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Lending

Prime Numbers

# Prime Numbers Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who actively interact with the Primenumbers lending protocol within a 30-day rolling window. This metric provides insight into user engagement and platform adoption by counting distinct addresses that perform lending-related activities (borrowing, lending, repaying, depositing, or withdrawing) on any given day over the previous 30 days. The calculation uses HyperLogLog approximation for efficient computation while maintaining accuracy.

## 2. Data sources

- **XDC blockchain event logs**: Parsed smart contract events from Primenumbers lending protocol including `LendingPool_Borrow_event`, `LendingPool_FlashLoan_event`, `LendingPool_Repay_event`, `LendingPool_Deposit_event`, and `LendingPool_Withdraw_event`
- **Transaction metadata**: Block timestamps and transaction hashes from XDC Network for temporal ordering and deduplication

## 3. Step-by-step calculation

### Component A: User interaction identification
- **Purpose**: Identify all users who interact with the Primenumbers lending protocol and classify them by activity type
- **Logic**: Extract user addresses from five different lending event types on XDC blockchain. For borrow, repay, deposit, and withdraw events, the user address is captured from the `user` field. For flash loan events, the user address comes from the `initiator` field. Each interaction is classified by user type (Borrower, Repayer, Depositor, Withdrawer) and tagged with product identifier 'primenumbersv1' and business segment 'lending'. All events include block timestamp for temporal analysis.
- **Key fields**: `user_address`, `user_type`, `block_timestamp`, `transaction_hash`, `product_id`, `chain`

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique active users from raw lending interactions
- **Logic**: Group all user interactions by day using timestamp truncation to day level. Apply distinct count on user addresses to identify unique users per day, regardless of how many transactions they performed. Maintain dimensional attributes including chain, user type, and product identifier for downstream breakdowns.
- **Key fields**: `timestamp`, `user_dau`, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Create memory-efficient approximate distinct user counts using HyperLogLog algorithm
- **Logic**: Initialize HyperLogLog sketches with precision 15 for each day's unique user set. Group by timestamp, chain, user type, and product dimensions. This approach enables efficient merging across time windows while maintaining accuracy for large user sets.
- **Key fields**: `hll` (HyperLogLog sketch), dimensional grouping fields

### Component D: 30-day rolling window construction
- **Purpose**: Create a complete time series and establish 30-day rolling windows for MAU calculation
- **Logic**: Generate a datespine covering all days from the first recorded event to the current date minus one day. For each day in the datespine, create join relationships with the previous 29 days (total 30-day window) using timestamp array generation. This ensures complete coverage even for days with no activity and enables consistent MAU calculation across all time periods.
- **Key fields**: `timestamp`, `joinday` (dates within each 30-day window)

### Component E: Window aggregation and dimensional breakdowns
- **Purpose**: Aggregate HLL sketches across 30-day windows and create multiple dimensional views
- **Logic**: Join the rolling window structure with daily HLL sketches to capture all user activity within each 30-day period. Merge HLL sketches across the time window for each dimensional combination. Create four separate breakdown levels: project-level (all dimensions aggregated), chain-level (grouped by chain), user-type-level (grouped by user type), and product-level (grouped by product identifier). Extract final distinct user counts from merged HLL sketches.
- **Key fields**: `user_mau`, `dimensions`, `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with unused dimensions set to null for each breakdown level
- **Time aggregation**: Apply daily timestamp grain with 30-day rolling window logic, ensuring each day represents unique users active over the prior 30 days
- **Final calculation**: Extract approximate distinct user counts from merged HyperLogLog sketches, providing MAU values for each dimensional breakdown and timestamp

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

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

Prime NumbersPRFI

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown