World Chain is designed as a Layer 2 blockchain built on the OP Stack and optimized for verified humans. It leverages World ID, a privacy-preserving proof of personhood, to prioritize real users over bots and Sybil attackers. It integrates with World App and other identity wallets using Safe smart contract infrastructure.

World Chain is an Ethereum Layer 2 chain that enables applications to distinguish between bots and verified humans. It forms a core part of the Worldcoin ecosystem, aiming to power more human-centric onchain activity by prioritizing real people in blockspace allocation and transaction processing.

World Chain is developed by the team behind Worldcoin, which is currently governed by the Worldcoin Foundation. Worldcoin's investors include a16z, Multicoin Capital, 1confirmation, CoinFund, among others.

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Blockchains (L2)

World Chain

# World Chain Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who performed at least one transaction on Worldchain within any 7-day rolling window. This metric provides insights into user engagement and platform adoption by tracking unique transaction senders across weekly periods. The calculation uses HyperLogLog algorithms for efficient approximate distinct counting and provides multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

- **Worldchain transaction records**: Raw blockchain transaction data containing transaction hashes, sender addresses, and execution timestamps
- **Block timestamp data**: Worldchain block timestamps used to establish temporal boundaries for daily and weekly aggregations
- **Transaction metadata**: Additional transaction context including market sector classification and business segment identification

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies daily active users from raw transaction data
- **Logic**: Extracts distinct transaction sender addresses (`from_address`) from Worldchain transaction records, treating each unique sender as an active user. Block timestamps are truncated to daily boundaries to group activity by date. Each user is classified as a "Transaction sender" type within the "blockchains_l2" market sector and "blockchain" business segment
- **Key fields**: `block_timestamp` (truncated to day), `from_address` as `user_address`, `transaction_hash`, `chain` identifier

### Component B: Daily user aggregation with HyperLogLog
- **Purpose**: Creates efficient data structures for distinct user counting across time periods
- **Logic**: Converts daily distinct user sets into HyperLogLog (HLL) sketches using `hll_count.init()` with 15-bit precision. This probabilistic data structure enables efficient approximate distinct counting when merging multiple time periods. Daily user counts are grouped by timestamp, chain, user type, and product dimensions
- **Key fields**: `timestamp` (daily grain), `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch of users)

### Component C: Rolling 7-day window construction
- **Purpose**: Creates overlapping 7-day periods for weekly active user calculation
- **Logic**: Generates a continuous datespine covering the full time range of available data, then creates rolling windows by joining each day with the 6 preceding days. This ensures every possible 7-day period is captured, including periods that might not have activity on all days. The window construction uses `generate_timestamp_array()` to create precise 7-day intervals
- **Key fields**: `timestamp` (target date), `joinday` (each of the 7 days in the window)

### Component D: Weekly user aggregation
- **Purpose**: Combines daily HLL sketches within each 7-day window to count weekly active users
- **Logic**: For each 7-day window, merges the HLL sketches from all constituent days using `hll_count.merge_partial()`, then extracts the approximate distinct count using `hll_count.extract()`. This produces the weekly active user count for each window end date
- **Key fields**: `timestamp` (window end date), merged `hll` sketches, `user_wau` (weekly active user count)

### Component E: Dimensional breakdowns
- **Purpose**: Provides multiple analytical perspectives on weekly active users
- **Logic**: Creates four separate dimensional views by grouping and merging HLL sketches differently: project-level (overall), chain-level, user type level, and product level. Each breakdown maintains the same weekly aggregation logic but groups data according to the specific dimension, with unused dimensions set to null
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives in a single table
- **Schema standardization**: The `finalize_metric_user_wau` macro applies consistent schema formatting, data type casting, and metadata enrichment across all records
- **Gap filling**: Missing days in the time series are filled with zero values to ensure continuous data availability for reporting and analysis
- **Final calculation**: The weekly active user count represents the approximate number of distinct users who performed at least one transaction within any 7-day rolling window, calculated using probabilistic HyperLogLog algorithms for scalability and efficiency

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

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

World ChainWLD

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown