BOB batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

BOB is an L2 smart contract platform for decentralized applications on Ethereum. BOB was founded by Alexei Zamyatin and Dominik Harz and launched in 2024.

BOB team & investors own and govern the protocol. BOB's investors include Castle Island Ventures, IOSG Ventures, Coinbase Ventures, etc.

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BOB (Build on Bitcoin)

# BOB (Build on Bitcoin) Active users (weekly) Methodology

## 1. Overview

The BOB Weekly Active Users (WAU) metric measures the number of unique addresses that initiated at least one transaction on the BOB Layer 2 blockchain within any 7-day rolling period. This metric answers the business question: "How many distinct users are actively using the BOB network each week?" The calculation uses HyperLogLog (HLL) probabilistic counting to efficiently aggregate unique users across rolling 7-day windows while providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

- **BOB blockchain transactions**: Complete transaction records from the BOB Layer 2 network including transaction hashes, block timestamps, and sender addresses
- **Block timestamp data**: Precise timestamp information for when each transaction was included in a block
- **Transaction metadata**: Chain identifiers, market sector classifications, and business segment categorizations
- **User classification data**: User type categorizations (specifically "Transaction sender" for all addresses initiating transactions)

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique active users for each day based on transaction activity
- **Logic**: Processes BOB blockchain transaction records by extracting the sender address from each transaction and truncating block timestamps to day-level granularity. Groups transactions by day and identifies distinct sender addresses as active users. Each unique address that initiated at least one transaction on a given day is counted as a daily active user. Includes dimensional attributes like chain identifier (set to 'bob'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender')
- **Key fields**: `user_address` (transaction sender), `block_timestamp` (truncated to day), `chain`, `user_type`, `market_sector_id`, `business_segment_id`, `product_id`

### Component B: HyperLogLog sketch creation
- **Purpose**: Converts daily user sets into HyperLogLog sketches for efficient approximate counting
- **Logic**: Takes the distinct daily user addresses and initializes HyperLogLog sketches with precision parameter 15. This probabilistic data structure allows efficient merging of user sets across time periods without storing individual addresses. Each day's unique user set is represented as an HLL sketch that can be combined with other sketches to calculate approximate unique counts across longer periods
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`

### Component C: Datespine generation
- **Purpose**: Creates a complete timeline to ensure no days are skipped in the rolling window calculation
- **Logic**: Generates a continuous array of timestamps from the earliest transaction date in the dataset to the day before the current date, with daily intervals. This datespine ensures that the rolling 7-day window calculation includes all calendar days, even those with no transaction activity. For incremental updates, the datespine starts from a lookback period to capture any late-arriving data
- **Key fields**: `timestamp` (daily granularity covering complete time range)

### Component D: Rolling window creation  
- **Purpose**: Establishes 7-day rolling windows for each day in the analysis period
- **Logic**: For each day in the datespine, creates a 7-day window by generating timestamps for the current day plus the previous 6 days. Uses array generation to create these date ranges and unnests them to create join relationships. This produces a mapping where each target day is associated with all days in its 7-day lookback period, enabling the calculation of users active within any rolling weekly period
- **Key fields**: `timestamp` (target day), `joinday` (each day within the 7-day window)

### Component E: Window aggregation
- **Purpose**: Combines daily HLL sketches within each 7-day rolling window
- **Logic**: Joins the rolling window mapping with daily HLL sketches to collect all user sketches within each 7-day period. For each target day, gathers the HLL sketches from all days in its 7-day window. This step prepares the data for merging sketches to calculate unique users across the weekly period
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` (from matching window days)

### Component F: Dimensional breakdowns
- **Purpose**: Calculates WAU across different dimensional cuts for analytical flexibility
- **Logic**: Creates four separate breakdowns of weekly active users by merging HLL sketches within each dimension and extracting final counts. The breakdowns include: project-level aggregation (all dimensions combined), chain-level (maintaining chain_id), user type level (maintaining user_type), and product level (maintaining product_id). Each breakdown merges the relevant HLL sketches using HyperLogLog merge operations and extracts the approximate unique count
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau` (final count)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, with null values for non-relevant dimensional fields in each breakdown type
- **Currency conversion**: Not applicable - this is a user count metric, not a financial metric
- **Time aggregation**: Uses daily granularity with 7-day rolling windows, where each day represents the count of unique users active in the 7-day period ending on that day
- **Final calculation**: Applies the finalization macro to standardize the schema, add metadata fields, and fill missing periods with zero values to ensure complete time series coverage

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

Unique revenue-generating users over a rolling 7-day window.

BOB (Build on Bitcoin)BOB

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown