corresponds to a specific user action - borrowing maps to "Borrower" user type, minting/depositing to "Depositor", redeeming/withdrawing to "Withdrawer", and repaying to "Repayer". The system captures the user address, timestamp, transaction hash, and classifies each activity with market sector as "lending" and business segment as "lending". Chain is hardcoded as "avalanche" and product_id is set to null for this protocol.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address` (extracted from event parameters like `borrower`, `minter`, `redeemer`, `payer`), `user_type`, `chain`, `market_sector_id`, `business_segment_id`

### Component B: Daily active user aggregation
- **Purpose**: Calculate Daily Active Users (DAU) by aggregating unique users per day
- **Logic**: Group the extracted user activities by day using timestamp truncation to the day level. For each day, identify distinct user addresses along with their associated dimensions (chain, user type, market sector, business segment, product). This creates a daily snapshot of unique users who performed any lending activity. The output preserves all dimensional attributes needed for downstream MAU calculations while ensuring each user is counted only once per day regardless of how many transactions they performed.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` ("day"), `chain`, `user_type`, `market_sector_id`, `business_segment_id`, `product_id`, `user_dau` (unique user address)

### Component C: HLL probabilistic counting initialization
- **Purpose**: Convert daily user sets into HyperLogLog data structures for efficient unique counting across time windows
- **Logic**: Transform the daily active user data into HyperLogLog probabilistic counting structures using the `hll_count.init` function with precision parameter 15. Group the data by timestamp, timestamp grain, chain, user type, and product to create HLL sketches that can later be merged efficiently. This probabilistic approach allows accurate estimation of unique user counts when combining data across multiple days without the computational overhead of exact distinct counting on large datasets.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component D: Rolling window generation
- **Purpose**: Create 30-day rolling windows for each date to calculate monthly active users
- **Logic**: Generate a complete datespine covering all days from the earliest DAU data to yesterday, ensuring no dates are skipped. For each date in the spine, create a 30-day lookback window by generating timestamps for the current date and the previous 29 days. This creates a cross-join structure where each target date is associated with all dates in its 30-day window. Join this window structure with the daily HLL data to collect all user activity within each 30-day period.
- **Key fields**: `timestamp` (target date), `joinday` (each day in the 30-day window), `hll` (from matching daily data)

### Component E: Multi-dimensional MAU calculation
- **Purpose**: Calculate Monthly Active Users across different analytical dimensions
- **Logic**: Aggregate the windowed HLL data across four different dimensional breakdowns. For project-level MAU, merge all HLL sketches regardless of dimension and extract the unique count. For chain-level MAU, group by chain and merge HLL sketches within each chain. For user-type-level MAU, group by user type and merge HLL sketches within each user type. For product-level MAU, group by product and merge HLL sketches within each product. Each breakdown nullifies irrelevant dimensions to create clean analytical views.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_mau` (extracted unique count from merged HLL)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with standardized schema
- **Currency conversion**: Not applicable - this metric counts users, not monetary values
- **Time aggregation**: Uses 30-day rolling windows calculated daily, with each date representing the MAU for that date's trailing 30-day period
- **Final calculation**: Apply the `finalize_metric_user_mau` macro to standardize the schema with data identifier "benqi", implement gap-filling strategy to insert zeros for missing periods, and ensure consistent time series output format. Filter results to include only relevant time periods based on incremental processing logic when updating existing data.

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

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

BENQIQI

Active users (monthly)

Active users (monthly)

Competitive landscape