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Blockchains (L2)

Soneium

# Soneium Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who performed at least one transaction on the Soneium blockchain within a 7-day rolling window. This metric provides insights into user engagement patterns and platform adoption by tracking unique transaction senders over weekly periods. The calculation uses HyperLogLog data structures to efficiently count distinct users across overlapping time windows and provides breakdowns across multiple dimensions including project-level, chain-level, user type, and product categories.

## 2. Data sources

The calculation relies on the following data sources:
- **Soneium blockchain transactions**: Raw transaction records from the Soneium blockchain including transaction hashes, block timestamps, and sender addresses
- **Token Terminal metadata**: Internal classification systems providing market sector categorization, business segment mapping, and chain availability filters

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify unique user addresses from blockchain transaction activity
- **Logic**: Process raw Soneium blockchain transaction records to identify transaction senders. Each transaction's `from_address` field represents a user performing blockchain activity. Apply metadata classification including chain identification as 'soneium', market sector as 'blockchains_l2', business segment as 'blockchain', and user type as 'Transaction sender'. The `product_id` field is set to null as Soneium operates as a unified blockchain platform.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for transaction identification, `from_address` as the user identifier, and classification fields for dimensional analysis

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users from the classified transaction data
- **Logic**: Group transaction data by day using timestamp truncation to day boundaries. For each day, identify all distinct user addresses that performed transactions, creating one record per unique user per day. Each user address becomes a `user_dau` record associated with the corresponding day, chain, user type, and product metadata.
- **Key fields**: `timestamp` representing the day boundary, `user_address` as the unique user identifier, and dimensional fields for `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog initialization for daily data
- **Purpose**: Convert daily user lists into efficient data structures for distinct counting across time windows
- **Logic**: Transform daily active user records into HyperLogLog (HLL) data structures with precision 15. Each day's distinct user addresses are initialized into an HLL structure that enables efficient merging and counting operations. Group data by timestamp, chain, user type, and product dimensions to maintain proper granularity for downstream aggregations.
- **Key fields**: `timestamp` for the day, `hll` containing the HyperLogLog structure with user data, and dimensional grouping fields

### Component D: 7-day rolling window creation
- **Purpose**: Generate overlapping 7-day windows for each day in the time series
- **Logic**: Create a comprehensive datespine covering the full date range from the earliest available data to the current date. For each day in this spine, generate joins to the previous 6 days to create 7-day rolling windows. This approach ensures that every day has a complete 7-day lookback window, even when some days have no transaction activity. The window includes the current day plus the previous 6 days for a total of 7 days.
- **Key fields**: `timestamp` representing the end date of each 7-day window, `joinday` representing each day within the window

### Component E: Weekly distinct user calculation
- **Purpose**: Count distinct users across each 7-day window using HLL merging
- **Logic**: For each 7-day window, merge the HyperLogLog structures from all days within that window. The HLL merge operation efficiently combines distinct user counts across multiple days while avoiding double-counting users who were active on multiple days within the window. Extract the final distinct count from the merged HLL structure to produce the WAU value.
- **Key fields**: `timestamp` for the window end date, merged `hll` structure, and final `user_wau` count

### Component F: Dimensional breakdown aggregation
- **Purpose**: Calculate WAU across multiple analytical dimensions
- **Logic**: Generate separate WAU calculations for four different dimensional breakdowns: project-level (overall), chain-level, user-type-level, and product-level. For each breakdown, group the 7-day window data by the relevant dimensional fields and merge HLL structures accordingly. Set non-relevant dimensional fields to null for each breakdown type. Union all dimensional breakdowns together to create a comprehensive dataset supporting various analytical perspectives.
- **Key fields**: `dimensions` field indicating the breakdown type, relevant dimensional fields populated based on breakdown type, null values for non-relevant dimensions

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single comprehensive dataset containing WAU calculations across all analytical perspectives
- **Currency conversion**: Not applicable - this is a user count metric without monetary values
- **Time aggregation**: Daily calculation with 7-day rolling windows, where each day represents the end date of a 7-day lookback period  
- **Final calculation**: The final WAU value represents the count of distinct user addresses that performed at least one transaction within the 7-day period ending on each given day, calculated using HyperLogLog merge operations for computational efficiency

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

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

Soneium

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown