Base batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Base is an Ethereum Layer 2 (L2) offering a secure, low-cost, builder-friendly way for anyone, anywhere, to build onchain.

Coinbase team owns and governs the protocol. Coinbase is a public company and its investors include Andreessen Horowitz, Vanguard Group, ARK Investment Management, etc.

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Blockchains (L2)

Base

# Base Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the average daily revenue generated per active user on the Base blockchain network. This metric provides insights into user monetization by dividing the total daily gas fee revenue by the number of daily active users. ARPU helps assess the economic value and engagement intensity of users on the Base network.

## 2. Data sources

The ARPU calculation relies on the following raw data sources:

- **Base network transactions**: Complete transaction records from the Base blockchain including gas usage metrics, gas prices, sender addresses, and Layer 1 data availability costs
- **End-of-day price feeds**: Token price data for WETH (Wrapped Ethereum) used to convert ETH-denominated gas fees to USD values
- **Transaction metadata**: Block timestamps and transaction identifiers for temporal aggregation and deduplication

## 3. Step-by-step calculation

### Component A: Base network gas fee calculation

- **Purpose**: Calculate the total gas fees paid by users on the Base network, representing the revenue component of ARPU
- **Logic**: Extract transaction data from Base network including Layer 2 execution costs and Layer 1 data availability costs. For L2 fees, multiply `gas_used` by `gas_price` and divide by 1e18 to convert from wei to ETH. For L1 fees, multiply `l1_gas_used` by `l1_gas_price` and `l1_fee_scalar`, then divide by 1e18. Sum both L1 and L2 gas fees for each transaction. Aggregate total fees by day using timestamp truncation to daily boundaries.
- **Key fields**: `gas_used` (L2 computation costs), `gas_price` (L2 gas price), `l1_gas_used` (L1 data costs), `l1_gas_price` (L1 gas price), `l1_fee_scalar` (L1 fee multiplier), `block_timestamp` (transaction timing)

### Component B: USD conversion of gas fees

- **Purpose**: Convert ETH-denominated gas fees to USD values for standardized revenue measurement
- **Logic**: Use end-of-day WETH price feeds from Ethereum to convert gas fee amounts from ETH to USD. Apply the conversion using the convert_to_usd_and_correct_decimals macro, which matches daily gas fees with corresponding daily WETH prices. Since Base uses ETH as its native token, WETH price serves as the appropriate conversion rate.
- **Key fields**: `fees` (total ETH gas fees), converted to USD using daily price feeds

### Component C: Base network daily active users calculation

- **Purpose**: Count distinct active users on Base network to serve as the denominator in ARPU calculation
- **Logic**: Extract transaction sender addresses (`from_address`) from Base network transactions and count distinct addresses per day. Use HyperLogLog sketches for efficient distinct counting, which enables scalable computation across large transaction volumes. Truncate transaction timestamps to daily boundaries and group by day to get daily distinct user counts. Create multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations.
- **Key fields**: `from_address` (transaction sender), `block_timestamp` (transaction timing), `user_address` (standardized user identifier)

### Component D: HyperLogLog aggregation for user counting

- **Purpose**: Efficiently aggregate distinct user counts across different dimensional breakdowns
- **Logic**: Initialize HyperLogLog sketches for each user address within daily boundaries. Merge HyperLogLog sketches across different grouping dimensions to create various user count breakdowns. Extract final distinct counts from merged sketches. This approach enables efficient distinct counting while supporting multiple dimensional views of the same underlying user data.
- **Key fields**: `hll` (HyperLogLog sketch), `user_dau` (extracted distinct user count)

### Final aggregation

- **Combining components**: Join daily revenue totals with daily active user counts using the calculate_arpu macro, which performs the division of total daily revenue by daily active user count for each day
- **Currency conversion**: Revenue is converted to USD using end-of-day WETH price feeds, ensuring consistent currency representation across time periods
- **Time aggregation**: Both revenue and user metrics use daily boundaries created by truncating transaction timestamps to day level, ensuring aligned temporal aggregation
- **Final calculation**: ARPU equals daily revenue in USD divided by daily active user count, with the finalize_metric_arpu macro applying schema standardization and filling missing days with zero values to maintain complete time series continuity

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

Base

Average revenue per user (ARPU)

Average revenue per user (ARPU)

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