Lenders deposit funds into Shoebill Finance and receive interest payments from borrowers (supply-side fees).

Shoebill Finance is a non-custodial lending protocol built on Manta Pacific. Shoebill Finance was founded by the pseudonymous Paguinfo and launched in 2023.

Shoebill Finance’s team own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue).

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Lending

Shoebill Finance

# Shoebill Finance Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Shoebill measures the number of unique users who interact with the Shoebill lending protocol within any 30-day rolling window. This metric answers the question "How many distinct users are actively engaging with Shoebill's lending markets each month?" The calculation uses HyperLogLog (HLL) sketches for efficient approximate distinct counting across large datasets and provides dimensional breakdowns by chain, user type, and product to enable detailed analysis of user engagement patterns.

## 2. Data sources

The MAU calculation relies on the following raw data sources:
- **Blockchain events**: Parsed Shoebill protocol event logs from the Manta blockchain, specifically capturing lending and borrowing interactions
- **Smart contract events**: Four types of protocol events - `Borrow`, `Mint` (deposits), `Redeem` (withdrawals), and `RepayBorrow` - across four token markets (sbEth, sbStone, sbUsdc, sbwUsdm)
- **User interaction data**: User addresses extracted from transaction parameters in protocol events, categorized by interaction type
- **Transaction metadata**: Block timestamps and transaction hashes for temporal analysis and deduplication

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses interacting with Shoebill protocol and categorizes them by interaction type
- **Logic**: Extracts user addresses from parsed Shoebill protocol events across four token markets on Manta network. Each event type (Borrow, Mint, Redeem, RepayBorrow) captures specific user parameters (borrower, minter, redeemer, payer) and maps them to standardized user types (Borrower, Depositor, Withdrawer, Repayer). All interactions are classified under the lending market sector and business segment.
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` for user identification, `user_type` for interaction classification, `chain` for network identification, and `product_id` for market segmentation

### Component B: Daily active user calculation  
- **Purpose**: Aggregates unique user addresses per day while maintaining dimensional breakdowns
- **Logic**: Groups user interactions by day using timestamp truncation and creates distinct counts of user addresses per day. Maintains dimensional attributes including chain, user type, market sector, business segment, and product ID. Applies chain availability filtering to ensure data quality and completeness.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing distinct user addresses, `timestamp_grain` set to 'day', and dimensional attributes for segmentation

### Component C: HLL sketch initialization
- **Purpose**: Converts daily distinct user counts into HyperLogLog sketches for efficient approximate counting
- **Logic**: Initializes HLL sketches with precision parameter 15 for each day's distinct user count. Groups data by timestamp, timestamp grain, chain, user type, and product ID to maintain dimensional structure while enabling efficient aggregation across time windows.
- **Key fields**: `hll` sketches containing compressed user count data, maintaining all dimensional grouping fields for later aggregation

### Component D: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each date to calculate monthly active users
- **Logic**: Generates a datespine covering all dates from the earliest available data to yesterday. For each date, creates join relationships with the previous 29 days using timestamp array generation. This produces a table where each row represents a date and one day within its 30-day lookback window, enabling proper windowed aggregation.
- **Key fields**: `timestamp` representing the current date, `joinday` representing each day within the 30-day window

### Final aggregation
- **Combining components**: Joins the rolling window structure with daily HLL sketches, then merges HLL sketches across the 30-day window to produce monthly active user counts
- **Currency conversion**: Not applicable for this user-based metric
- **Time aggregation**: Uses 30-day rolling windows where each date represents the count of unique users active in the preceding 30 days (including the current date)
- **Final calculation**: Merges HLL sketches using `hll_count.merge_partial()` across the 30-day window, then extracts the final distinct count using `hll_count.extract()`. Creates separate dimensional breakdowns for project level (all users combined), chain level, user type level, and product level, then unions all breakdowns together. The finalize_metric_user_mau macro applies standardized schema formatting and fills missing days with zero values for complete time series data.

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

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

Shoebill Finance

Active users (monthly)

Active users (monthly)

Competitive landscape