OpenChat users can send messages to each other containing tokens such as ICP. Users can upgrade to Diamond membership to have access to extra or enhanced features.

OpenChat is a chat application running end-to-end on the Internet Computer blockchain.

CHAT tokenholders own and govern the protocol.

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Social

OpenChat

# OpenChat Active users (monthly) Methodology

## 1. Overview

OpenChat Monthly Active Users (MAU) measures the number of unique users who have been active on the OpenChat social platform within any 30-day rolling window. This metric answers the business question "How many distinct users engaged with OpenChat in the past month?" and uses a sophisticated rolling window approach with HyperLogLog probabilistic data structures to efficiently calculate unique user counts across overlapping time periods.

## 2. Data sources

The calculation relies on the following raw data sources:
- **OpenChat platform events**: Native event logs from the OpenChat social platform running on Internet Computer Protocol, capturing all user interactions and system activities
- **User activity events**: Specific `user_online` events that indicate when users are actively engaged with the platform
- **Event metadata**: Timestamps, user identifiers, event names, source information, and payload data associated with each platform interaction
- **Internet Computer Protocol data**: Blockchain-level information for the Internet Computer network where OpenChat operates

## 3. Step-by-step calculation

### Component A: Event deduplication and cleaning
- **Purpose**: Remove duplicate events and ensure data quality from raw OpenChat event streams
- **Logic**: Raw OpenChat events are deduplicated using a combination of `timestamp`, `user`, event `name`, `source`, and serialized `payload` data. A row number is assigned to each event within these partition groups, and only the first occurrence of each unique event combination is retained. This handles cases where the same user interaction might be logged multiple times due to system retries or processing errors.
- **Key fields**: Event `timestamp`, `user` identifier, event `name`, `source` system, and `payload` content

### Component B: Active user identification
- **Purpose**: Filter events to identify genuine user activity sessions on the OpenChat platform
- **Logic**: From the cleaned event data, only `user_online` events are selected, which specifically indicate when users are actively engaged with the platform. Events are filtered to include only those occurring on or after February 14, 2024, establishing a consistent measurement baseline. Each qualifying event represents a user activity session with the associated `timestamp` and `user_address`.
- **Key fields**: Event `timestamp` truncated to day level, `user_address` as the unique user identifier

### Component C: Daily active user aggregation
- **Purpose**: Calculate daily unique active users as the foundation for rolling MAU calculations
- **Logic**: User activity events are grouped by calendar day using timestamp truncation. For each day, distinct user addresses are identified and classified with metadata including 'internetcomputer' as the `chain`, 'social' as both `market_sector_id` and `business_segment_id`, and 'day' as the `timestamp_grain`. HyperLogLog sketches are initialized for each daily user set to enable efficient cardinality estimation in subsequent rolling window calculations.
- **Key fields**: Daily `timestamp`, `chain` identifier, `user_dau` containing the daily active user set

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each calendar day to calculate monthly active users
- **Logic**: A datespine is generated covering all calendar days from the earliest available data through the current date. For each day in the datespine, a 30-day lookback window is constructed by generating timestamps for the current day plus the previous 29 days. This creates overlapping 30-day periods that slide forward one day at a time, ensuring continuous MAU calculation without gaps in the time series.
- **Key fields**: Target `timestamp` for MAU calculation, `joinday` timestamps within each 30-day window

### Component E: HyperLogLog aggregation across time windows
- **Purpose**: Efficiently calculate unique user counts across each 30-day rolling window
- **Logic**: For each 30-day window, daily HyperLogLog sketches are retrieved and merged using `hll_count.merge_partial()` to combine the probabilistic user sets. The merged HLL sketch is then extracted using `hll_count.extract()` to produce the estimated count of unique users who were active at any point during the 30-day period. This approach efficiently handles the complex set union operations required for rolling MAU calculations.
- **Key fields**: Merged `hll` sketches representing unique users across 30-day windows

### Component F: Dimensional breakdowns
- **Purpose**: Provide MAU metrics across multiple analytical dimensions for flexible reporting
- **Logic**: The rolling MAU data is pivoted into four distinct dimensional views: project-level aggregation (combining all users regardless of other attributes), chain-level breakdown (though OpenChat operates only on Internet Computer), user-type segmentation (currently null but structured for future classification), and product-level analysis (currently null but prepared for multi-product scenarios). Each breakdown maintains the same temporal structure while grouping users according to the specific dimension.
- **Key fields**: `dimensions` label indicating the breakdown type, dimensional identifiers (`chain_id`, `product_id`, `user_type`), and calculated `user_mau` counts

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a unified dataset containing MAU metrics across all analytical perspectives
- **Time aggregation**: Each MAU value represents unique users active within the trailing 30-day period ending on the specified date, with daily granularity maintained throughout the time series
- **Final calculation**: The metric represents the estimated count of unique users who had at least one `user_online` event within any 30-day rolling window, calculated using HyperLogLog probabilistic counting for computational efficiency across large user sets and long time series

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

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

OpenChatCHAT

Active users (monthly)

Active users (monthly)

Competitive landscape