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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Kilt measures the approximate number of unique user addresses that have submitted at least one transaction (extrinsic) on the Kilt blockchain within a rolling 30-day window. This metric uses HyperLogLog probabilistic counting to efficiently calculate approximate distinct user counts across multiple dimensional breakdowns while maintaining high accuracy with reduced computational overhead.

## 2. Data sources

- **Kilt blockchain extrinsics**: Transaction records from the Kilt blockchain containing sender addresses and block timestamps. Extrinsics on Kilt are equivalent to transactions on other blockchains and represent all user-initiated activity
- **Chain availability filters**: Configuration data that determines which time periods have reliable data coverage for the Kilt network

## 3. Step-by-step calculation

### Component A: User facts extraction
- **Purpose**: Identifies all unique user addresses that performed transactions on a given day
- **Logic**: Extracts transaction sender addresses from Kilt blockchain extrinsics, truncates block timestamps to daily boundaries, and enriches each record with metadata including chain identifier (`kilt`), user type (`Transaction sender`), market sector (`blockchains_l2`), and business segment (`blockchain`). The product ID is set to null as Kilt operates as a single protocol
- **Key fields**: `block_timestamp` (transaction time), `id` (sender address from extrinsics), `user_address` (standardized user identifier)

### Component B: Daily active users calculation  
- **Purpose**: Calculates the count of distinct active users per day using HyperLogLog sketches
- **Logic**: Groups user facts by day and creates HyperLogLog sketches initialized with precision parameter 15 for each unique user address. This probabilistic data structure enables efficient approximate counting of distinct users while maintaining accuracy within 1-2% margin of error. Records are filtered to include only data from supported time periods using chain availability rules
- **Key fields**: `timestamp` (day boundary), `timestamp_grain` (set to `day`), `chain` (always `kilt`), `user_type` (always `Transaction sender`), `hll` (HyperLogLog sketch of user addresses)

### Component C: 30-day rolling window construction
- **Purpose**: Creates overlapping 30-day windows to capture monthly active user periods
- **Logic**: Generates a datespine covering all days from the earliest DAU data to yesterday, then for each day creates a 30-day lookback window (including the current day, so 30 total days). Uses array generation to create joinday arrays spanning from 29 days prior to the current day, then joins these windows with the daily HyperLogLog sketches to capture all user activity within each 30-day period
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within the 30-day window), `hll` (daily user sketches to be merged)

### Component D: Dimensional MAU aggregation
- **Purpose**: Calculates MAU across different dimensional breakdowns
- **Logic**: Merges HyperLogLog sketches across each 30-day window to produce approximate distinct user counts. Creates four separate dimensional views: project-level (all users combined), chain-level (grouped by chain, though only Kilt), user type level (grouped by user type, though only transaction senders), and product level (grouped by product, though null for Kilt). Each breakdown sets irrelevant dimension fields to null while preserving the relevant grouping dimension
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type` (dimensional grouping fields), `user_mau` (extracted distinct count from merged HyperLogLog)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with consistent schema across all breakdown types
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamps representing the end date of each 30-day rolling window, with monthly partitioning for efficient incremental processing
- **Final calculation**: The `finalize_metric_user_mau` macro applies standardized schema formatting, fills missing time periods with zero values, and adds metadata fields including the data ID (`kilt`) to identify the source protocol

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

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

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Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown