Validators process transactions and receive fees from users (supply-side fees).

Gnosis Chain is a smart contract platform for decentralized applications (a blockchain). Gnosis was founded by Martin Köppelmann and Stefan George and launched in 2015.

GNO holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). 

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Blockchains (L1)

Gnosis

# Gnosis Active users (weekly) Methodology

## 1. Overview

This metric measures the number of unique users who have initiated at least one transaction on the Gnosis blockchain within any 7-day rolling window. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across overlapping weekly periods. The metric answers the business question: "How many distinct users are actively transacting on Gnosis each week?" and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Gnosis blockchain transactions**: Complete transaction records from the Gnosis blockchain including sender addresses, recipient addresses, block timestamps, gas usage, and transaction hashes
- **Token metadata**: The wxDAI token contract address (`0xe91d153e0b41518a2ce8dd3d7944fa863463a97d`) which serves as the native token for the Gnosis chain
- **Table modification timestamps**: System metadata tracking when underlying data tables were last updated, used for incremental processing optimization

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique users who initiated transactions on each day
- **Logic**: Extracts transaction sender addresses from Gnosis blockchain transaction records. Each transaction's `from_address` field represents a user who initiated that transaction. Block timestamps are truncated to daily boundaries to group transactions by calendar day. Users are classified with the type "Transaction sender" and associated with market sector "blockchains_l1" and business segment "blockchain". Product ID remains null as this represents chain-level activity rather than specific product usage.
- **Key fields**: `from_address` (user identifier), `block_timestamp` (truncated to day), `user_type` (set to "Transaction sender"), chain identifier set to "gnosis"

### Component B: HyperLogLog aggregation for daily users
- **Purpose**: Creates probabilistic data structures to efficiently count unique daily users
- **Logic**: For each day's set of unique user addresses, initializes a HyperLogLog sketch with precision level 15. This probabilistic counting structure allows efficient merging of user sets across different time windows without storing actual user addresses. Daily user sets are grouped by timestamp, chain, user type, and product ID to maintain proper dimensional breakdowns.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily boundary), `chain_id`, `user_type`, `product_id`

### Component C: Date spine and rolling window construction
- **Purpose**: Creates complete time series coverage and 7-day rolling windows
- **Logic**: Generates a continuous date spine from the earliest available daily active user data through the current date minus one day. For each date in this spine, creates a 7-day lookback window consisting of the current day plus the 6 preceding days. This ensures every possible 7-day period is covered without gaps. For incremental processing, the date range starts from 29+ days before the last table modification time to ensure proper window coverage.
- **Key fields**: `timestamp` (main date), `joinday` (each day within the 7-day window)

### Component D: Weekly user window aggregation  
- **Purpose**: Combines daily user data across 7-day rolling windows
- **Logic**: Joins the 7-day windows with the daily HyperLogLog data to capture all users active within each weekly period. Each date's 7-day window includes users who were active on that date or any of the 6 preceding days. This creates overlapping weekly periods where a user active on multiple days within a week is counted only once per weekly window.
- **Key fields**: `timestamp` (window end date), `hll` (daily user sketches), dimension fields for grouping

### Component E: Multi-dimensional WAU calculation
- **Purpose**: Calculates weekly active users across different analytical dimensions
- **Logic**: Merges HyperLogLog sketches across each 7-day window and extracts the final unique user count. Creates separate calculations for four dimensional breakdowns: project-level (all users regardless of other dimensions), chain-level (grouped by chain), user-type-level (grouped by user classification), and product-level (grouped by product ID). Each breakdown nullifies the non-relevant dimension fields while preserving the grouping dimension.
- **Key fields**: `user_wau` (final unique user count), `dimensions` (breakdown type identifier), dimension-specific fields

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset containing WAU metrics at project, chain, user-type, and product levels within a single table structure
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows with daily granularity, where each day represents the end date of a weekly period. Missing days are filled with zero values to ensure complete time series coverage
- **Final calculation**: The final weekly active user count for any given date represents the number of unique addresses that initiated at least one transaction during the 7-day period ending on that date, calculated by merging and extracting counts from HyperLogLog probabilistic data structures

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

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

GnosisGNO

Active users (weekly)

Active users (weekly)

Competitive landscape