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Blockchains (L2)

Superseed

# Superseed Active users (daily) Methodology

## 1. Overview

The Superseed daily active users (DAU) metric measures the number of unique addresses that initiated at least one transaction on the Superseed blockchain per day. This metric provides insights into user engagement and network adoption by tracking distinct transaction senders across daily time periods. The calculation identifies unique user addresses from transaction activity and aggregates them to daily boundaries.

## 2. Data sources

The metric is built from the following raw data sources:

- **Superseed blockchain transactions**: Complete transaction records from the Superseed network containing sender addresses, timestamps, and transaction metadata
- **Transaction logs**: Raw blockchain transaction data including `from_address` fields that identify transaction initiators and `block_timestamp` fields for temporal analysis

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify and standardize user activity from raw Superseed blockchain transactions
- **Logic**: Extract transaction sender addresses from Superseed blockchain transaction records. Cast the `from_address` field to string format to standardize user identifiers and cast `block_timestamp` to timestamp format for temporal analysis. Add standardized metadata including chain identifier ('superseed'), user type classification ('Transaction sender'), market sector ('blockchains_l2'), and business segment ('blockchain'). This creates a clean dataset of user activity events with consistent formatting and classification.
- **Key fields**: `user_address` (transaction sender), `block_timestamp` (transaction time), `transaction_hash` (unique transaction identifier), and standardized metadata fields

### Component B: Daily aggregation
- **Purpose**: Count distinct active users per day from the standardized user activity data
- **Logic**: Truncate the `block_timestamp` field to daily boundaries using day-level granularity to group transactions by date. Count distinct `user_address` values within each day to identify unique active users. Include additional dimensions like `chain`, `user_type`, and `product_id` for segmentation. Apply incremental processing logic to efficiently update recent data by filtering to recent date ranges when updating the model.
- **Key fields**: `timestamp` (daily boundary), `user_dau` (distinct user address), `timestamp_grain` ('day'), and dimensional metadata

### Final aggregation
- **Combining components**: The daily user activity data flows directly from user activity extraction to daily aggregation within the same processing pipeline
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Timestamps are truncated to daily boundaries, creating discrete 24-hour periods for user counting
- **Final calculation**: The final metric value represents the count of distinct `user_address` values per day, where each address that initiated at least one transaction on that day contributes exactly one to the daily active user count

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

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

SuperseedSUPR

Archived

Active users (daily)

Active users (daily)

Competitive landscape