website

Blockchains (L2)

Superseed

# Superseed Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Superseed measures the number of unique addresses that have initiated transactions on the Superseed blockchain over a rolling 30-day window. This metric provides insight into user engagement and platform adoption by counting distinct users who have been active within any 30-day period. The calculation uses HyperLogLog algorithms for efficient aggregation and provides breakdowns across multiple dimensions including chain, user type, and product categories.

## 2. Data sources

- **Superseed blockchain transactions**: Raw transaction records from the Superseed blockchain containing transaction metadata, timestamps, and sender addresses
- **Transaction sender addresses**: User wallet addresses that initiated transactions, serving as unique user identifiers
- **Block timestamps**: Precise timestamps for when transactions were included in blocks, used for temporal grouping and window calculations

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Identify and classify unique users from raw transaction data
- **Logic**: Extract transaction sender addresses from Superseed blockchain transactions and classify them as transaction senders. Each unique `from_address` represents a distinct user, with the `block_timestamp` providing the temporal context for when the user was active. Users are categorized under the 'blockchains_l2' market sector and 'blockchain' business segment, with 'Transaction sender' as the user type classification.
- **Key fields**: `block_timestamp` for timing, `from_address` as user identifier, `user_type` for classification

### Component B: Daily active users calculation  
- **Purpose**: Aggregate unique users by day and create HyperLogLog sketches for efficient monthly aggregation
- **Logic**: Group transactions by day using timestamp truncation to daily boundaries. Count distinct user addresses per day, creating HyperLogLog sketches with 15-bit precision to enable efficient merging in later steps. This daily aggregation captures the unique users active on each specific day while preparing the data structure for rolling window calculations.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing user addresses, `hll` sketches for aggregation

### Component C: Rolling window creation
- **Purpose**: Build 30-day rolling windows to capture monthly active user periods
- **Logic**: Generate a complete datespine covering the full time range to ensure no missing days. For each day, create a 30-day lookback window by joining that day with the previous 29 days using timestamp arrays. This creates overlapping 30-day periods where each day represents the end of a monthly measurement window. The datespine prevents gaps that could skew user counts during periods of low activity.
- **Key fields**: `timestamp` as window end date, `joinday` for individual days within each window

### Component D: Monthly aggregation with HyperLogLog
- **Purpose**: Combine daily user counts into monthly totals while avoiding double-counting
- **Logic**: For each 30-day window, merge the HyperLogLog sketches from all days within that window using HyperLogLog merge operations. This efficiently counts distinct users across the entire 30-day period without double-counting users who were active on multiple days. Extract the final distinct count from the merged HyperLogLog sketch to get the monthly active user total.
- **Key fields**: Merged `hll` sketches, extracted `user_mau` counts

### Component E: Dimensional breakdowns
- **Purpose**: Create separate MAU calculations for different analytical dimensions
- **Logic**: Generate four separate breakdowns of the monthly active user data: project-level aggregation (combining all dimensions), chain-level breakdown, user type breakdown, and product breakdown. Each breakdown groups the data differently while maintaining the same underlying MAU calculation methodology. Union all dimensional views into a single result set with a `dimensions` field indicating the breakdown type.
- **Key fields**: `dimensions` indicating breakdown type, dimension-specific fields like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union the four dimensional breakdowns (project, chain, user type, product) into a single comprehensive dataset, with null values for non-applicable dimension fields in each breakdown
- **Currency conversion**: Not applicable - this metric counts users rather than monetary values
- **Time aggregation**: Uses rolling 30-day windows where each day represents the endpoint of a monthly measurement period, providing daily updates of monthly active user counts
- **Final calculation**: The monthly active users count represents the distinct number of unique addresses that initiated at least one transaction during any 30-day rolling window, calculated using HyperLogLog merge operations for efficiency and accuracy

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

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

SuperseedSUPR

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape