Social

Story Protocol

# Story Protocol Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Story Protocol measures the number of unique addresses that submitted transactions on the Story blockchain within a rolling 30-day period. This metric provides insights into user engagement and adoption trends for the Story Protocol ecosystem. The calculation uses a rolling window approach where each day's MAU represents the distinct count of users who were active within the preceding 30 days (including the current day).

## 2. Data sources

The MAU calculation relies on the following raw data sources:
- **Story blockchain transactions**: Complete transaction records from the Story Protocol blockchain containing sender addresses and execution timestamps
- **Block metadata**: Block timestamps for accurate temporal analysis and daily aggregation boundaries
- **Transaction traces**: Detailed transaction execution data including sender addresses (`from_address`) used to identify active users

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique active users per day from Story Protocol transactions
- **Logic**: Extracts transaction sender addresses from Story blockchain transaction records. Each transaction's `from_address` field represents an active user. Block timestamps are truncated to day boundaries to create daily user activity snapshots. Only transaction senders are considered active users (classified as "Transaction sender" user type). All activity is attributed to the Story chain and categorized under the "blockchains_l2" market sector and "blockchain" business segment.
- **Key fields**: `block_timestamp` for temporal analysis, `from_address` as user identifier, `transaction_hash` for transaction context, `chain` set to "story"

### Component B: HyperLogLog initialization for efficient counting  
- **Purpose**: Prepares daily user data for efficient distinct counting across rolling windows
- **Logic**: Converts each day's unique user addresses into HyperLogLog (HLL) data structures with 15-bit precision. This probabilistic data structure enables efficient merging of user sets across multiple days while maintaining near-exact distinct counts. Daily user sets are grouped by timestamp, chain, user type, and product dimensions to support various analytical breakdowns.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `hll` structures initialized from daily user sets

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each target date
- **Logic**: Constructs a complete datespine covering all days from the first recorded transaction through the previous day. For each date in the datespine, generates a 30-day lookback window by creating timestamps for the current day and the preceding 29 days. Uses cross-join operations to create all combinations of target dates with their corresponding 30-day windows, ensuring no gaps in the time series even if certain days have no activity.
- **Key fields**: `timestamp` as target date, `joinday` representing each day within the 30-day window

### Component D: Window aggregation and HLL merging
- **Purpose**: Calculates distinct user counts across 30-day rolling windows
- **Logic**: Joins the rolling window framework with daily HLL user data to collect all relevant user activity within each 30-day period. Merges HLL structures across all days within each window to produce a single distinct user count. This approach efficiently handles overlapping user activity across multiple days while avoiding double-counting users who were active on multiple days within the same 30-day period.
- **Key fields**: `timestamp` for the target date, `hll` structures merged across the 30-day window

### Component E: Dimensional breakdowns
- **Purpose**: Provides MAU analysis across different analytical dimensions
- **Logic**: Calculates MAU separately for multiple dimensional breakdowns: project-level MAU (overall Story Protocol activity), chain-level breakdown (Story chain), user type breakdown (Transaction sender), and product-level breakdown (currently null for Story). Each breakdown extracts distinct user counts from the merged HLL structures while grouping by the appropriate dimensional attributes. Dimensions not relevant to each breakdown are set to null.
- **Key fields**: `dimensions` indicating breakdown type, `chain_id`, `user_type`, `product_id` as dimensional attributes, `user_mau` as final distinct user count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive MAU dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable as MAU is a count-based metric
- **Time aggregation**: Daily MAU calculations using rolling 30-day windows, with results available for each day showing the preceding 30-day user activity
- **Final calculation**: Extract distinct user counts from merged HyperLogLog structures using `hll_count.extract()` function. Apply data quality filters based on blockchain data availability. Fill missing days with zero values using the `finalize_metric_user_mau` macro to ensure complete time series coverage.

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

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

Story ProtocolIP

Active users (monthly)

Active users (monthly)

Competitive landscape