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Blockchains (L1)

Movement

# Movement Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the average amount of revenue generated per daily active user on the Movement blockchain. This metric is calculated by dividing the total daily revenue (derived from transaction fees) by the number of unique daily active users (transaction senders). ARPU provides insight into the economic value generated per user and helps assess the network's revenue efficiency and user monetization.

## 2. Data sources

The ARPU calculation relies on the following data sources:
- **Movement blockchain transaction records**: Complete transaction data including sender addresses, gas consumption, gas pricing, block timestamps, and transaction hashes
- **End-of-day native token price feeds**: USD exchange rates for Movement's native token to enable revenue conversion
- **Transaction metadata**: Block-level timing information for daily aggregation boundaries

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Extract and standardize all Movement blockchain transactions
- **Logic**: The system retrieves all transaction records from Movement blockchain logs, filtering for transactions after January 1st, 2000. Gas unit prices are normalized by dividing by 10^8 to correct for decimal precision. A special handling rule processes any transactions with timestamps before 2000-01-01 by reassigning them to December 5th, 2024, likely representing genesis or initialization transactions.
- **Key fields**: `block_timestamp` for timing, `hash` as transaction identifier, `sender` for user identification, `gas_used` for consumption measurement, `gas_unit_price` for fee calculation

### Component B: Daily fee calculation
- **Purpose**: Calculate total transaction fees paid each day
- **Logic**: Transaction fees are computed by multiplying `gas_used` by the normalized `gas_unit_price` for each transaction. These fees are then aggregated by day using timestamp truncation to daily boundaries. The aggregation sums all fee amounts within each 24-hour period to determine total daily fees paid to the network.
- **Key fields**: `timestamp` truncated to day level, `fee_amount` as the sum of individual transaction fees

### Component C: Daily active user counting
- **Purpose**: Count unique users (transaction senders) active each day
- **Logic**: The system identifies unique daily active users by collecting distinct `sender` addresses for each day. Users are classified as "Transaction sender" type. The counting uses HyperLogLog (HLL) data structures for efficient aggregation across multiple dimensions including project-level, chain-level, user-type-level, and product-level breakdowns. This approach enables scalable unique user counting while maintaining accuracy.
- **Key fields**: `timestamp` for daily grouping, `sender` addresses as user identifiers, `user_type` classification

### Component D: Revenue calculation  
- **Purpose**: Convert daily fees to USD revenue
- **Logic**: The daily fee amounts in native tokens are converted to USD using end-of-day price feeds for Movement's native token. This creates the revenue component of the ARPU calculation, representing the total USD value of fees collected from users each day.
- **Key fields**: `fees` converted to USD amounts, `timestamp` for daily alignment

### Final aggregation
- **Combining components**: The ARPU calculation combines the revenue and user metrics by joining them on the daily timestamp. The system ensures both metrics cover the same time periods and data scope
- **Currency conversion**: All fee amounts are converted to USD using end-of-day native token price feeds, providing standardized revenue measurements
- **Time aggregation**: Both revenue and user counts are aggregated to daily boundaries using timestamp truncation, ensuring consistent temporal alignment
- **Final calculation**: ARPU is calculated by dividing the total daily revenue (USD) by the number of unique daily active users, producing an average revenue per user value for each day. Missing days are filled with zeros to maintain continuous time series data

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

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