Social

Story Protocol

# Story Protocol Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who have been active on the Story blockchain over a rolling 7-day period. This metric provides insights into user engagement and platform adoption by tracking distinct transaction senders. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across time windows while supporting multiple dimensional breakdowns.

## 2. Data sources

- **Story blockchain transactions**: Raw transaction records from the Story blockchain containing transaction hashes, block timestamps, and sender addresses
- **Chain availability metadata**: Token Terminal's internal metadata defining when the Story chain became available for tracking and analysis

## 3. Step-by-step calculation

### Component A: User data extraction
- **Purpose**: Identify unique users and their activity timestamps from Story blockchain transactions
- **Logic**: Extract transaction data from Story blockchain records, focusing on the sender address (`from_address`) as the user identifier. Each transaction represents a user action, with the `block_timestamp` providing the timing of activity. All users are classified as "Transaction sender" type since the metric tracks any address that initiates transactions on Story
- **Key fields**: `block_timestamp` for activity timing, `from_address` as user identifier, transaction metadata for context

### Component B: Daily active users calculation
- **Purpose**: Calculate daily distinct active users using probabilistic counting methods
- **Logic**: Group user activity by calendar day boundaries using timestamp truncation. For each day, collect all unique user addresses and initialize HyperLogLog sketches for efficient distinct counting. This approach allows for memory-efficient aggregation of large user sets while maintaining accuracy. Filter data based on incremental processing windows and chain availability periods
- **Key fields**: `timestamp` (truncated to day), `user_address` converted to `user_dau`, HyperLogLog sketches (`hll`) for probabilistic counting

### Component C: Rolling window creation
- **Purpose**: Generate 7-day sliding windows for each day to calculate weekly active users
- **Logic**: Create a comprehensive datespine covering all days from the earliest available data to the current date. For each day in the datespine, generate a 7-day lookback window including the current day and the previous 6 days. This ensures that even days with no activity are included in the calculation, preventing gaps in the time series. Join each day's window with the corresponding daily active user data
- **Key fields**: `timestamp` for the target date, `joinday` for each day within the 7-day window

### Component D: Weekly active users aggregation
- **Purpose**: Calculate distinct users across the 7-day rolling window using HyperLogLog merging
- **Logic**: For each target day, merge the HyperLogLog sketches from all days within the 7-day window. This provides an approximation of distinct users who were active at any point during the 7-day period. The HyperLogLog merge operation efficiently handles overlapping users across multiple days while maintaining accuracy for distinct counting
- **Key fields**: Merged HyperLogLog sketches representing unique users across the 7-day period

### Component E: Dimensional breakdowns
- **Purpose**: Calculate WAU across different analytical dimensions
- **Logic**: Generate separate calculations for multiple dimensional views: project-level (aggregated across all dimensions), chain-level (Story chain specific), user type level (transaction senders), and product level (not applicable for Story, set to null). Each breakdown uses the same HyperLogLog merging logic but groups data differently. Unused dimension columns are set to null using safe_cast operations
- **Key fields**: `dimensions` indicating breakdown type, dimensional identifiers (`chain_id`, `product_id`, `user_type`), `user_wau` as final count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set using UNION ALL operations, creating a comprehensive view with multiple analytical perspectives
- **Currency conversion**: Not applicable - this is a user count metric with no monetary values
- **Time aggregation**: Daily time boundaries with 7-day rolling windows, partitioned by month for efficient incremental processing
- **Final calculation**: Extract distinct user counts from merged HyperLogLog sketches using `hll_count.extract()` function, apply Token Terminal's standardized metric finalization including zero-filling for missing days and schema standardization

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

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

Story ProtocolIP

Active users (weekly)

Active users (weekly)

Competitive landscape