Infrastructure

Maestro

# Maestro Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the average daily revenue generated per active user on the Maestro platform. This metric is calculated by dividing daily revenue by daily active users, providing insight into monetization efficiency and user value generation. The calculation uses fee payments made to Maestro's Solana smart contracts, where users pay fees in native SOL tokens.

## 2. Data sources

- **Solana native transfers**: Native SOL transfer events from the Solana blockchain, specifically transfers directed to Maestro's fee collection account
- **End-of-day price feeds**: Daily SOL-to-USD conversion rates for currency standardization
- **Token metadata**: SOL token metadata for decimal handling and validation

## 3. Step-by-step calculation

### Component A: Fee collection

- **Purpose**: Captures all fee payments made to Maestro platform
- **Logic**: Extracts native SOL transfers directed to Maestro's fee account (`MaestroUL88UBnZr3wfoN7hqmNWFi3ZYCGqZoJJHE36`) from Solana blockchain data. Filters out transfers originating from the same fee account to exclude self-transfers and internal operations. Each qualifying transfer represents a user fee payment.
- **Key fields**: `block_timestamp` for daily aggregation, `transaction_signer` for user identification, `amount` in lamports for fee value, `transaction_id` for deduplication

### Component B: Revenue calculation  

- **Purpose**: Converts raw fee amounts to standardized daily revenue figures
- **Logic**: Aggregates fee transfers by day and converts amounts from lamports to SOL by dividing by 1 billion (1e9). Applies end-of-day SOL price feeds to convert SOL amounts to USD values. Groups all fees by day to produce daily total revenue.
- **Key fields**: `timestamp` truncated to day, `amount` converted to SOL then USD, `chain_id` as 'solana'

### Component C: Daily active users

- **Purpose**: Counts unique users who paid fees each day
- **Logic**: Identifies unique `transaction_signer` addresses from fee transfer events for each day. Uses HyperLogLog (HLL) sketches for efficient distinct counting, especially when aggregating across different dimensional breakdowns (by chain, user type, product). Each unique signer who made at least one fee payment on a given day counts as an active user.
- **Key fields**: `user_dau` as the transaction signer address, `timestamp` for daily grouping, `user_type` categorized as 'Trader'

### Component D: User aggregation

- **Purpose**: Aggregates user counts across different dimensions while maintaining distinct counting accuracy
- **Logic**: Creates HyperLogLog sketches for daily user counts, then merges and extracts final distinct user counts. Produces breakdowns by project level, chain level, user type, and product ID. Uses HLL merge operations to avoid double-counting users who appear in multiple dimensions.
- **Key fields**: `user_dau` as final distinct user count, `dimensions` indicating breakdown level, dimensional fields like `chain_id` and `user_type`

### Final aggregation

- **Combining components**: Revenue and daily active user metrics are joined by date and dimensional attributes (chain, product ID) to create matched daily pairs for ARPU calculation
- **Currency conversion**: All revenue values are converted to USD using end-of-day SOL price feeds, ensuring consistent currency representation across time periods
- **Time aggregation**: Data is aggregated at daily boundaries using timestamp truncation to day level, with block timestamps determining which day each transaction belongs to
- **Final calculation**: ARPU is calculated by dividing the daily revenue (in USD) by the daily active user count for each corresponding day. Days with zero users result in null ARPU values, while days with zero revenue result in zero ARPU values. Missing data points are filled with zeros using the fill_zeros strategy to maintain time series continuity.

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

Maestro

Average revenue per user (ARPU)

Average revenue per user (ARPU)

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