Scroll batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Scroll is an L2 smart contract platform for decentralized applications on Ethereum. Scroll was founded by Sandy Peng, Haichen Shen, and Ye Zhang and launched in 2023.

SCR tokenholders own and govern the protocol. Scroll's investors include Polychain, Qiming Venture Partners, etc.

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Blockchains (L2)

Scroll

# Scroll Active users (weekly) Methodology

## 1. Overview

Weekly active users (WAU) measures the number of unique users who have performed at least one transaction on the Scroll blockchain within a 7-day rolling window. This metric answers the business question: "How many distinct users are actively using Scroll on a weekly basis?" The calculation uses HyperLogLog data structures to efficiently approximate unique user counts across rolling 7-day periods, providing multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

The metric is calculated using the following raw data sources:

- **Scroll blockchain transactions**: Complete transaction records from the Scroll L2 blockchain including transaction hashes, sender addresses, and block timestamps
- **Transaction metadata**: Block timestamps that determine when transactions occurred for proper time-based aggregation
- **User identification data**: Transaction sender addresses (`from_address`) used to identify unique users performing blockchain activity

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Extract daily active users from raw Scroll transaction data
- **Logic**: Process raw Scroll blockchain transactions to identify unique users per day. Each transaction sender address represents an active user. Filter transactions by block timestamp and truncate to daily boundaries. Classify all users as "Transaction sender" type and assign to the "blockchains_l2" market sector and "blockchain" business segment. Set chain identifier as "scroll" with null product ID.
- **Key fields**: `block_timestamp` for temporal aggregation, `from_address` for user identification, `transaction_hash` for transaction tracking, `chain` set to "scroll"

### Component B: Daily active user aggregation  
- **Purpose**: Create daily unique user counts with proper deduplication
- **Logic**: Group transaction data by day using timestamp truncation to daily boundaries. Deduplicate user addresses within each day to ensure each user is counted only once per day, regardless of how many transactions they performed. Preserve dimensional attributes including chain, user type, market sector, business segment, and product ID for downstream aggregation.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` deduplicated as `user_dau`, `timestamp_grain` set to "day"

### Component C: HyperLogLog preparation
- **Purpose**: Convert daily user sets into HyperLogLog data structures for efficient weekly aggregation
- **Logic**: Initialize HyperLogLog sketches for each day's unique user set using BigQuery's `hll_count.init()` function with 15-bit precision. Group by timestamp, chain, user type, and product ID dimensions. This probabilistic data structure enables efficient approximate counting when merging across multiple days.
- **Key fields**: `hll` containing HyperLogLog sketch of daily unique users, dimensional grouping fields for aggregation

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine covering all days from the first available data to yesterday. For each day, create a 7-day lookback window by generating timestamps for the current day plus the previous 6 days. Join this window structure with the daily HLL data to ensure complete coverage across all time periods, even when certain days have no transaction activity.
- **Key fields**: `timestamp` as the end date of the 7-day window, `joinday` representing each day within the window

### Final aggregation
- **Combining components**: Join the rolling 7-day window structure with daily HLL data on matching dates. Merge HLL sketches across the 7-day period using `hll_count.merge_partial()` to combine probabilistic user sets, then extract the final unique count using `hll_count.extract()`
- **Currency conversion**: Not applicable - this is a user count metric
- **Time aggregation**: Each timestamp represents the end date of a 7-day rolling window. Weekly active users are calculated as the distinct count of users who were active on any day within that 7-day period
- **Final calculation**: Create four dimensional breakdowns: project-level aggregation (all dimensions nulled), chain-level (grouped by `chain_id`), user type level (grouped by `user_type`), and product level (grouped by `product_id`). Union all breakdowns into a single result set. Apply the finalize_metric_user_wau macro to set proper schema formatting and fill missing days with zero values where appropriate.

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

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

ScrollSCR

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown