Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Kilt is digital identity solution powered by Polkadot Relay chain.

KILT holders own and govern the protocol.

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Kilt

# Kilt Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed at least one transaction on the Kilt protocol within any 7-day rolling window. This metric provides insight into user engagement and adoption trends over weekly time periods. The calculation uses HyperLogLog data structures for efficient cardinality estimation and creates multiple dimensional breakdowns to analyze user activity across different segments.

## 2. Data sources

- **Kilt blockchain extrinsics**: Raw transaction records from the Kilt protocol containing transaction sender addresses and block timestamps
- **Blockchain transaction data**: Kilt protocol transaction logs with user identification and timing information
- **Chain metadata**: Protocol classification data identifying Kilt as a Layer 2 blockchain in the blockchains sector

## 3. Step-by-step calculation

### Component A: User facts extraction
- **Purpose**: Transform raw Kilt extrinsics into standardized user activity records
- **Logic**: Extracts user addresses from Kilt extrinsics data, identifying transaction senders as active users. Maps block timestamps to user activity and classifies all users under the "Transaction sender" user type. Associates activity with Kilt chain and categorizes under 'blockchains_l2' market sector and 'blockchain' business segment
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` set to 'kilt', `user_type` set to 'Transaction sender'

### Component B: Daily active users calculation  
- **Purpose**: Calculate daily unique active users as the foundation for weekly aggregation
- **Logic**: Groups user activity by day using timestamp truncation to day granularity. Identifies distinct user addresses per day from the user facts. Includes incremental processing logic to efficiently update recent data while maintaining historical records
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `timestamp_grain` set to 'day'

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user lists into HyperLogLog data structures for efficient cardinality estimation
- **Logic**: Initializes HyperLogLog sketches from daily active user lists using precision parameter 15. Groups by timestamp, chain, user type, and product dimensions. Creates compact representations that can be efficiently merged across time windows
- **Key fields**: `hll` containing HyperLogLog sketch data, dimensional fields for grouping

### Component D: Datespine and window creation
- **Purpose**: Ensure complete time series coverage and create 7-day rolling windows
- **Logic**: Generates a complete datespine from the earliest available data to the current date with daily granularity. Creates jointables by mapping each day to its preceding 6 days using generate_timestamp_array, forming 7-day sliding windows. Handles incremental processing by focusing on recent time periods while maintaining historical context
- **Key fields**: `timestamp` for target dates, `joinday` for days within each 7-day window

### Component E: Weekly window aggregation
- **Purpose**: Merge HyperLogLog sketches across 7-day windows to count unique weekly users
- **Logic**: Joins the datespine with daily HyperLogLog data to create 7-day rolling windows. For each day, includes that day plus the preceding 6 days of user activity. Merges HyperLogLog sketches across the 7-day period and extracts final cardinality estimates to determine unique weekly users
- **Key fields**: `hll` merged across 7-day periods, dimensional breakdowns maintained

### Final aggregation
- **Combining components**: Creates multiple dimensional views including project-level totals, chain-level breakdowns, user type segments, and product-level analysis. Each breakdown maintains the same weekly unique user calculation while aggregating across different dimensional cuts
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents users active in that day plus the preceding 6 days. Maintains daily granularity in output while each point represents weekly activity
- **Final calculation**: Extracts unique user counts from merged HyperLogLog sketches using hll_count.extract function. Applies the finalize_metric_user_wau macro to standardize schema, handle missing values with zero-filling strategy, and format for downstream consumption

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

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

KiltKILT

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown