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.

governance

twitter

discord

etherscan

website

NFT marketplaces

Blur

# Blur Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who engage in trading activities on the Blur platform within any 7-day period. This metric captures user engagement across Blur's entire ecosystem, including direct exchange trading, Seaport-routed trades, and Blend collateral transactions. The calculation uses HyperLogLog cardinality estimation for efficient computation of unique user counts across rolling 7-day windows, providing breakdowns by chain, user type (buyer vs seller), and product.

## 2. Data sources

- **Blur exchange events**: Parsed `OrdersMatched` events from Blur v1 Exchange contract and various `Execution` events from Blur v2 Exchange contract on Ethereum
- **Seaport trading events**: Parsed `OrderFulfilled` events from OpenSea's Seaport contracts (versions 1.1 through 1.6) that are attributed to Blur through zone identification
- **Blend collateral transactions**: Parsed function calls for NFT purchases involving collateralized positions (`buyLockedETH`, `buyLocked`, `buyToBorrowLocked`, `buyToBorrowLockedETH`) from Blur's Blend lending protocol
- **Transaction records**: Ethereum transaction data providing context about transaction initiators and gas payers
- **Token metadata**: Information about NFT collections, token contracts, and trading pairs involved in transactions

## 3. Step-by-step calculation

### Component A: Trade data unification
- **Purpose**: Consolidates all trading activities across Blur's ecosystem into a standardized format
- **Logic**: Combines data from four distinct trading venues: Blur v1 direct exchange trades from `OrdersMatched` events, Blur v2 exchange trades from various `Execution` event types, Seaport-based trades filtered to Blur's zone identifier, and Blend collateral NFT trades from lending protocol function calls. Each trade record extracts the `nft_seller_address` and `nft_buyer_address` along with timestamps, chain information, and trade metadata. Seaport trades require complex unpacking of offer and consideration arrays to identify NFT and currency flows. Blur v2 trades use custom bitwise operations to decode packed event parameters. Transaction records are joined to resolve actual user addresses when trades involve intermediate contracts
- **Key fields**: `block_timestamp`, `nft_seller_address`, `nft_buyer_address`, `chain`, `user_type`, `product_id`

### Component B: Daily active users aggregation
- **Purpose**: Converts raw trading data into daily unique user counts by dimension
- **Logic**: Extracts user addresses from the unified trade data, creating separate records for NFT sellers and NFT buyers with appropriate `user_type` classification. Groups trading activity by day using `timestamp_trunc` on `block_timestamp`, along with `chain`, `user_type`, `market_sector_id`, `business_segment_id`, and `product_id` dimensions. Calculates distinct user addresses per day-dimension combination. Initializes HyperLogLog sketches for each daily user count to enable efficient cardinality estimation in downstream calculations
- **Key fields**: `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (as HLL sketch)

### Component C: Rolling window construction
- **Purpose**: Creates the infrastructure for calculating 7-day rolling active user windows
- **Logic**: Generates a complete date spine covering the full time range from the earliest trade date to the current date using `generate_timestamp_array` with daily intervals. For each date in the spine, creates join relationships to the preceding 6 days plus the current day (7 total days) using `unnest` and `generate_timestamp_array`. This creates a mapping where each date is associated with all dates in its 7-day window. Joins this windowed structure with the daily HLL sketches from the DAU calculation, ensuring every day in each window has its corresponding user data
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window), HLL sketches from daily aggregation

### Component D: Weekly unique user calculation
- **Purpose**: Computes unique users across 7-day windows using HyperLogLog merging
- **Logic**: Groups the windowed data by the window end date and dimensional attributes (`chain`, `user_type`, `product_id`). For each group, merges all HyperLogLog sketches from the 7-day window using `hll_count.merge_partial()` to combine cardinality estimates efficiently. Extracts the final unique count using `hll_count.extract()` to get the estimated number of distinct users who were active at least once during the 7-day period. This approach handles overlapping users across days within the window without double-counting
- **Key fields**: `timestamp`, `user_wau` (extracted unique count)

### Final aggregation
- **Combining components**: Creates four distinct dimensional breakdowns of the WAU data: project-level aggregation (combining all chains, user types, and products), chain-level breakdown (preserving chain dimension), user-type breakdown (sellers vs buyers), and product-level breakdown (different Blur products). Each breakdown nullifies non-applicable dimensional fields and unions the results into a single dataset
- **Currency conversion**: Not applicable for user count metrics - no monetary conversion required
- **Time aggregation**: Uses daily boundaries with 7-day rolling windows, where each day represents the count of unique users active in that day plus the preceding 6 days
- **Final calculation**: The final WAU value represents the estimated number of distinct users who performed at least one trading action (as either buyer or seller) within any continuous 7-day period ending on the specified date, calculated using HyperLogLog cardinality estimation for computational efficiency

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

Unique revenue-generating users over a rolling 7-day window.

BlurBLUR

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown