Traders buy NFTs either via the Blur aggregator or Blur's own marketplace from creators & and other users. When trading via Blur's own marketplace, traders pay royalty fees to the creators (supply-side fees).

Blur is a non-custodial NFT marketplace and a marketplace aggregator protocol built on Ethereum. Blur was founded by a pseudonymous team and launched in 2022.

BLUR tokenholders own and govern the protocol. They currently do not take a cut of the royalty fees paid by traders (revenue). Blur's investors include Paradigm, eGirl Capital, etc.

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Blur

# Blur Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the average daily revenue generated per active user on the Blur NFT marketplace. This metric is calculated by dividing total daily revenue by the number of daily active users. For Blur, this metric consistently equals zero since Blur operates as a zero-fee marketplace, charging no protocol fees to users while still tracking user activity for business analytics.

## 2. Data sources

- **Blur v1 Exchange events**: Parsed `OrdersMatched` events from the Blur v1 Exchange contract capturing standard NFT trades
- **Blur v2 Exchange events**: Multiple execution event types (`Execution721TakerFeePacked`, `Execution721MakerFeePacked`, `Execution721Packed`, `Execution`) with packed data requiring bitwise operations to extract trade details
- **Seaport events**: `OrderFulfilled` events from Seaport versions 1.1 through 1.6, filtered to include only trades with Blur's specific owner ID (`0x0000000000d80cfcb8dfcd8b2c4fd9c813482938`)
- **Blur Blend traces**: Function call traces for collateral NFT trades (`buyLockedETH`, `buyLocked`, `buyToBorrowLocked`, `buyToBorrowLockedETH`) from the Blend lending protocol
- **Ethereum transaction data**: Transaction records providing user addresses, gas information, and transaction metadata
- **End-of-day price feeds**: Token price data for USD conversion capabilities
- **Token metadata**: Information including decimal precision for proper amount calculations

## 3. Step-by-step calculation

### Component A: Trade data collection
- **Purpose**: Gather all NFT trading activity across Blur's various trading mechanisms
- **Logic**: Collect trades from four distinct sources. For Blur v1, extract `OrdersMatched` events containing seller/buyer addresses, collection addresses, token IDs, and trade prices. For Blur v2, process execution events that use packed data structures, requiring JavaScript user-defined functions to perform bitwise operations and extract `tokenId`, `listingIndex`, trader addresses, order types, prices, and collection addresses from the packed integers. For Seaport trades, process `OrderFulfilled` events across multiple Seaport versions, flatten offer and consideration arrays, and filter for trades matching Blur's owner ID to identify Blur-facilitated trades. For Blend, capture collateral NFT trades through function call traces where NFTs locked as collateral are sold. Each trade record includes timestamps, transaction hashes, participant addresses, token details, and volume amounts.
- **Key fields**: `block_timestamp`, `transaction_hash`, `nft_seller_address`, `nft_buyer_address`, `volume_amount`, `token_address`, `nft_address`

### Component B: User address extraction and processing
- **Purpose**: Identify all unique users participating in Blur trades each day
- **Logic**: Extract both NFT seller and buyer addresses from all collected trades. Handle special cases where the Blur Swap contract (`0x39da41747a83aee658334415666f3ef92dd0d541`) appears as a buyer, replacing it with the actual transaction originator from Ethereum transaction records. Union seller and buyer addresses into a combined dataset, categorizing them by user type. Truncate timestamps to day boundaries to group activity by calendar day. Apply HyperLogLog sketching to efficiently count distinct users across multiple dimensions including overall project level, by chain, by user type, and by product ID.
- **Key fields**: `user_address`, `timestamp`, `user_type`, `chain`, `product_id`

### Component C: Revenue calculation
- **Purpose**: Calculate total daily revenue from protocol fees
- **Logic**: For Blur, set revenue to zero for all time periods and dimensions, reflecting the platform's business model as a zero-fee marketplace that doesn't charge protocol fees to users. This hardcoded zero value is maintained in the same table structure as other fee-generating protocols to ensure consistency in the metrics framework.
- **Key fields**: `revenue` (always 0), `timestamp`, `chain_id`, `product_id`

### Final aggregation
- **Combining components**: The `calculate_arpu` macro divides daily revenue by daily active users. Since Blur's revenue is always zero, this calculation consistently yields zero regardless of user activity levels
- **Currency conversion**: While individual trade volumes are converted to USD using end-of-day price feeds and decimal corrections, this doesn't affect the ARPU calculation since revenue (not trading volume) is the numerator
- **Time aggregation**: All metrics are calculated at daily grain using day-truncated timestamps, ensuring consistent temporal boundaries across revenue and user metrics
- **Final calculation**: ARPU equals daily revenue divided by daily active users, which for Blur equals 0 divided by the daily user count, resulting in 0. The `finalize_metric_arpu` macro applies schema standardization and forward-fills missing days with zeros to maintain complete time series data

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

BlurBLUR

Average revenue per user (ARPU)

Average revenue per user (ARPU)

Competitive landscape