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.

github

twitter

discord

website

Blockchains (L1)

Ronin

# Ronin Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the count of unique wallet addresses that initiated at least one transaction on the Ronin blockchain within any 7-day period. This metric answers the business question: "How many distinct users are actively transacting on Ronin each week?" The calculation uses HyperLogLog sketches for memory-efficient distinct counting and provides multiple dimensional breakdowns including project-level, chain-level, user type, and product-level aggregations.

## 2. Data sources

- **Blockchain transaction data**: Ronin blockchain transaction records containing sender addresses and block timestamps
- **Chain availability data**: Configuration data that defines operational periods for the Ronin blockchain to filter out downtime or maintenance periods

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify unique daily active users from transaction data
- **Logic**: Extract all transaction records from Ronin blockchain data, focusing on the sender address (`from_address`) and block timestamp. Each transaction sender is classified as a 'Transaction sender' user type. Timestamps are truncated to daily granularity to group activity by calendar day. Apply distinct operation on user addresses per day to eliminate duplicate counting of users who made multiple transactions on the same day.
- **Key fields**: `block_timestamp` for timing, `from_address` as user identifier, `transaction_hash` for transaction reference, with static classifications of chain as 'ronin' and user type as 'Transaction sender'

### Component B: Daily user aggregation with HLL
- **Purpose**: Create memory-efficient daily user count representations
- **Logic**: Transform daily distinct user lists into HyperLogLog sketches using `hll_count.init` function with precision parameter 15. This enables efficient storage and merging of large user sets across time windows. Group data by timestamp, chain, user type, and product dimensions while maintaining the HLL sketch for each combination.
- **Key fields**: `timestamp` at daily granularity, `hll` sketch containing user set, dimensional grouping fields `chain_id`, `user_type`, `product_id`

### Component C: Date spine and rolling window construction
- **Purpose**: Ensure complete time coverage and create 7-day rolling windows
- **Logic**: Generate a complete date spine covering the entire data range to handle missing days where no transactions occurred. For each date in the spine, create a 7-day window consisting of the current date plus the 6 preceding days using `generate_timestamp_array`. This creates a cross-join structure that maps each target date to all dates within its 7-day window.
- **Key fields**: `timestamp` as target date, `joinday` representing each day within the 7-day window

### Component D: Window-based user aggregation
- **Purpose**: Calculate weekly active users using rolling 7-day windows
- **Logic**: Join the windowed date structure with daily HLL user data to collect all user activity within each 7-day period. For each target timestamp and dimensional combination, gather HLL sketches from all 7 days in the window. Apply safety margins for incremental processing to ensure data completeness during updates.
- **Key fields**: `timestamp` as target date, dimensional fields `chain_id`, `user_type`, `product_id`, and `hll` sketches from window period

### Component E: Dimensional breakdown calculation
- **Purpose**: Create multiple views of WAU data across different dimensions
- **Logic**: Generate four separate dimensional breakdowns: project-level (all dimensions null), chain-level (chain_id populated), user type level (user_type populated), and product-level (product_id populated). For each breakdown, merge HLL sketches using `hll_count.merge_partial` across the 7-day window, then extract the final distinct count using `hll_count.extract`. Set non-relevant dimensional fields to null for each breakdown type.
- **Key fields**: `dimensions` indicating breakdown type, relevant dimensional field populated with actual values, others set to null, `user_wau` containing final count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, creating rows for project-level, chain-level, user type, and product-level WAU calculations
- **Currency conversion**: Not applicable - this is a user count metric with no currency values
- **Time aggregation**: Daily timestamps represent the end date of each 7-day rolling window, with each row showing WAU for the period ending on that date
- **Final calculation**: Apply finalization macro to standardize schema, fill missing periods with zero values, and apply any additional business rules. Filter results for incremental processing to maintain data freshness while avoiding unnecessary recomputation.

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

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

RoninRON

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape