opBNB is built on the OP Stack, the same underlying framework as Optimism. It extends the Ethereum Virtual Machine (EVM) to provide faster and cheaper transactions, while leveraging BNB Smart Chain (BSC) for ecosystem integration.

opBNB is an Ethereum-compatible Layer 2 scaling solution designed to improve the performance and affordability of decentralized applications on BNB Chain. It provides high throughput and low fees, making it suitable for gaming, social, and DeFi applications.

opBNB is developed by BNB Chain, which is supported by contributors from the broader Binance ecosystem.

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opBNB

# opBNB Average revenue per user (ARPU) Methodology

## 1. Overview

ARPU measures the average revenue generated per daily active user on the opBNB blockchain. This metric is calculated by dividing daily gas fee revenue by the number of unique daily active users. It provides insight into user monetization and network economics by showing how much revenue each active user generates on average. The calculation uses a ratio of total daily revenue (from L1 and L2 gas fees) divided by the count of unique transaction senders per day.

## 2. Data sources

- **opBNB transaction records**: Complete transaction history including sender addresses, gas usage, gas prices, and L1 rollup cost components
- **Ethereum mainnet gas pricing**: L1 gas prices and fee scalars for opBNB's rollup data availability costs posted to Ethereum
- **End-of-day price feeds**: BNB token prices for USD conversion of gas fee revenue
- **Token metadata**: BNB token contract address and symbol information for the native gas token

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Collect raw transaction data with gas fee components and user addresses
- **Logic**: Extract all opBNB transactions with key fields including `block_timestamp`, `transaction_hash`, `from_address` (transaction sender), gas usage metrics (`gas_used`, `l1_gas_used`), and pricing data (`gas_price`, `l1_gas_price`, `l1_fee_scalar`). The L1 components capture the Ethereum mainnet costs for data availability since opBNB is an optimistic rollup
- **Key fields**: `from_address` identifies unique users, `gas_used` and `gas_price` for L2 execution costs, `l1_gas_used`, `l1_gas_price`, and `l1_fee_scalar` for L1 data availability costs

### Component B: Gas fee calculation
- **Purpose**: Calculate total gas fees paid per transaction combining L1 and L2 costs
- **Logic**: For each transaction, compute L2 gas fee as (`gas_used` × `gas_price`) ÷ 1e18 to convert from wei to BNB. Calculate L1 gas fee as (`l1_gas_used` × `l1_gas_price` × `l1_fee_scalar`) ÷ 1e18. Sum L1 and L2 components to get total gas fee per transaction. This captures both the execution cost on opBNB and the data availability cost on Ethereum
- **Key fields**: `l2_gas_fee` and `l1_gas_fee` components, `block_timestamp` for daily aggregation

### Component C: Daily revenue aggregation
- **Purpose**: Calculate total daily revenue from gas fees in USD
- **Logic**: Group gas fee data by day using `timestamp_trunc` to `day` granularity. Sum total gas fees (`l1_gas_fee` + `l2_gas_fee`) across all transactions per day. Convert BNB-denominated fees to USD using end-of-day price feeds and apply decimal corrections. Apply standardization through the finalize metric process including schema validation and data quality filters
- **Key fields**: Daily `timestamp`, aggregated `revenue` in USD, `chain_id` set to 'opbnb'

### Component D: Daily active user calculation
- **Purpose**: Count unique users (transaction senders) per day efficiently
- **Logic**: Extract unique `from_address` values from transaction data, grouped by `timestamp_trunc` to `day`. Use HyperLogLog (HLL) sketches for scalable distinct counting through `hll_count.init` on user addresses. Create multiple dimension breakdowns including project-level, chain-level, user-type-level, and product-level aggregations. Merge HLL sketches using `hll_count.merge_partial` and extract final counts with `hll_count.extract` for accurate unique user counts
- **Key fields**: Daily `timestamp`, `user_dau` count, dimension breakdowns for different aggregation levels

### Final aggregation
- **Combining components**: Join daily revenue data with daily active user counts by matching on `timestamp` (daily boundaries). Both components use the same daily timestamp truncation for consistent alignment
- **Currency conversion**: Revenue is converted from BNB to USD using end-of-day price feeds before the ARPU calculation. Gas fees are normalized from wei units by dividing by 1e18
- **Time aggregation**: Both revenue and user data use daily boundaries with `timestamp_trunc` to `day` granularity. Missing days are filled with zeros to maintain continuous time series
- **Final calculation**: ARPU = Daily Revenue (USD) ÷ Daily Active Users. The division is performed through the `calculate_arpu` macro which handles the ratio calculation and applies final standardization including schema consistency, zero-filling for missing dates, and data quality filters through the `finalize_metric_arpu` process

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

opBNB

Average revenue per user (ARPU)

Average revenue per user (ARPU)

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