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Blockchains (L2)

Unichain

# Unichain Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that initiated at least one transaction on the Unichain network within any 7-day rolling window. This metric provides insight into user engagement and platform adoption by tracking how many distinct users interact with the blockchain on a weekly basis. The calculation uses HyperLogLog (HLL) probabilistic data structures to efficiently estimate unique user counts across overlapping time windows.

## 2. Data sources

- **Unichain transaction records**: Raw blockchain transaction data containing block timestamps, transaction hashes, and sender addresses from the Unichain network
- **Chain availability configuration**: Internal data quality filters that ensure proper chain coverage and data reliability

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique active users per day from blockchain transactions
- **Logic**: Extract all Unichain transactions and identify users as the sender addresses (`from_address` field). Group transactions by day using timestamp truncation to day boundaries. Count distinct user addresses per day to create Daily Active Users (DAU) records. Initialize HyperLogLog sketches for each daily user count to enable efficient merging in subsequent rolling window calculations. Apply chain availability filters to ensure data quality.
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (user identifier), `user_address` (standardized user field), `timestamp` (day-truncated), `user_dau` (unique daily users)

### Component B: Rolling 7-day window construction  
- **Purpose**: Create overlapping 7-day windows for weekly user counting
- **Logic**: Generate a complete datespine covering all dates from the earliest DAU record to the current date to ensure no missing timestamps. For each date in the datespine, create a 7-day window that includes the current date plus the previous 6 days. Join this window structure with the daily HLL data to map each day's user sketches to all relevant weekly windows.
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day lookback), `hll` (HyperLogLog sketch for efficient unique counting)

### Component C: Dimensional breakdown calculation
- **Purpose**: Calculate WAU across multiple analytical dimensions
- **Logic**: Group the windowed data by different dimensional combinations to create separate breakdowns. Create four distinct views: project-level (overall WAU), chain-level (by blockchain), user-type-level (by user classification), and product-level (by specific products). For each dimensional grouping, merge all HyperLogLog sketches within each 7-day window and extract the approximate unique count.
- **Key fields**: `dimensions` (breakdown type), `chain_id` (blockchain identifier), `user_type` (user classification), `product_id` (product identifier), `user_wau` (weekly active users count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single output table, with null values for non-applicable dimension fields in each breakdown
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the previous 6 days, creating overlapping weekly periods
- **Final calculation**: Apply the `finalize_metric_user_wau` macro to standardize the output schema, fill any missing days with zero values, set proper metadata fields, and ensure consistent formatting across all dimensional breakdowns

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

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

UnichainUNI

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown