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World Mobile Chain

# World Mobile Chain Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the count of unique addresses that received ERC20 token transfers from World Mobile Chain's telecom service token contract over rolling 30-day periods. This metric represents active subscribers in World Mobile Chain's telecommunications network, where token transfers indicate service usage, rewards, or other subscriber activities. The calculation uses HyperLogLog probabilistic counting for efficient estimation of unique addresses across large datasets.

## 2. Data sources

- **World Mobile Chain blockchain events**: Parsed ERC20 `Transfer` events from World Mobile Chain blockchain
- **Token contract data**: Transfers specifically from contract address `0xd7d10d554fb33a37a7720e5666590205dbf5f482` (World Mobile Chain's telecom service token)
- **Transaction metadata**: Block timestamps and transaction hashes from World Mobile Chain transactions
- **Address data**: Recipient addresses (`to` field) from token transfer events

## 3. Step-by-step calculation

### Component A: User identification from transfers
- **Purpose**: Identify daily active addresses based on token transfer recipients
- **Logic**: Extract all ERC20 transfer events from the specific World Mobile Chain token contract address. Filter transfers to include only those from the designated telecom service token contract. For each transfer event, capture the recipient address, block timestamp, and transaction hash. Classify all recipients as "Telecom service active subscriber" users, indicating they are active participants in World Mobile Chain's telecommunications network.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `to` address as the user identifier, contract `address` for filtering

### Component B: Daily unique address aggregation  
- **Purpose**: Calculate daily distinct active users from transfer events
- **Logic**: Group transfer events by day using timestamp truncation to day granularity. For each day, identify distinct recipient addresses to eliminate multiple transfers to the same address on the same day. Include metadata fields such as chain identifier, user type classification, and product identifier. Store results with daily partitioning for efficient incremental processing.
- **Key fields**: `timestamp` truncated to day, `user_address` as unique identifier, `chain` identifier, `user_type` classification

### Component C: HyperLogLog sketch creation
- **Purpose**: Create probabilistic data structures for efficient cardinality estimation
- **Logic**: Convert daily lists of unique addresses into HyperLogLog sketches with precision parameter 15. Group daily address lists by timestamp, chain, user type, and product identifier. Initialize HLL sketches from the address lists to enable efficient merging across time periods. Store HLL sketches alongside the original grouping dimensions.
- **Key fields**: `hll` sketch data structure, grouping dimensions for breakdown analysis

### Component D: Rolling window construction
- **Purpose**: Build 30-day rolling windows for monthly active address calculation
- **Logic**: Create a complete datespine covering all days from the earliest data through yesterday to ensure no gaps in the time series. For each day in the datespine, generate a 30-day lookback window including the current day and the previous 29 days. Join the datespine with HLL sketches to associate each day in the rolling window with its corresponding address data. Handle missing days by including them in windows but with null HLL data.
- **Key fields**: `timestamp` as window end date, `joinday` for window components, associated HLL sketches

### Component E: Monthly active address estimation
- **Purpose**: Estimate unique addresses over 30-day rolling periods using HLL merging
- **Logic**: For each day's 30-day window, merge all HLL sketches within that window to estimate the total unique addresses. Use HLL merge operations to combine sketches while maintaining cardinality estimation accuracy. Extract the final count estimate from the merged HLL sketch. Group results by the window end date and relevant dimensions.
- **Key fields**: Merged `hll` sketches, extracted `user_mau` counts, timestamp and dimensional groupings

### Component F: Multi-dimensional breakdown
- **Purpose**: Calculate monthly active addresses across different analytical dimensions
- **Logic**: Break down the monthly active address calculation into separate views: project-level aggregation (project_dbt_tag dimension with nulled chain/product/user_type), chain-level aggregation (chain dimension with nulled product/user_type), user type aggregation (user_type dimension with nulled chain/product), and product aggregation (product_id dimension with nulled chain/user_type). Combine all dimensional breakdowns using union operations to create a comprehensive view.
- **Key fields**: `dimensions` field indicating breakdown type, `user_mau` counts, appropriately nulled dimensional fields

### Final aggregation
- **Combining components**: Select monthly active user data specifically for 'chain' and 'project_dbt_tag' dimensions, filtering out other dimensional breakdowns. Rename the `user_mau` field to `active_addresses_monthly` to align with the metric's semantic meaning.
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Uses rolling 30-day windows calculated daily, providing daily snapshots of monthly active address counts
- **Final calculation**: The final metric value represents the estimated count of unique addresses that received token transfers from the World Mobile Chain telecom service contract within any 30-day rolling period, calculated using HyperLogLog probabilistic counting for scalable cardinality estimation

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

Unique addresses active over a rolling 30-day window.

World Mobile ChainWMTX

Active addresses (monthly)

Active addresses (monthly)

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