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World Mobile Chain

# World Mobile Chain Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who were active on the WorldMobile Chain telecom platform within any 30-day rolling window. This metric specifically tracks subscribers who received tokens from WorldMobile's core service contract, representing active participation in the decentralized telecom network. The calculation uses a rolling 30-day window approach with HyperLogLog data structures for efficient approximate counting across multiple dimensional breakdowns.

## 2. Data sources

- **WorldMobile Chain ERC20 transfer events**: Parsed `Transfer` events from the WorldMobile Chain blockchain, specifically from the core token contract `0xd7d10d554fb33a37a7720e5666590205dbf5f482`
- **Blockchain transaction data**: WorldMobile Chain transaction records containing block timestamps and transaction hashes
- **Token contract metadata**: Information about the WorldMobile token contract representing telecom service activities

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies active telecom service subscribers based on token transfer activity
- **Logic**: Extracts `Transfer` events from the specific WorldMobile token contract `0xd7d10d554fb33a37a7720e5666590205dbf5f482`. The recipient address (`to` field) from each transfer represents an active subscriber receiving telecom service tokens. Each transfer event is classified as "Telecom service active subscriber" activity, with the chain identified as "worldmobilechain" and no specific product segmentation applied.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for deduplication, `to` address as the active user identifier, and constant classification values for user categorization

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily unique active users (DAU) from the raw transfer events
- **Logic**: Groups transfer events by day using timestamp truncation to daily boundaries. Applies distinct counting on user addresses within each day to eliminate multiple activities by the same user. Maintains dimensional attributes including chain, user type, and product segmentation for downstream analysis.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` as the unique user identifier, and dimensional fields for chain, user type, and product classification

### Component C: Rolling window preparation
- **Purpose**: Creates 30-day rolling windows using HyperLogLog data structures for efficient approximate counting
- **Logic**: Initializes HyperLogLog sketches from the daily active user data to enable efficient merging across time windows. Generates a complete datespine covering all days from the earliest data to the current date to ensure no missing timestamps. Creates join relationships between each day and its preceding 29 days to form 30-day windows, accounting for both present and historical activity periods.
- **Key fields**: HyperLogLog sketches (`hll`) containing user address information, `timestamp` for window anchoring, and `joinday` for rolling window construction

### Component D: Dimensional MAU calculation
- **Purpose**: Computes MAU across multiple dimensional breakdowns using HyperLogLog merging
- **Logic**: Merges HyperLogLog sketches across each 30-day window to approximate unique user counts. Creates four dimensional views: project-level aggregation (overall MAU), chain-level breakdown, user-type-level breakdown, and product-level breakdown. Each dimension uses HLL merging to combine daily sketches and extracts the final approximate unique count. Null values are assigned to dimensions not being aggregated in each breakdown.
- **Key fields**: `user_mau` as the final approximate unique user count, `dimensions` to identify the aggregation level, and dimension-specific fields (`chain_id`, `user_type`, `product_id`) populated based on the breakdown type

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using UNION ALL operations to create a comprehensive view with multiple aggregation levels in a single result set
- **Time aggregation**: Uses 30-day rolling windows updated daily, where each day's MAU represents unique users active in the current day plus the preceding 29 days
- **Final calculation**: Applies the finalize_metric_user_mau macro to standardize the output schema, handle missing data with zero-filling strategies, and ensure consistent formatting across all WorldMobile Chain user metrics

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

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

World Mobile ChainWMTX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown