Foundation enables users to mint and sell their digital art and earn royalties in return (supply-side fees). The platform takes a fee for each successful trade (revenue). 

Foundation aims to build a new creative economy by empowering artists, collectors and other users to buy, sell, and participate in live auctions for various digital pieces of art.

Foundation is a NFT marketplace, featuring live auctions, built on Ethereum. The platform was founded by 
Kayvon Tehranian and it was launched in February 2021. 

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NFT marketplaces

Foundation

# Foundation Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed any trading activity on the Foundation NFT marketplace within a rolling 7-day window. This metric captures user engagement across Foundation's marketplace by tracking buyers and sellers from various transaction types including private sales, accepted offers, auction completions, and fixed-price mints. The calculation uses HyperLogLog (HLL) cardinality estimation for efficient computation of unique users across overlapping time windows.

## 2. Data sources

- **Foundation smart contract events**: Parsed Ethereum event logs from Foundation's marketplace smart contracts, including the Market contract and NFTDropMarket contract
- **Ethereum blockchain data**: Transaction timestamps (`block_timestamp`) and transaction hashes from Ethereum blocks
- **Event types processed**:
  - `PrivateSaleFinalized` events capturing private sale completions
  - `BuyPriceAccepted` events from accepted buy orders
  - `OfferAccepted` events from accepted offers
  - `ReserveAuctionFinalized` events from completed auctions
  - `MintFromFixedPriceDrop` events from fixed-price NFT drops

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identify all unique user addresses participating in Foundation marketplace activity
- **Logic**: Extract user addresses from Foundation marketplace events on Ethereum. Process five different event types from Foundation's smart contracts, capturing both buyer and seller addresses from each transaction. For private sales, buy orders, offers, and auctions, extract both the `buyer` and `seller` addresses. For fixed-price drops, extract the `buyer` address. All users are classified as 'Trader' type since they represent marketplace participants. Each record includes the original `block_timestamp`, `transaction_hash`, and is tagged with chain='ethereum', market_sector='nfts', and business_segment='marketplace'
- **Key fields**: `user_address` (buyer/seller addresses), `block_timestamp`, `transaction_hash`, `chain`, `user_type`, `market_sector_id`, `business_segment_id`

### Component B: Daily active users aggregation
- **Purpose**: Convert raw user activity into daily active user counts while preserving dimensional breakdowns
- **Logic**: Aggregate user addresses to daily granularity by truncating `block_timestamp` to day level using timestamp truncation. Create distinct user records per day to eliminate duplicate addresses within the same day. Maintain dimensional attributes including chain, user type, and product identifiers. Apply chain availability filtering to ensure data completeness. Set `timestamp_grain` to 'day' to mark the temporal resolution
- **Key fields**: `timestamp` (daily), `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (unique daily user address)

### Component C: HyperLogLog sketch initialization  
- **Purpose**: Convert daily user sets into HyperLogLog sketches for efficient cardinality estimation in rolling windows
- **Logic**: Transform daily active user data into HyperLogLog sketches using 15-bit precision for cardinality estimation. Group by timestamp, chain, user type, and product dimensions while initializing HLL sketches from the daily user sets. This enables efficient merging of user sets across overlapping time windows without double-counting users who appear on multiple days
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows to calculate weekly active users
- **Logic**: Generate a complete datespine covering the analysis period to handle missing timestamps. For each day in the datespine, create a 7-day window by generating timestamps for the current day plus the previous 6 days using timestamp array generation. Join the datespine windows with daily HLL data to create rolling window datasets. This ensures every day has a complete 7-day lookback window, filling gaps where no user activity occurred
- **Key fields**: `timestamp` (window end date), `joinday` (dates within window), `hll` (daily sketches)

### Component E: Weekly unique user calculation
- **Purpose**: Calculate unique weekly active users by merging HLL sketches across 7-day windows
- **Logic**: For each 7-day window, merge all HyperLogLog sketches from the constituent days to calculate the unique user count across the full week. Use HLL merge operations to combine daily user sets while avoiding double-counting users who appear on multiple days within the week. Extract the final cardinality estimate from the merged HLL sketch to get the weekly active user count
- **Key fields**: `user_wau` (weekly active users count)

### Final aggregation
- **Combining components**: Create multiple dimensional breakdowns of the weekly active user metric: total project level (all dimensions null), by chain (chain-specific WAU), by user type (user type-specific WAU), and by product (product-specific WAU). Union all dimensional views together into a single result set
- **Time aggregation**: Use rolling 7-day windows where each day represents the WAU for that day plus the previous 6 days. Window boundaries are based on calendar days using timestamp truncation
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize the output schema, set the data_id to 'foundation', and implement zero-filling for missing time periods
- **Final calculation**: The final WAU value represents the count of unique user addresses that performed any Foundation marketplace activity within the 7-day window ending on each 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.

Foundation

Active users (weekly)

Active users (weekly)

Competitive landscape