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

Ronin is a smart contract platform for decentralized applications (a blockchain). Ronin was founded by Sky Mavis and launched in 2021.

RON holders own and govern the protocol. They currently don’t take a cut of the total transaction fees paid by users (revenue). Sky Mavis' investors include a16z, Binance, Animoca Brands, etc.

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Blockchains (L1)

Ronin

# Ronin Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Ronin measures the number of unique addresses that have sent at least one transaction on the Ronin blockchain within a rolling 30-day period. This metric provides insight into user engagement and adoption by tracking distinct transaction senders over time. The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale user deduplication across multiple time periods and dimensions.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Ronin blockchain transactions**: Complete transaction records from the Ronin blockchain including transaction hashes, block timestamps, and sender addresses
- **Transaction metadata**: Block-level timing information used to establish daily boundaries and chronological ordering
- **Address data**: Sender addresses (`from_address`) from each transaction, representing user activity
- **Chain metadata**: Ronin chain identifiers and classification data for dimensional analysis

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses from transaction activity
- **Logic**: Extracts transaction sender addresses from Ronin blockchain transactions. Each transaction represents a user interaction, with the `from_address` field identifying the active user. Data is classified with standard metadata including chain identifier, market sector (`blockchains_l1`), business segment (`blockchain`), and user type (`Transaction sender`)
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `from_address` as the user identifier, `chain` for network classification

### Component B: Daily active user calculation
- **Purpose**: Aggregates unique users per day to create daily active user counts
- **Logic**: Truncates transaction timestamps to daily boundaries using day-level granularity. Deduplicates user addresses within each day to count distinct active users. Applies incremental processing filters to handle only recent data updates efficiently
- **Key fields**: `timestamp` truncated to day level, `user_address` for deduplication, `user_type` and `product_id` for dimensional grouping

### Component C: HyperLogLog initialization
- **Purpose**: Creates probabilistic counting sketches for efficient user deduplication
- **Logic**: Initializes HyperLogLog sketches for each daily user set with 15-bit precision. Groups data by timestamp, chain, user type, and product to create separate HLL sketches for each dimensional combination. This enables efficient merging of user sets across time periods
- **Key fields**: `hll` sketch containing compressed user data, dimensional grouping fields for breakdown analysis

### Component D: Rolling window construction
- **Purpose**: Creates a 30-day sliding window for MAU calculation
- **Logic**: Generates a complete datespine covering the full analysis period to ensure no missing days. For each target date, creates a 30-day lookback window by generating timestamps from 29 days prior through the current date. Joins this window structure with daily HLL data to create a comprehensive view of user activity across each 30-day period
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window

### Component E: Dimensional aggregations
- **Purpose**: Calculates MAU across different analytical dimensions
- **Logic**: Creates separate aggregations for project-level, chain-level, user-type-level, and product-level analysis. For each dimension, groups HLL sketches appropriately and merges them using HyperLogLog union operations. Extracts final user counts from merged sketches to produce distinct user estimates. Null values are used for non-applicable dimensions in each breakdown
- **Key fields**: `dimensions` indicating the aggregation level, `user_mau` containing the final distinct user count, dimensional identifier fields

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows with daily boundary logic, ensuring complete coverage through datespine construction
- **Final calculation**: Applies the finalize_metric_user_mau macro to standardize schema, handle missing periods with zero-filling, and apply consistent formatting across all dimensional breakdowns

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

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

RoninRON

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape