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

ZeroLend is a non-custodial lending protocol built on Ethereum and other adjacent blockchains.

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

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Lending

ZeroLend

# ZeroLend Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for ZeroLend measures the count of unique user addresses that interact with ZeroLend's lending pools within any 30-day rolling window. This metric answers the business question "How many unique users are actively engaging with ZeroLend's lending protocol each month?" The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across multiple blockchain networks and lending activities, providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Blockchain events**: Parsed `Borrow`, `FlashLoan`, `Repay`, `Supply`, and `Withdraw` events from ZeroLend's LendingPool smart contracts across six blockchains
- **Multi-chain coverage**: Events from Manta, Base, zkSync Era, Blast, Ethereum, and Linea blockchains
- **Multi-pool architecture**: Different pool configurations per chain - Ethereum includes BtcLrt, EthLrt, and RwaStablecoin pools; Linea includes Main, Foxy, and Croak pools; other chains have single lending pools
- **Event metadata**: Block timestamps, transaction hashes, user addresses, and contract-specific parameters from each lending interaction

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that interact with ZeroLend lending pools and classifies their activity types
- **Logic**: Extracts user addresses from parsed lending pool events across all supported blockchains. For `Borrow` and `FlashLoan` events, users are classified as "Borrower" (using `user` or `initiator` fields respectively). For `Repay` events, users are classified as "Repayer". For `Supply` events, users are classified as "Depositor". For `Withdraw` events, users are classified as "Withdrawer". Each interaction includes the block timestamp, transaction hash, chain identifier, and user address. All activities are categorized under the 'lending' market sector and business segment.
- **Key fields**: `user_address`, `user_type`, `chain`, `block_timestamp`, `transaction_hash`

### Component B: Daily active user aggregation
- **Purpose**: Calculates the count of unique users active each day across different dimensions
- **Logic**: Groups user interactions by truncated day timestamps and aggregates unique user addresses. Creates daily snapshots grouped by chain, user type, and product identifiers. Applies chain availability filtering to ensure data quality and consistency. Generates distinct user address counts for each combination of day, chain, user type, and product.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau`

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient unique counting across time windows
- **Logic**: Transforms daily active user lists into HyperLogLog sketches with 15-bit precision using the `hll_count.init` function. This probabilistic approach allows efficient merging of user counts across different time periods while maintaining reasonable accuracy for unique user counting. Groups sketches by timestamp, chain, user type, and product dimensions.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll`

### Component D: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each date to define the MAU calculation period
- **Logic**: Generates a datespine covering all dates from the earliest data point to yesterday. For each date in the datespine, creates a 30-day window by joining with the 29 preceding days using timestamp arithmetic. This approach ensures every date has a complete 30-day lookback window for MAU calculation, handling missing days gracefully through the join logic.
- **Key fields**: `timestamp`, `joinday`

### Component E: HyperLogLog sketch merging
- **Purpose**: Combines daily HLL sketches across 30-day windows to count unique users
- **Logic**: Joins the rolling window datespine with daily HLL sketches, matching each window day with its corresponding daily sketch. Merges HLL sketches across the 30-day period using `hll_count.merge_partial` to combine probabilistic user counts. Extracts the final unique user count from the merged sketch using `hll_count.extract`. This process handles user overlaps across days automatically through the HLL probabilistic counting mechanism.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll`

### Final aggregation
- **Combining components**: Creates four separate dimensional breakdowns from the windowed HLL data: project-level aggregation (no dimensional filters), chain-level aggregation (grouped by chain), user-type-level aggregation (grouped by user type), and product-level aggregation (grouped by product). Each breakdown merges HLL sketches and extracts unique user counts for its specific dimensional focus.
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows where each date represents the unique user count for that date and the preceding 29 days. Monthly partitioning is used for data organization and incremental processing efficiency.
- **Final calculation**: The final MAU value for any given date equals the count of unique user addresses that had at least one lending interaction (borrow, repay, supply, withdraw, or flashloan) within the 30-day window ending on that date, calculated using merged HyperLogLog sketches and provided in multiple dimensional breakdowns

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

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

ZeroLendZERO

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown