Lens provides a full-stack platform combining Lens Chain for blockchain infrastructure and Lens Protocol for modular social primitives. Lens Chain offers instant transaction settlement with stable, low costs powered by GHO, backed by Ethereum security. Lens Protocol provides smart accounts with signless and gasless functionality, customizable usernames, graphs, feeds, and groups.

Lens is the full-stack platform for social and financial experiences, enabling developers to build SocialFi applications on-chain. The platform features Lens Chain as its blockchain infrastructure layer optimized for speed and scalability, and Lens Protocol for composable social primitives including accounts, usernames, graphs, feeds, and groups. Lens simplifies onchain adoption through portable data, simple onboarding via email or phone, and settlements in the USD-native stablecoin GHO.

Lens is built by the Aave team and is currently overseen by a multisig.

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# Lens Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique user addresses that interact with the Lens Protocol within any 7-day rolling window. This metric captures user engagement across all Lens social activities including profile creation, content creation, and social interactions. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across sliding windows while providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

- **Lens Protocol smart contract events**: Parsed event logs from the Lens Hub smart contract deployed on Polygon, including all user interaction events
- **Profile creation events**: `ProfileCreated` events that establish the mapping between profile IDs and user wallet addresses
- **Social interaction events**: `Collected` events for content collection actions and `Followed` events for user following actions
- **Content creation events**: `PostCreated`, `CommentCreated`, and `MirrorCreated` events for various forms of content publishing on Lens
- **Blockchain transaction data**: Polygon transaction timestamps and hashes for temporal ordering and deduplication

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Extracts and classifies all user interactions with the Lens Protocol, identifying different types of user behavior
- **Logic**: Parses Lens Hub smart contract events to identify six types of user interactions. For direct address events (`ProfileCreated`, `Collected`, `Followed`), extracts user addresses directly from event parameters like `to`, `collector`, and `follower`. For profile-based actions (`PostCreated`, `CommentCreated`, `MirrorCreated`), uses the `profileId` parameter and maps it to user addresses through a profile creation lookup table. Each interaction is classified by user type: Profile Creator, Collector, Follower, Poster, Commenter, or Mirroreeer
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for deduplication, `user_address` for user identification, and `user_type` for behavioral classification

### Component B: Daily active user aggregation
- **Purpose**: Aggregates user interactions into daily unique user counts as the foundation for weekly calculations
- **Logic**: Groups all user interactions by truncated daily timestamps, chain, user type, and product dimensions. Identifies unique user addresses per day across all interaction types. Maintains dimensional information (chain as 'polygon', user type classification, market sector as 'social') for downstream breakdowns
- **Key fields**: `timestamp` truncated to daily boundaries, `user_address` for uniqueness counting, `chain` for blockchain identification, and `user_type` for behavioral segmentation

### Component C: Sliding window construction
- **Purpose**: Creates a continuous 7-day sliding window structure to calculate WAU for every date
- **Logic**: First generates a complete datespine covering the full time range from the earliest DAU data to the current date minus one day. Then creates sliding windows by cross-joining each date with the previous 6 days using timestamp array generation. This produces a table where each row represents a specific date and one of the 7 days in its lookback window, enabling efficient window-based aggregations
- **Key fields**: `timestamp` as the target date for WAU calculation and `joinday` representing each day within the 7-day lookback window

### Component D: HyperLogLog user counting
- **Purpose**: Efficiently counts unique users across the 7-day windows using probabilistic data structures
- **Logic**: Joins the sliding window structure with daily user data, matching on the lookback days to the actual DAU timestamps. Initializes HyperLogLog sketch data structures for each daily user set with 15-bit precision. This enables approximate unique user counting with high accuracy while maintaining efficient memory usage for large-scale aggregations across multiple dimensions
- **Key fields**: `hll` containing the HyperLogLog sketch data for probabilistic counting, along with all dimensional fields for grouping

### Component E: Dimensional breakdowns
- **Purpose**: Calculates WAU across different business dimensions for comprehensive analytics
- **Logic**: Creates four separate dimensional views by grouping the HLL data differently. Project-level WAU aggregates across all dimensions for overall protocol metrics. Chain-level WAU groups by blockchain (currently Polygon only). User type WAU segments by interaction behavior (Profile Creator, Collector, etc.). Product-level WAU allows for future product segmentation. Each breakdown uses HLL merge operations to combine daily sketches within the 7-day windows, then extracts the final unique user count
- **Key fields**: `dimensions` indicating the breakdown type, dimension-specific fields (`chain_id`, `user_type`, `product_id`), and `user_wau` containing the final unique user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single comprehensive dataset, with null values for non-applicable dimension fields in each breakdown
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily boundaries with 7-day sliding windows, ensuring each date represents unique users active in that day and the previous 6 days
- **Final calculation**: Applies the `finalize_metric_user_wau` macro to standardize schema, fill missing dates with zero values, add metadata fields, and ensure consistent output formatting across all Lens metrics

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

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

Lens

Active users (weekly)

Active users (weekly)

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