website

Wallet providers

Phantom

# Phantom Active users (monthly) Methodology

## 1. Overview

Phantom Monthly Active Users (MAU) measures the number of unique users who actively used Phantom wallet services within a rolling 30-day period. The metric identifies active usage by tracking users who paid fees to Phantom's fee collection accounts on the Solana blockchain. This provides a reliable indicator of genuine wallet engagement, as fee-paying transactions represent actual usage of Phantom's services rather than passive wallet ownership.

## 2. Data sources

The calculation relies on the following blockchain data sources:

- **Solana native transfers**: SOL transfer records showing native Solana token movements between addresses, including transaction signers and amounts
- **Solana SPL token transfers**: Transfer records for SPL (Solana Program Library) tokens, including token addresses, amounts, and transaction signers
- **Phantom fee collection accounts**: A predefined set of 8 Solana addresses (`25mYnjJ2MXHZH6NvTTdA63JvjgRVcuiaj6MRiEQNs1Dq`, `9yj3zvLS3fDMqi1F8zhkaWfq8TZpZWHe6cz1Sgt7djXf`, etc.) that serve as Phantom's fee collection endpoints

## 3. Step-by-step calculation

### Component A: SOL fee collection tracking
- **Purpose**: Identifies users who paid SOL fees to Phantom wallet services
- **Logic**: Extracts native SOL transfers where the recipient address matches one of Phantom's predefined fee collection accounts. Filters out transfers where the transaction signer is also a fee collection account to avoid counting internal Phantom operations. Records the transaction signer as the active user, along with the SOL token address and transfer amount
- **Key fields**: `block_timestamp` for daily aggregation, `transaction_signer` as the user identifier, `amount` for fee value, hardcoded `token_address` for SOL

### Component B: SPL token fee collection tracking  
- **Purpose**: Identifies users who paid fees using SPL tokens to Phantom wallet services
- **Logic**: Extracts SPL token transfers where the recipient owner address matches one of Phantom's fee collection accounts. Similar to SOL fees, excludes transfers where the transaction signer is a fee collection account. Captures the specific SPL token address being transferred as fees
- **Key fields**: `block_timestamp` for daily aggregation, `transaction_signer` as the user identifier, `token_address` for the specific SPL token, `amount` for fee value

### Component C: Daily active user aggregation
- **Purpose**: Combines fee-paying users from both SOL and SPL transfers into daily unique user counts
- **Logic**: Unions the SOL and SPL fee transactions, truncating timestamps to daily boundaries. Deduplicates users within each day across both fee types, ensuring users who paid both SOL and SPL fees on the same day are counted only once. Assigns all users the category 'Trader' as the user type, indicating they actively used trading or transaction services
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing unique user addresses, `user_type` set to 'Trader', `chain` identifying Solana

### Component D: HyperLogLog initialization
- **Purpose**: Converts daily user sets into memory-efficient probabilistic data structures for scalable counting
- **Logic**: Groups daily user data by timestamp, chain, user type, and product dimensions. Initializes HyperLogLog sketches with precision parameter 15 for each group, allowing efficient approximate counting of large user sets without storing individual user addresses
- **Key fields**: `hll` containing the HyperLogLog sketch data structure, grouped dimensions for later breakdown analysis

### Component E: Rolling 30-day window creation
- **Purpose**: Establishes the time windows for calculating monthly active users
- **Logic**: Generates a complete date spine covering all days from the earliest data through yesterday. For each target date, creates join relationships with the previous 29 days plus the target day itself, forming 30-day rolling windows. Uses BigQuery's timestamp array generation to ensure no missing dates in the analysis period
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window for that target date

### Component F: MAU window aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 30-day periods to calculate monthly user counts
- **Logic**: Joins the rolling window dates with the daily HyperLogLog data, collecting all daily sketches that fall within each 30-day period. Groups by the target timestamp and dimensional breakdowns, then merges the HyperLogLog sketches using BigQuery's `hll_count.merge_partial` function and extracts the final approximate user count
- **Key fields**: `user_mau` containing the estimated unique user count over the 30-day period

### Component G: Multi-dimensional breakdown
- **Purpose**: Provides MAU calculations across different analytical dimensions
- **Logic**: Creates four separate views of the MAU data: project-level (overall Phantom metrics), chain-level (by blockchain), user-type level (by user category), and product-level (by specific product features). Each breakdown nullifies irrelevant dimensions while preserving the relevant grouping dimension. Results are combined using union operations to provide a comprehensive analytical dataset
- **Key fields**: `dimensions` indicating the breakdown type, dimensional fields like `chain_id`, `user_type`, and `product_id` populated only for relevant breakdowns

### Final aggregation
- **Combining components**: The multi-dimensional breakdowns are unioned together into a single result set, with each row representing MAU for a specific dimension combination and time period
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 30-day windows calculated daily, providing a smooth trend of monthly active users without the volatility of fixed monthly boundaries
- **Final calculation**: The final MAU value represents the approximate count of unique users who paid fees to Phantom within any 30-day rolling period, calculated using HyperLogLog probabilistic counting for computational efficiency

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

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

Phantom

Active users (monthly)

Active users (monthly)

Competitive landscape