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Blockchains (L2)

Humanity Protocol

# Humanity Protocol Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the count of unique users who have performed at least one transaction on the Humanity blockchain within a rolling 30-day period. This metric uses a sophisticated rolling window approach that calculates MAU for each day by looking back 30 days from that date, providing a smooth trend line that reflects user engagement patterns. The calculation employs HyperLogLog algorithms for efficient distinct counting and provides breakdowns across multiple dimensions including overall project-level, chain-level, user type, and product-level views.

## 2. Data sources

The MAU calculation relies on the following raw data sources:

- **Humanity blockchain transactions**: Complete transaction records from the Humanity blockchain including transaction hashes, block timestamps, sender addresses, and transaction metadata
- **Transaction metadata**: Block-level timing information and transaction classification data used for user identification and activity tracking

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses from Humanity blockchain transaction activity
- **Logic**: Processes all Humanity blockchain transactions to extract transaction sender addresses as active users. Each transaction sender (`from_address`) represents a unique user performing activity on the blockchain. Transactions are classified under the "Transaction sender" user type and categorized within the "blockchains_l2" market sector and "blockchain" business segment.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for deduplication, and classification fields for dimensional analysis

### Component B: Daily active users calculation  
- **Purpose**: Aggregates user activity into daily distinct active user counts
- **Logic**: Groups transaction activity by day using timestamp truncation to day granularity. For each day, identifies all unique user addresses who performed at least one transaction. This creates a foundation dataset of daily distinct users broken down by chain, user type, market sector, business segment, and product dimensions. Duplicate user addresses within the same day are automatically deduplicated.
- **Key fields**: `timestamp` truncated to day level, `user_address` for unique user identification, dimensional fields like `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily active user data into HyperLogLog sketches for efficient distinct counting across time windows  
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches with precision parameter 15 for each day, grouped by timestamp, chain, user type, and product dimensions. HyperLogLog is a probabilistic data structure that enables efficient estimation of distinct counts, crucial for calculating MAU across rolling 30-day windows without performance issues from large-scale distinct counting operations.
- **Key fields**: `hll` sketch containing encoded user information, grouping dimensions preserved from daily data

### Component D: Date spine and rolling window creation
- **Purpose**: Generates complete time series and creates 30-day rolling windows for MAU calculation
- **Logic**: Creates a datespine covering all days from the earliest available data to yesterday, ensuring no gaps in the time series. For each day in the datespine, generates a corresponding 30-day lookback window spanning from 29 days prior to the current day (inclusive). This rolling window approach means that each day's MAU represents users active in the 30-day period ending on that day. The window uses timestamp arrays to efficiently create joinable date ranges.
- **Key fields**: `timestamp` as the target date, `joinday` as the lookback dates within each 30-day window

### Component E: Window-based user aggregation
- **Purpose**: Merges HyperLogLog sketches across 30-day windows to calculate distinct user counts
- **Logic**: For each target date, joins the daily HyperLogLog sketches from all days within the corresponding 30-day window. Merges the HyperLogLog sketches using partial merge operations to combine user activity across the entire window period. This approach efficiently handles overlapping users who were active on multiple days within the 30-day window, ensuring they are only counted once in the final MAU calculation.
- **Key fields**: Merged `hll` sketches containing distinct user information across the full 30-day window, preserved dimensional breakdowns

### Component F: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across multiple dimensional views for comprehensive analysis
- **Logic**: Creates four separate dimensional breakdowns from the window-based data: 1) Project-level MAU aggregating all activity across all dimensions, 2) Chain-level MAU broken down by individual blockchain, 3) User type MAU segmented by different user categories, and 4) Product-level MAU divided by product offerings. Each breakdown extracts the final distinct count from merged HyperLogLog sketches using the extract function, with unused dimension fields set to null for that particular view.
- **Key fields**: `user_mau` as the extracted distinct count, `dimensions` indicating the breakdown type, relevant dimensional fields populated per breakdown

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset containing MAU values across all dimensional views for each day
- **Currency conversion**: Not applicable as this is a user count metric rather than a financial metric
- **Time aggregation**: MAU is calculated as a daily time series where each day represents the count of unique users active in the trailing 30-day period ending on that date
- **Final calculation**: The final MAU value for each day and dimensional breakdown represents the distinct count of users who performed at least one transaction within the 30-day rolling window, extracted from merged HyperLogLog sketches and standardized through the finalization process with zero-filling for missing data points

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

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

Humanity ProtocolH

Active users (monthly)

Active users (monthly)

Competitive landscape