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 chats (daily) Methodology

## 1. Overview

The daily active chats metric measures the number of unique chat conversations that had activity on OpenChat platform each day. This metric tracks engagement across the OpenChat social platform by counting distinct chats where users performed activities like sending messages, editing messages, adding reactions, or tipping other users. The calculation provides dimensional breakdowns by project, product, and blockchain to enable multi-faceted analysis of chat engagement patterns.

## 2. Data sources

- **OpenChat platform events**: Raw event logs from the Internet Computer blockchain capturing all user interactions on the OpenChat social platform
- **Event metadata**: Timestamps, user identifiers, event types, and source information for each platform interaction  
- **Chat interaction data**: JSON payloads containing chat-specific details like `chat_id`, `chat_type`, and other contextual information
- **Platform activity logs**: Complete record of user actions including message sending, editing, reactions, and tipping activities

## 3. Step-by-step calculation

### Component A: Event deduplication and processing
- **Purpose**: Clean and deduplicate raw OpenChat events to ensure accurate counting
- **Logic**: Raw OpenChat events from Internet Computer are processed to remove duplicate entries. Events are deduplicated using a combination of `timestamp`, `user`, event `name`, `source`, and the JSON-stringified `payload` content. A row number is assigned to each event within duplicate groups, and only the first occurrence is retained to prevent double-counting of the same user action
- **Key fields**: Event `timestamp`, `name` (event type), `user` identifier, `source`, and `payload` (JSON data containing chat details)

### Component B: Chat activity filtering  
- **Purpose**: Identify events that represent actual chat engagement activities
- **Logic**: From all deduplicated OpenChat events, filter to include only activities that indicate active chat engagement. The qualifying event types are `message_sent` (new messages), `message_edited` (message modifications), `reaction_added` (emoji reactions), and `message_tipped` (cryptocurrency tips). These event types represent meaningful user interactions within chat conversations
- **Key fields**: Event `name` field filtered to specific activity types, `payload` containing chat context

### Component C: Chat identification and daily aggregation
- **Purpose**: Extract chat identifiers and aggregate to daily active chat counts
- **Logic**: From filtered chat activity events, extract the `chat_id` from the JSON `payload` to identify which specific chat conversation had activity. Parse additional metadata like `chat_type` from the payload. Truncate event timestamps to daily boundaries to group activities by date. Set fixed dimensional attributes including `market_sector_id` and `business_segment_id` to 'social', `chain_id` to 'internetcomputer', and `product_id` to null
- **Key fields**: `chat_id` extracted from payload, truncated `timestamp` at daily grain, dimensional categorization fields

### Component D: Dimensional breakdown creation
- **Purpose**: Create multiple analytical perspectives of the active chats data  
- **Logic**: Generate three separate dimensional views of the daily active chat counts. First breakdown is by project dbt tag (with null `chain_id` and `product_id`). Second breakdown is by `product_id` (with null `chain_id`). Third breakdown is by `chain_id` (with null `product_id`). Each breakdown counts distinct `chat_id` values grouped by `timestamp`, `timestamp_grain`, and the respective dimensional field. All three views use identical aggregation logic but different dimensional focus
- **Key fields**: `timestamp`, `timestamp_grain`, `dimensions` (breakdown type), dimensional identifier fields, `active_chats_daily` count

### Final aggregation
- **Combining components**: The three dimensional breakdowns are combined using union all operations to create a comprehensive dataset with multiple analytical perspectives in a single table
- **Currency conversion**: Not applicable for this count-based metric
- **Time aggregation**: Events are aggregated at daily boundaries using timestamp truncation to day level, with `timestamp_grain` set to 'day'  
- **Final calculation**: The final metric value is the count of distinct `chat_id` values per day within each dimensional breakdown, representing the number of unique chat conversations that had user activity on that specific date. Missing days are filled with zero values using the finalization macro, and chain availability filtering is applied based on the `chain_id` field

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

Unique active chats over a fixed 24-hour window.

OpenChatCHAT

Active chats (daily)

Active chats (daily)

Competitive landscape