Creators create and sell NFTs on Zora. They receive a cut of the primary sales (supply-side fees).

Zora is a non-custodial NFT minting protocol built on Ethereum and launched in 2021.

Team and investors own and govern the protocol. They currently take a cut the primary sales paid by collectors (revenue).

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Zora

# Zora Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Zora measures the number of unique users who interact with the Zora NFT protocol within any 7-day rolling window. This metric captures both NFT minters (users who purchase or mint NFTs) and drop creators (users who create NFT collections) across Ethereum, Optimism, and Base networks. The calculation uses HyperLogLog sketches to efficiently deduplicate users across multiple days while providing breakdowns by chain, user type, and product dimensions.

## 2. Data sources

- **Blockchain events**: Parsed event logs from Ethereum, Optimism, and Base networks capturing Zora protocol activities
  - `Sale` events from 721Drop contracts tracking NFT minting transactions
  - `CreatedDrop` events from Factory721 and NFTCreator contracts tracking collection creation
  - `Purchased` events from 1155 contracts tracking fungible token purchases
  - `SetupNewContract` events from Factory1155 and Creator1155Factory contracts tracking fungible collection setup
- **Event parameters**: User addresses extracted from event fields including `to`, `creator`, and `sender` parameters
- **Transaction metadata**: Block timestamps and transaction hashes for temporal analysis and deduplication

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify all user interactions with Zora protocol across supported chains
- **Logic**: Parse blockchain event logs from Zora v1 contracts on Ethereum, Optimism, and Base. Extract user addresses from different event parameter fields depending on the event type. For `Sale` events, extract the `to` address as the minter. For `CreatedDrop` events, extract the `creator` address as the drop creator. For `Purchased` events, extract the `sender` address as the minter. For contract setup events, extract the `creator` address as the drop creator.
- **Key fields**: `block_timestamp` for temporal grouping, `transaction_hash` for deduplication, `chain` for network identification, user addresses from various event parameters, derived `user_type` field categorizing users as either 'Minter' or 'Drop Creator'

### Component B: Daily active user calculation  
- **Purpose**: Calculate unique daily active users grouped by relevant dimensions
- **Logic**: Aggregate the extracted user activities by truncating timestamps to daily boundaries. Group users by timestamp, chain, user type, and product ID. Create distinct counts of user addresses within each group to eliminate duplicate activities by the same user on the same day. Initialize HyperLogLog sketches for each daily user set to enable efficient merging in subsequent steps.
- **Key fields**: Daily `timestamp` boundary, `chain` identifier, `user_type` classification, `product_id` for product-level analysis, `user_dau` containing deduplicated user addresses, HLL sketch representation for efficient set operations

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each date to calculate weekly active users
- **Logic**: Generate a complete datespine covering all days from the first recorded activity through yesterday to ensure no gaps in the analysis. For each date in the datespine, create a 7-day window by joining with the current date plus the previous 6 days. This creates overlapping windows where each day appears in 7 different weekly calculations. Join the window dates with the daily HLL sketches to collect all user data needed for weekly aggregation.
- **Key fields**: `timestamp` for the end date of each weekly window, `joinday` representing each day within the 7-day window, HLL sketches for each day's user set

### Component D: Dimensional breakdowns
- **Purpose**: Calculate weekly active users across multiple dimensional views
- **Logic**: Merge HyperLogLog sketches across each 7-day window to calculate unique weekly active users while properly handling users who are active on multiple days within the same week. Create four separate dimensional breakdowns: overall project level (aggregating all dimensions), by chain (preserving chain splits), by user type (preserving minter vs creator splits), and by product ID (preserving product-level splits). For each breakdown, set unused dimension fields to null and apply the appropriate grouping.
- **Key fields**: `user_wau` containing the final unique weekly active user count, `dimensions` field indicating the breakdown type, preserved dimension fields for chain, user type, or product ID as applicable

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with standardized schema. Each row represents weekly active users for a specific combination of date and dimensional filter.
- **Time aggregation**: Apply 7-day rolling windows where each date represents the end of a week-long period. Users active on any day within that 7-day window are counted once using HyperLogLog deduplication.
- **Missing data handling**: Use the finalize_metric_user_wau macro to standardize the final schema and fill missing dates with zero values to ensure consistent time series data.
- **Final calculation**: The metric value represents the count of unique user addresses that performed any tracked activity (minting, purchasing, or creating collections) within each 7-day rolling window, broken down by the specified dimensional filters.

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

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

ZoraZORA

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown