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 (monthly) Methodology

## 1. Overview

The Foundation Monthly Active Users (MAU) metric measures the number of unique users who interact with the Foundation NFT marketplace within any 30-day period. This metric tracks user engagement across all marketplace activities including private sales, auctions, offers, and NFT drops. The calculation uses a rolling 30-day window approach with HyperLogLog probabilistic counting to efficiently handle large-scale unique user aggregations across multiple dimensional breakdowns.

## 2. Data sources

The MAU metric is calculated from the following raw data sources:

- **Ethereum event logs**: Parsed events from Foundation's v1 marketplace smart contracts on Ethereum
- **Foundation marketplace events**: Specific event types including `PrivateSaleFinalized`, `BuyPriceAccepted`, `OfferAccepted`, `ReserveAuctionFinalized`, and `MintFromFixedPriceDrop` events
- **Transaction metadata**: Block timestamps and transaction hashes from Ethereum blockchain data
- **Contract parameters**: User addresses extracted from event parameters including buyers, sellers, and bidders

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identify all user addresses that interact with Foundation marketplace
- **Logic**: Extract user addresses from Foundation marketplace events on Ethereum. Parse specific event parameters including `seller`, `buyer`, and `bidder` addresses from events like `PrivateSaleFinalized`, `BuyPriceAccepted`, `OfferAccepted`, `ReserveAuctionFinalized`, and `MintFromFixedPriceDrop`. Each extracted address is classified as a 'Trader' user type. Include transaction metadata like `block_timestamp` and `transaction_hash` for temporal analysis.
- **Key fields**: User addresses from event parameters (`seller`, `buyer`, `bidder`), `block_timestamp` for timing, `user_type` set to 'Trader', `chain` identifier, `market_sector_id` set to 'nfts', `business_segment_id` set to 'marketplace'

### Component B: Daily active users aggregation
- **Purpose**: Calculate daily unique active users (DAU) as foundation for MAU calculation
- **Logic**: Group user addresses by day using timestamp truncation of `block_timestamp` to daily granularity. Create distinct count of unique user addresses per day. Add metadata including chain identifier, user type, and product categorization. Apply chain availability filters to ensure data quality and completeness.
- **Key fields**: `timestamp` truncated to day level, `timestamp_grain` set to 'day', distinct `user_address` as `user_dau`, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog initialization for efficient set operations
- **Purpose**: Convert daily user counts into HyperLogLog sketches for efficient unique counting across time windows
- **Logic**: Initialize HyperLogLog sketches for each day's unique users with precision level 15. Group by timestamp, chain, user type, and product dimensions. HyperLogLog sketches enable probabilistic unique counting that can be efficiently merged across time periods without storing individual user addresses.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` sketch containing daily unique users

### Component D: Rolling 30-day window creation
- **Purpose**: Create continuous 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest data date to yesterday using `generate_timestamp_array`. For each day in the datespine, create associations with the previous 29 days using another `generate_timestamp_array` to form 30-day windows. This ensures no missing dates and enables consistent rolling window calculations even when some days have no activity.
- **Key fields**: `timestamp` for the end date of each 30-day window, `joinday` for each day within the window

### Component E: MAU calculation using HyperLogLog merging
- **Purpose**: Calculate monthly active users by merging daily HyperLogLog sketches within each 30-day window
- **Logic**: Join the 30-day windows with daily HyperLogLog sketches. For each 30-day period ending on a specific date, collect all HyperLogLog sketches from days within that window. Use `hll_count.merge_partial` to combine sketches and `hll_count.extract` to get the final unique user count. This approach efficiently handles overlapping users across multiple days within the same month.
- **Key fields**: `timestamp` for window end date, merged `hll` sketches, extracted `user_mau` count

### Component F: Dimensional breakdowns
- **Purpose**: Provide MAU metrics across different analytical dimensions
- **Logic**: Create four separate dimensional views of MAU data. First, project-level aggregation combining all chains and user types. Second, chain-level breakdown showing MAU per blockchain. Third, user-type breakdown showing MAU by user classification. Fourth, product-level breakdown showing MAU by specific products. Each breakdown maintains the same HyperLogLog merging logic but groups by different dimensional combinations.
- **Key fields**: `dimensions` field indicating breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), respective dimension values, aggregated `user_mau` counts

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with standardized schema. Each row represents MAU for a specific date and dimensional combination.
- **Schema standardization**: Apply the `finalize_metric_user_mau` macro to ensure consistent field types, naming conventions, and data quality. Fill missing dates with zero values to maintain continuous time series.
- **Time boundaries**: Use monthly partitioning for efficient querying. Apply incremental processing logic with configurable lookback periods to optimize performance while ensuring data completeness.
- **Final calculation**: The final MAU value represents the count of unique users who performed at least one marketplace transaction within any 30-day period ending on the specified date, calculated using probabilistic HyperLogLog counting for scalability and accuracy.

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

Unique revenue-generating users over a rolling 30-day window.

Foundation

Active users (monthly)

Active users (monthly)

Competitive landscape