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Blockchains (L2)

Shape

# Shape Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have executed at least one transaction on the Shape protocol within a rolling 30-day window. This metric provides insights into user engagement and platform adoption by tracking distinct addresses that have initiated transactions over monthly periods. The calculation uses HyperLogLog algorithms for efficient approximation of unique user counts across large datasets and provides breakdowns by different dimensions including chain, user type, and product.

## 2. Data sources

- **Shape blockchain transactions**: Complete transaction records from the Shape blockchain, including transaction timestamps, sender addresses, and transaction hashes
- **Token Terminal chain availability metadata**: Chain-specific availability and quality filters to ensure data completeness and accuracy

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique active users per day from Shape blockchain transactions
- **Logic**: Extracts all transaction records from Shape blockchain data and identifies the `from_address` (transaction sender) as the user. Each transaction sender is classified as a "Transaction sender" user type. Transactions are grouped by day using timestamp truncation, and unique user addresses are counted per day to establish daily active users (DAU). The data includes chain identification as "shape", market sector as "blockchains_l2", and business segment as "blockchain"
- **Key fields**: `block_timestamp` (truncated to day), `user_address` (transaction sender), `chain` (always "shape"), `user_type` (always "Transaction sender")

### Component B: HyperLogLog sketch creation
- **Purpose**: Creates probabilistic data structures for efficient unique user counting across time windows
- **Logic**: Takes the daily active user data and creates HyperLogLog sketches with precision 15 for each combination of day, chain, user type, and product. These sketches enable efficient merging of user sets across multiple days while maintaining approximate uniqueness counts. Each daily user set is converted into an HLL sketch that can later be merged with other days
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (day-level granularity), dimension grouping fields

### Component C: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a complete datespine covering all days from the earliest data point to yesterday, ensuring no gaps in the time series. For each day in the datespine, creates a 30-day lookback window by generating an array of the previous 29 days plus the current day. This produces a comprehensive mapping where each day is associated with its corresponding 30-day period, enabling consistent rolling window calculations even when some days have no activity
- **Key fields**: `timestamp` (target day), `joinday` (each day within the 30-day window)

### Component D: Window-based HLL aggregation
- **Purpose**: Merges daily HLL sketches within each 30-day window
- **Logic**: Joins the rolling window structure with the daily HLL sketches, matching each day in a 30-day window with its corresponding daily user data. For days without activity, the HLL sketch is null, but the window structure ensures complete coverage. This creates a dataset where each target day has associated HLL sketches for all days within its 30-day lookback period
- **Key fields**: `timestamp` (target day), `hll` (daily HLL sketch), dimension grouping fields

### Component E: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across different analytical dimensions
- **Logic**: Creates four separate dimensional views of the MAU data. For project-level MAU, merges all HLL sketches regardless of chain, user type, or product, providing an overall protocol view. For chain-level breakdown, groups by chain and merges HLL sketches within each chain. For user type breakdown, groups by user type and merges sketches within each type. For product breakdown, groups by product and merges sketches within each product. Each breakdown uses HLL merge operations to combine sketches within the 30-day windows and extracts the final unique count
- **Key fields**: `user_mau` (extracted unique count from merged HLL), `dimensions` (breakdown type), dimension-specific fields

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives of the same underlying MAU calculation
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamps with 30-day rolling windows, where each day represents the MAU for the trailing 30-day period ending on that day
- **Final calculation**: For each day and dimensional combination, the final MAU value represents the approximate count of unique users who were active within the 30-day period ending on that day, calculated by merging HyperLogLog sketches and extracting the estimated distinct count

## Key methodology notes

- **HyperLogLog precision**: Uses precision level 15, providing approximately 0.65% standard error for distinct count estimates
- **Rolling window logic**: Each day's MAU includes users active on that day plus the previous 29 days, creating true rolling monthly windows rather than calendar month boundaries
- **Data completeness**: Uses datespine generation to ensure consistent time series coverage even for days with zero activity
- **Incremental processing**: Supports incremental updates with configurable lookback periods to reprocess recent data while maintaining historical calculations
- **Dimensional flexibility**: Provides MAU breakdowns at project, chain, user type, and product levels within a single unified calculation

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

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

ShapeSHAPE

Active users (monthly)

Active users (monthly)

Competitive landscape