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World Mobile Chain

# World Mobile Chain Active users (daily) Methodology

## 1. Overview

WorldMobileChain Daily Active Users (DAU) measures the number of unique telecom service subscribers who are active each day on the WorldMobileChain network. This metric tracks user engagement with telecom services by monitoring ERC20 token transfers from a specific service contract. The calculation identifies unique recipient addresses from transfer events as active subscribers and provides breakdowns across multiple dimensions including project-level, chain-level, user type, and product categories.

## 2. Data sources

- **WorldMobileChain ERC20 Transfer events**: Parsed `Transfer` events from the WorldMobileChain blockchain, specifically from contract `0xd7d10d554fb33a37a7720e5666590205dbf5f482` which represents telecom service subscriptions
- **WorldMobileChain transaction data**: Blockchain transaction records containing timestamps and transaction hashes for transfer events
- **Token transfer logs**: Event logs capturing the movement of tokens between addresses, where recipients represent active service subscribers

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify active telecom service subscribers from blockchain transfer events
- **Logic**: Filter ERC20 `Transfer` events specifically from contract address `0xd7d10d554fb33a37a7720e5666590205dbf5f482`, which represents the telecom service contract. Extract the recipient address from each transfer event's `to` field, treating each unique recipient as an active telecom subscriber. All users are classified with the user type 'Telecom service active subscriber' and assigned to the 'worldmobilechain' chain. The `product_id` is set to null as there are no product-level distinctions in this implementation.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for event identification, `to` address mapped to `user_address`, `user_type` hardcoded as 'Telecom service active subscriber'

### Component B: Daily user aggregation
- **Purpose**: Calculate daily distinct active users from the extracted user activity
- **Logic**: Truncate the block timestamp to daily boundaries using day-level granularity. Group all user activity by the truncated timestamp, chain, user type, and product ID. Count distinct user addresses within each day to determine the number of unique active subscribers. Apply incremental processing logic to only process recent data when updating existing calculations, looking back a configurable number of days from the maximum partition date.
- **Key fields**: `timestamp` truncated to day level, `timestamp_grain` set to 'day', `user_dau` containing the distinct user address, dimension fields for grouping

### Component C: HyperLogLog sketch generation
- **Purpose**: Create memory-efficient data structures for scalable distinct counting across multiple dimensional breakdowns
- **Logic**: Convert the daily distinct user counts into HyperLogLog (HLL) sketches with 15-bit precision using the `hll_count.init` function. These sketches enable approximate distinct counting that scales efficiently for large datasets and can be merged across different dimensional groupings. Group the data by timestamp, timestamp grain, chain, user type, and product ID to maintain the dimensional structure while creating the HLL representations.
- **Key fields**: `hll` containing the HyperLogLog sketch representation of user counts, all original dimension fields preserved for downstream aggregations

### Component D: Multi-dimensional breakdowns
- **Purpose**: Calculate DAU across different analytical dimensions for comprehensive reporting
- **Logic**: Create four separate dimensional views of the data: 1) Project-level aggregation combining all dimensions into a single total, 2) Chain-level breakdown maintaining chain identification while nulling other dimensions, 3) User type-level breakdown preserving user type classification, and 4) Product ID-level breakdown (though product ID is null for this protocol). Each breakdown uses `hll_count.merge_partial` to combine HLL sketches and `hll_count.extract` to get the final distinct count. Set unused dimension fields to null using safe casting to maintain consistent schema across all breakdowns.
- **Key fields**: `dimensions` indicating the breakdown type, dimension-specific fields populated or nulled appropriately, `user_dau` containing the final extracted distinct count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type-level, and product ID-level) into a single result set with consistent schema structure. Each breakdown contributes rows with different dimension combinations while maintaining the same column structure.
- **Currency conversion**: No currency conversion required as this metric counts users rather than monetary values
- **Time aggregation**: Daily boundary logic using timestamp truncation to day level, with incremental processing for efficient updates of recent data
- **Final calculation**: Apply the `finalize_metric_user_dau` macro to standardize the output schema, implement a fill-zeros strategy for missing days, and set the data ID to 'worldmobilechain'. Apply chain availability filters to ensure data quality and consistent reporting across the supported blockchain network.

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

Unique revenue-generating users over a fixed 24-hour window.

World Mobile ChainWMTX

Active users (daily)

Active users (daily)

Competitive landscape

Chain breakdown