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World Mobile Chain

# World Mobile Chain Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses for World Mobile Chain measures the number of unique addresses that received tokens from World Mobile's telecom service contract within a 7-day rolling window. This metric tracks active subscribers of World Mobile's telecom services by monitoring token transfer activity to user addresses. The calculation uses HyperLogLog approximation algorithms to efficiently count distinct addresses across time windows.

## 2. Data sources

The calculation relies on the following raw data sources:
- **World Mobile Chain ERC20 transfer events**: Parsed `Transfer` events from the World Mobile Chain blockchain, specifically filtering transfers from the contract address `0xd7d10d554fb33a37a7720e5666590205dbf5f482` (World Mobile's telecom service contract)
- **Transaction metadata**: Block timestamps and transaction hashes associated with transfer events
- **Chain data**: World Mobile Chain block and transaction records

## 3. Step-by-step calculation

### Component A: User identification from transfer events
- **Purpose**: Identifies unique telecom service subscribers from blockchain transfer activity
- **Logic**: Filters World Mobile Chain ERC20 transfer events to include only transfers from the designated telecom service contract address. 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.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identification, `to` address as `user_address`, and fixed classification as telecom service activity

### Component B: Daily active user aggregation
- **Purpose**: Creates daily snapshots of distinct active subscribers
- **Logic**: Groups transfer events by day using timestamp truncation to day granularity. Counts distinct recipient addresses per day to identify unique active subscribers. Maintains dimensional attributes including chain identifier, user type classification, and product identification.
- **Key fields**: Daily truncated `timestamp`, distinct `user_address` counts as `user_dau`, chain and user type classifications

### Component C: HyperLogLog sketch creation
- **Purpose**: Enables efficient approximate distinct counting for large datasets
- **Logic**: Creates HyperLogLog sketches for each day's unique user addresses using the `hll_count.init()` function with precision parameter 15. These probabilistic data structures allow memory-efficient counting of distinct values across time windows without storing actual address values.
- **Key fields**: Daily HLL sketches (`hll`) representing compressed distinct user sets

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for weekly active user calculation
- **Logic**: Generates a complete datespine covering all days from the earliest data through yesterday. For each day in the datespine, creates associations with the previous 6 days plus the current day (total 7-day window). This ensures consistent weekly calculations even when some days have no activity.
- **Key fields**: Target `timestamp` for each week-ending date, `joinday` timestamps for the 7-day lookback period

### Component E: Weekly aggregation with HLL
- **Purpose**: Calculates weekly active users using rolling 7-day windows
- **Logic**: For each day, merges HLL sketches from all 7 days in the rolling window using `hll_count.merge_partial()`. Extracts the final distinct count using `hll_count.extract()` to get the approximate number of unique addresses active within the 7-day period. This approach handles overlapping users across days efficiently.
- **Key fields**: Weekly `user_wau` counts from merged HLL sketches

### Component F: Dimensional breakdown
- **Purpose**: Provides multiple aggregation levels for analysis flexibility
- **Logic**: Creates separate aggregations for different dimensional views including project-level totals ('project_dbt_tag'), chain-level breakdowns ('chain'), user type classifications ('user_type'), and product-specific counts ('product_id'). Each breakdown maintains the same weekly active user calculation logic but groups data differently.
- **Key fields**: `dimensions` identifier, dimensional attributes (`chain_id`, `user_type`, `product_id`), and corresponding `user_wau` values

### Final aggregation
- **Combining components**: Selects weekly active user counts from the WAU aggregation model, specifically filtering for 'chain' and 'project_dbt_tag' dimensions to get both project-wide totals and chain-specific breakdowns
- **Metric transformation**: Directly assigns `user_wau` values to `active_addresses_weekly`, treating active users and active addresses as equivalent for blockchain protocols
- **Standardization**: Applies the `finalize_metric_active_addresses_weekly` macro to ensure consistent output schema, fills missing periods with zero values using 'fill_zeros' strategy, and applies chain availability filtering to exclude data outside operational periods
- **Final calculation**: The final weekly active addresses count represents the approximate number of distinct addresses that received tokens from World Mobile's telecom service contract within any 7-day rolling period, calculated using HyperLogLog approximation for efficiency

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

Unique active addresses over a rolling 7-day window.

World Mobile ChainWMTX

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown