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Blockchains (L1)

Movement

# Movement Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who performed at least one transaction on the Movement blockchain within a rolling 30-day period. This metric answers the business question of user engagement and retention by tracking deduplicated unique addresses that initiated transactions over monthly timeframes. The calculation uses a sophisticated rolling window approach with HyperLogLog data structures to efficiently handle deduplication across overlapping time periods.

## 2. Data sources

- **Movement blockchain transaction records**: Contains transaction-level data including sender addresses, timestamps, transaction hashes, gas usage, and gas pricing information
- **Table metadata**: Incremental processing metadata from table edits cache to optimize data refresh operations and determine processing boundaries

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identify unique users (addresses) active each day as the foundation for MAU calculation
- **Logic**: Extract distinct `sender` addresses from Movement blockchain transaction data, grouped by day. Each transaction sender is classified as a 'Transaction sender' user type. Timestamps are truncated to daily granularity using `timestamp_trunc` to group all transactions within the same calendar day. Special handling applies genesis timestamp normalization where transactions with timestamps before 2000-01-01 are reassigned to '2024-12-05 00:00:00 UTC' to handle genesis block timing issues.
- **Key fields**: `sender` address as user identifier, `block_timestamp` truncated to daily granularity, `user_type` set to 'Transaction sender', classification fields including `chain` as 'movement', `market_sector_id` as 'blockchains_l2', and `business_segment_id` as 'blockchain'

### Component B: HyperLogLog sketch creation  
- **Purpose**: Convert daily user sets into efficient data structures for deduplication across rolling windows
- **Logic**: Transform daily active user counts into HyperLogLog sketches using `hll_count.init` with precision parameter 15. This creates compact probabilistic data structures that enable efficient unique counting and merging across multiple days without storing individual user addresses. Each daily user set becomes an HLL sketch that can be combined with other sketches.
- **Key fields**: `hll` sketch containing compressed user set information, `timestamp` for daily granularity, dimensional breakdowns by `chain_id`, `user_type`, and `product_id`

### Component C: Rolling window generation
- **Purpose**: Create 30-day rolling windows for each day to calculate monthly active users
- **Logic**: Generate a complete datespine covering all days from the earliest transaction date to the current date minus one day. For each day in the datespine, create a 30-day lookback window by generating an array of dates from 29 days prior to the current day. This creates a `jointables` structure where each target date is associated with all dates in its 30-day window, ensuring complete temporal coverage without gaps.
- **Key fields**: `timestamp` as the target date for MAU calculation, `joinday` representing each day within the 30-day lookback window

### Component D: Window-based HLL merging
- **Purpose**: Combine HLL sketches across the 30-day window to calculate deduplicated unique users
- **Logic**: Join the rolling window structure with daily HLL sketches, matching each `joinday` with corresponding daily HLL data. For users active on multiple days within the 30-day window, the HLL sketches automatically handle deduplication. Group by target timestamp and dimensional attributes, then merge HLL sketches using `hll_count.merge_partial` to combine user sets across the entire 30-day period.
- **Key fields**: `timestamp` as target date, `hll` sketches merged across the 30-day window, dimensional groupings preserved from daily data

### Component E: Dimensional breakdowns
- **Purpose**: Generate MAU calculations across different analytical dimensions
- **Logic**: Create separate aggregations for different analytical perspectives: project-level MAU (no dimensional breakdown), chain-level MAU (grouped by `chain_id`), user type-level MAU (grouped by `user_type`), and product-level MAU (grouped by `product_id`). Each breakdown uses `hll_count.extract` to convert merged HLL sketches back to actual unique user counts. Unused dimensions are set to null using `safe_cast(null as string)` for consistent schema structure.
- **Key fields**: `user_mau` as the final unique user count, `dimensions` indicating the breakdown type, relevant dimensional fields populated while others are nulled

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with consistent schema structure
- **Schema standardization**: Apply the `finalize_metric_user_mau` macro with parameters `data_id` as 'movement' and `fill_strategy` as 'fill_zeros' to standardize output schema and fill missing timestamps with zero values
- **Incremental processing**: Use incremental strategy with 30-day lookback margin (`interval 29 + incremental_lookback_days day`) to ensure complete window coverage while optimizing processing performance
- **Final calculation**: Extract unique user counts from merged HLL sketches using `hll_count.extract`, providing the final MAU value representing deduplicated unique users active within each 30-day rolling window

*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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Active users (monthly)

Active users (monthly)

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