Stakers deposit funds into BENQI and receive a return on investment in the form of staking rewards (supply-side fees).

BENQI Liquid Staking is the liquid staking derivative business line of BENQI. BENQI was founded by an international team of software developers and launched in 2021.

BENQI tokenholders own and govern the BENQI protocol, including the BENQI Liquid Staking business line. They currently take a cut of the total staking rewards earned by the protocol (revenue). BENQI's investors include Dragonfly Capital, Mechanism Capital, Spartan, etc.

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BENQI Liquid Staking

# BENQI Liquid Staking Active users (monthly) Methodology

## 1. Overview

Monthly active users (MAU) for BenQi Liquid Staking measures the number of unique user addresses that have interacted with the BenQi Liquid Staking protocol on Avalanche within any 30-day rolling window. This metric captures user engagement by counting distinct addresses that have either deposited AVAX for liquid staking or redeemed their staked tokens. The calculation uses HyperLogLog data structures for efficient unique user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Avalanche blockchain event logs**: Parsed event logs from the BenQi Liquid Staking contract on Avalanche
- **Staking events**: `StakedAvax_Submitted_event` logs capturing when users deposit AVAX for liquid staking
- **Redemption events**: `StakedAvax_Redeem_event` logs capturing when users withdraw/redeem their staked tokens
- **Event parameters**: User addresses extracted from the `user` parameter in both event types

## 3. Step-by-step calculation

### Component A: User interaction identification
- **Purpose**: Identifies all user interactions with the BenQi Liquid Staking protocol
- **Logic**: Extracts user addresses from two types of Avalanche blockchain events. For `StakedAvax_Submitted_event` logs, users are classified as "Depositor" types representing staking activities. For `StakedAvax_Redeem_event` logs, users are classified as "Withdrawer" types representing redemption activities. Each event record includes the user address from the `user` parameter, block timestamp, transaction hash, and chain identifier
- **Key fields**: `user_address` (from `user` parameter), `user_type` (Depositor or Withdrawer), `block_timestamp`, `chain` (avalanche), `market_sector_id` (liquid_staking), `business_segment_id` (liquid_staking)

### Component B: Daily active user calculation  
- **Purpose**: Aggregates user interactions into daily unique user counts
- **Logic**: Groups all user interactions by day using timestamp truncation to daily granularity. Takes distinct user addresses within each day to eliminate duplicate interactions from the same user. Groups results by timestamp, chain, user type, and product identifier to create dimensional breakdowns of daily active users
- **Key fields**: `timestamp` (truncated to day), `user_dau` (distinct user addresses), `timestamp_grain` (day), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog preparation
- **Purpose**: Converts daily active user counts into HyperLogLog structures for efficient unique counting
- **Logic**: Takes the daily active user data and initializes HyperLogLog data structures with precision 15 for each day and dimensional combination. This allows for approximate but highly efficient unique user counting when aggregating across time periods
- **Key fields**: `hll` (HyperLogLog structure), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window creation
- **Purpose**: Creates a 30-day rolling window for each date to calculate monthly active users
- **Logic**: Generates a datespine of consecutive days from the earliest available data to the most recent day. For each day in the datespine, creates joins with the previous 29 days of data to form complete 30-day windows. This ensures every day has a complete 30-day lookback period for MAU calculation
- **Key fields**: `timestamp` (target date), `joinday` (each of the 30 days in the window)

### Final aggregation
- **Combining components**: Joins the rolling window structure with the daily HLL data to create 30-day user activity windows. Merges HLL structures across the 30-day periods using `hll_count.merge_partial()` to combine unique user counts, then extracts final counts using `hll_count.extract()`
- **Dimensional breakdowns**: Calculates MAU across four different dimensional levels: project-level (overall protocol MAU), chain-level (MAU by blockchain), user-type-level (MAU by Depositor vs Withdrawer), and product-level (MAU by product identifier). Each breakdown groups data differently while maintaining the 30-day rolling window logic
- **Time aggregation**: Uses daily boundaries for the rolling window calculation, with each day representing the MAU for the 30-day period ending on that date
- **Final calculation**: The final MAU value represents the count of unique user addresses that had at least one interaction (deposit or withdrawal) with the BenQi Liquid Staking protocol within the trailing 30-day period, calculated using HyperLogLog approximation for computational efficiency

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

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

BENQI Liquid Staking

Active users (monthly)

Active users (monthly)

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