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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OpenChat

# OpenChat Active direct chats (monthly) Methodology

## 1. Overview

This metric measures the number of direct chat conversations that had at least one activity within a 30-day rolling window on the OpenChat platform. OpenChat is a decentralized messaging platform built on the Internet Computer blockchain. The metric uses a rolling 30-day window to capture chat activity and applies a division by 2 factor as recommended by the OpenChat team. This metric helps understand user engagement in private messaging functionality on the platform.

## 2. Data sources

- **OpenChat platform events**: Raw event data from the OpenChat application on Internet Computer blockchain, including user interactions and chat activities
- **Chat activity logs**: Platform-generated events capturing messaging activities such as message sending, editing, reactions, and tipping
- **Chat metadata**: Information about chat types, participants, and chat identifiers embedded in event payloads

## 3. Step-by-step calculation

### Component A: Event data preparation
- **Purpose**: Clean and deduplicate raw OpenChat platform events
- **Logic**: Raw events from OpenChat's event stream are processed to remove duplicates based on timestamp, user identifier, event name, source, and payload content. The deduplication uses row numbering to keep only the first occurrence of identical events
- **Key fields**: Event `timestamp`, event `name`, `user` identifier, event `source`, and JSON `payload` containing chat metadata

### Component B: Chat activity filtering
- **Purpose**: Identify relevant chat interaction events
- **Logic**: From the cleaned events, only chat activity events are selected including `message_sent`, `message_edited`, `reaction_added`, and `message_tipped` events. Each event is processed to extract chat identifiers and chat type information from the JSON payload
- **Key fields**: `chat_id` extracted from payload, `chat_type` indicating direct vs group chats, daily `timestamp` truncation

### Component C: Direct chat isolation
- **Purpose**: Filter for direct messaging conversations only
- **Logic**: Chat activities are filtered to include only those where `chat_type` equals 'direct', excluding group chats and other conversation types. This creates the base dataset of direct chat activities
- **Key fields**: `chat_id` for direct conversations, `timestamp` for activity timing, Internet Computer `chain_id`

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each day in the analysis period
- **Logic**: A complete daily time series is generated from the earliest activity date to the current day. For each day, a 30-day window is created by generating timestamps from 29 days prior through the current day. This ensures consistent window sizing across all time periods
- **Key fields**: Window `timestamp` as the end date, `joinday` representing each day within the 30-day window

### Component E: Activity window mapping
- **Purpose**: Map chat activities to their respective rolling windows
- **Logic**: Direct chat activities are joined to the rolling windows based on matching timestamps. Each chat activity can appear in multiple windows (up to 30 different windows). This creates the foundation for counting active chats within each 30-day period
- **Key fields**: Window `timestamp`, activity `timestamp_grain`, `chat_id` for distinct chat identification

### Final aggregation
- **Combining components**: Chat activities within each 30-day window are aggregated by counting distinct `chat_id` values, representing unique direct chats that had activity
- **Team adjustment**: The distinct chat count is divided by 2 using floor division, as recommended by the OpenChat team (noted as a temporary solution)
- **Dimensional breakdowns**: Results are calculated separately for project-level totals, product-level breakdowns, and chain-level aggregation, then combined into a single result set
- **Time series completion**: Missing days are filled with zero values to ensure complete time series coverage
- **Final calculation**: For each day, the metric equals the floor of (count of distinct direct chats with activity in the 30-day window ending on that day) divided by 2

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

Unique active direct chats over a rolling 30-day window.

OpenChatCHAT

Active direct chats (monthly)

Active direct chats (monthly)

Competitive landscape