Ankr operates a global network of both full and validator nodes on an array of blockchains. These nodes provide the basis for services like Ankr Staking which delegates users’ tokens to blockchain validators and delivers liquid staking rewards (supply-side fees) to users for their contributions to validation efforts.

Ankr is a web3 infrastructure provider of blockchain API/RPC connections, developer tools, and liquid staking products. Ankr was founded and launched in 2017 by Chandler Song, Ryan Fang, and Stanley Wu.

ANKR tokenholders own and govern the protocol. They currently take a cut of the total staking rewards earned by the protocol (revenue). Ankr’s investors include Binance Labs, Block VC, MapleBlock Capital, Bixin Ventures etc.

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Liquid staking

Ankr

# Ankr Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Ankr measures the number of unique users who interacted with Ankr's liquid staking protocols within any 30-day period. This metric captures user engagement across Ankr's staking and unstaking activities on Ethereum and BSC (Binance Smart Chain), providing insights into the protocol's user adoption and retention. The calculation uses a rolling 30-day window approach with HyperLogLog sketches for efficient distinct user counting across multiple blockchain networks.

## 2. Data sources

The metric relies on the following raw data sources:

- **Blockchain events**: Parsed smart contract events from Ankr's liquid staking protocols on Ethereum and BSC, including `StakeConfirmed`, `PendingUnstake`, `Staked`, `UnstakePending`, and `Unstaked` events
- **Transaction data**: Ethereum and BSC transaction records containing timestamps, transaction hashes, log indices, and block numbers for temporal analysis
- **Protocol metadata**: Information about Ankr's contract versions (V1, V2, V2.1) and their deployment blocks to handle protocol upgrades and migrations

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify unique user addresses from Ankr liquid staking interactions
- **Logic**: The system identifies users from parsed smart contract events across multiple Ankr protocol versions on Ethereum and BSC. For Ethereum, it captures users from `ETHStaking_StakeConfirmed_event` (classifying them as 'ETH Staker') and `ETHStakingV2_PendingUnstake_event` (classifying them as 'ETH Unstaker'). For BSC, it handles three contract versions with block-based filtering: V1 contracts (blocks up to 23,545,403), V2 contracts (blocks 23,545,404 to 28,123,525), and V2.1 contracts (blocks 28,123,526+). Each user interaction is classified as either 'BNB Staker' or 'BNB Unstaker' based on the event type.
- **Key fields**: `user_address` (extracted from event parameters like `staker`, `receiverAddress`, `delegator`, `claimer`), `user_type` (classification based on action), `chain` (blockchain network), `block_timestamp` (interaction timing)

### Component B: Daily active user aggregation
- **Purpose**: Aggregate user interactions into daily distinct counts using efficient data structures
- **Logic**: User interactions are grouped by day using timestamp truncation, creating daily snapshots of unique active users. The system maintains dimensional breakdowns by chain, user type, market sector, business segment, and product. HyperLogLog sketches are initialized for each user group to enable memory-efficient distinct counting when aggregating across larger time windows.
- **Key fields**: `timestamp` (daily granularity), `chain` (blockchain identifier), `user_type` (staker/unstaker classification), `user_dau` (user address for distinct counting)

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for monthly active user calculation
- **Logic**: A datespine is generated covering all days from the earliest user interaction to the current date. For each day in the datespine, the system creates a 30-day lookback window (29 previous days plus the current day) using timestamp array generation. This approach ensures that no days are skipped in the analysis, even when there were no user interactions on specific dates.
- **Key fields**: `timestamp` (target date for MAU calculation), `joinday` (each day within the 30-day window)

### Component D: Multi-dimensional breakdown
- **Purpose**: Calculate MAU across different analytical dimensions
- **Logic**: The system creates separate calculations for four different dimensional breakdowns: project-level (aggregating all users regardless of other dimensions), chain-level (showing MAU per blockchain), user-type-level (showing MAU for stakers vs unstakers), and product-level (reserved for future product segmentation). Each breakdown merges HyperLogLog sketches across the appropriate groupings and extracts the final distinct user count.
- **Key fields**: `dimensions` (breakdown type identifier), `chain_id`, `user_type`, `product_id` (dimensional attributes), `user_mau` (final distinct count)

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a comprehensive view of MAU across all analysis perspectives
- **Time aggregation**: Each MAU value represents unique users active within the 30-day window ending on that specific date, calculated using HyperLogLog merge operations for efficiency
- **Final calculation**: The final MAU metric extracts distinct user counts from merged HyperLogLog sketches, applies schema normalization through the finalize macro, and fills missing data points with zeros 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.

AnkrANKR

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown