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

Viction is a smart contract platform for decentralized applications (a blockchain). Viction was founded by Ninety Eight and launched in 2018.

VIC holders own and govern the protocol. They currently don't take a cut of the total transaction fees paid by users (revenue). Ninety Eight's investors include Binance Labs, The Spartan Group, Multicoin Capital, etc.

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Blockchains (L1)

Viction

# Viction Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Viction measures the count of unique wallet addresses that have sent at least one transaction on the Viction blockchain within any 30-day rolling period. This metric provides insight into user engagement and platform adoption by tracking distinct active participants over monthly timeframes. The calculation uses HyperLogLog data structures to efficiently count unique users across rolling windows while supporting multiple dimensional breakdowns.

## 2. Data sources

- **Viction blockchain transactions**: Raw transaction records from the Viction blockchain containing sender addresses, timestamps, and transaction identifiers
- **Transaction metadata**: Block timestamps for temporal analysis and transaction hashes for data integrity
- **User classification data**: Internal categorization of users as transaction senders

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify unique daily active users from Viction blockchain transactions
- **Logic**: Extract all transactions from Viction blockchain transaction records and identify unique sender addresses per day. Each transaction's `from_address` represents a user, and the `block_timestamp` is truncated to day boundaries to group activity by date. All users are classified as 'Transaction sender' type, with the chain set to 'viction'. Additional metadata includes market sector as 'blockchains_l1' and business segment as 'blockchain', with product ID set to null.
- **Key fields**: `user_address` (transaction sender), `timestamp` (daily boundary), `chain` (viction), `user_type` (Transaction sender)

### Component B: HyperLogLog preparation  
- **Purpose**: Convert daily user data into efficient counting structures for MAU calculation
- **Logic**: Transform the daily active user data into HyperLogLog (HLL) data structures using BigQuery's `hll_count.init` function with 15-bit precision. Group the data by timestamp, timestamp grain, chain, user type, and product ID to create distinct HLL sketches for each dimensional combination. This enables efficient unique user counting across time periods without storing individual user addresses.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (day), `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete date spine covering all days from the earliest user activity date to the present, ensuring no missing dates in the analysis period. For each date in the spine, create associations with the previous 29 days using `generate_timestamp_array` to establish 30-day rolling windows. Join this date spine with the HLL user data to align user activity with the appropriate time windows, ensuring every day has access to its 30-day historical context.
- **Key fields**: `timestamp` (current date), `joinday` (dates within 30-day window), `hll` (user data for each day)

### Component D: Dimensional MAU aggregation
- **Purpose**: Calculate MAU across different dimensional breakdowns
- **Logic**: Aggregate HLL sketches across the 30-day windows for four different dimensional levels. For project-level MAU, merge all HLL sketches regardless of other dimensions. For chain-level MAU, group by `chain_id` while nullifying other dimensions. For user type-level MAU, group by `user_type` while nullifying other dimensions. For product-level MAU, group by `product_id` while nullifying other dimensions. Use `hll_count.merge_partial` to combine HLL sketches within each group, then `hll_count.extract` to get the final unique user count.
- **Key fields**: `dimensions` (breakdown type), `user_mau` (extracted unique count), dimensional identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing a specific dimensional slice and timestamp
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamps with 30-day rolling windows, where each day's MAU represents unique users active in the trailing 30 days ending on that date
- **Final calculation**: The `user_mau` field contains the final unique user count extracted from the merged HyperLogLog structures, representing the number of distinct wallet addresses that sent transactions within the 30-day period

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

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

VictionVIC

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape