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.

github

twitter

website

Social

OpenChat

# OpenChat Active groups (monthly) Methodology

## 1. Overview

Active groups monthly measures the number of unique group chats on OpenChat that had at least one user interaction within a 30-day rolling window. This metric tracks engagement and activity levels across OpenChat's group communication features on the Internet Computer blockchain. The calculation uses a rolling 30-day window approach to provide smooth trending and avoid month-over-month volatility, counting distinct groups that received messages, edits, reactions, or tips during each window period.

## 2. Data sources

- **OpenChat platform event logs**: Structured event data from OpenChat's native event tracking system on the Internet Computer blockchain
- **User interaction events**: Records of message sending, editing, reaction adding, and tipping activities within OpenChat
- **Chat metadata**: JSON payload data containing chat identifiers, chat types, and interaction context
- **Platform activity logs**: Complete activity feed from OpenChat's social communication platform

## 3. Step-by-step calculation

### Component A: Event deduplication and filtering
- **Purpose**: Clean and filter raw OpenChat events to focus on group engagement activities
- **Logic**: Start with raw OpenChat events and remove duplicates using row numbering partitioned by timestamp, user, event name, source, and JSON payload content. Filter events to include only engagement activities: `message_sent`, `message_edited`, `reaction_added`, and `message_tipped`. Parse the JSON `payload` field to extract `chat_id` and `chat_type` information for each event.
- **Key fields**: `timestamp`, `name` (event type), `user`, `payload` (JSON containing chat metadata)

### Component B: Group chat identification
- **Purpose**: Isolate events that occurred within group chats specifically
- **Logic**: Filter the engagement events to include only those where `chat_type` equals 'group', excluding direct messages and other chat types. Truncate event timestamps to daily granularity for consistent aggregation. Assign fixed metadata including `chain_id` as 'internetcomputer', `market_sector_id` and `business_segment_id` as 'social'.
- **Key fields**: `chat_id` (unique group identifier), `chat_type`, `timestamp` (daily truncated)

### Component C: Rolling window generation
- **Purpose**: Create 30-day rolling windows for each date to ensure smooth trending
- **Logic**: Generate a complete datespine from the earliest event timestamp to the current day. For each date in the datespine, create a 30-day window spanning from 29 days prior through the current date. This approach ensures no missing dates and provides consistent 30-day lookback periods for activity measurement.
- **Key fields**: `timestamp` (window end date), `joinday` (dates within each 30-day window)

### Component D: Activity window matching
- **Purpose**: Match group activities to their corresponding 30-day windows
- **Logic**: Join the group chat activities to the rolling windows by matching activity dates to the window date ranges. This creates a mapping where each group activity appears in multiple windows - specifically in all windows where the activity date falls within the 30-day lookback period.
- **Key fields**: `chat_id`, `timestamp` (window date), activity occurrence dates

### Final aggregation
- **Combining components**: Count distinct `chat_id` values within each 30-day window to determine how many unique groups had activity during that period
- **Dimensional breakdowns**: Create three aggregation levels - overall project level (project_dbt_tag), by product identifier, and by blockchain chain
- **Time aggregation**: Daily window boundaries with 30-day rolling lookback periods
- **Final calculation**: For each date, the active groups monthly metric equals the count of distinct group chat IDs that had at least one engagement event (message, edit, reaction, or tip) within the 30-day window ending on that date

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

Unique active groups over a rolling 30-day window.

OpenChatCHAT

Active groups (monthly)

Active groups (monthly)

Competitive landscape