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 User registration count (referred) Methodology

## 1. Overview

The user registration count referred metric measures the daily number of new user registrations on the OpenChat social platform that were referred by existing users. This metric tracks the effectiveness of referral programs and viral growth mechanisms by counting only those user registrations where the new user was brought to the platform through a referral from an existing user. The calculation aggregates daily counts of referred registrations from OpenChat platform events.

## 2. Data sources

The metric uses the following raw data sources:
- **OpenChat platform events**: Event logs from the OpenChat social platform running on the Internet Computer blockchain, containing user registration activities and referral tracking information
- **User registration events**: Specific `user_registered` events that capture new user onboarding activities with associated metadata
- **Referral tracking data**: JSON payload data within events that indicates whether a user registration was the result of a referral from an existing user

## 3. Step-by-step calculation

### Component A: Event processing and deduplication
- **Purpose**: Processes raw OpenChat events and removes duplicate entries to ensure accurate counting
- **Logic**: Raw OpenChat platform events are deduplicated using a combination of `timestamp`, `user`, event `name`, `source`, and JSON `payload` content. The deduplication process uses row numbering to identify and retain only the first occurrence of identical events, preventing double-counting of user registrations that may appear multiple times in the event stream
- **Key fields**: Event `timestamp`, event `name`, `user` identifier, event `source`, and JSON `payload` containing event metadata

### Component B: User registration event filtering
- **Purpose**: Isolates user registration events and extracts referral status information
- **Logic**: The processed events are filtered to include only those with the event name `user_registered`. For each registration event, the JSON `payload` is parsed to extract the `referred` boolean field, which indicates whether the new user was brought to the platform through a referral. Additional fields like `is_bot` are also extracted for potential future filtering or analysis
- **Key fields**: `referred` boolean flag extracted from the JSON payload, `is_bot` field for bot detection

### Component C: Referred registration filtering and counting
- **Purpose**: Counts only the user registrations that resulted from referrals
- **Logic**: User registration events are filtered to include only those where the `referred` field equals true, indicating that the new user joined through a referral from an existing user. These filtered events are then aggregated by day using timestamp truncation to the daily grain
- **Key fields**: Boolean `referred` field used for filtering, daily truncated `timestamp` for time grouping

### Final aggregation
- **Combining components**: The filtered referred registration events are counted using a simple count aggregation grouped by daily timestamp boundaries
- **Time aggregation**: Events are aggregated to daily boundaries by truncating the event timestamp to the day level, ensuring all registrations within a 24-hour period are grouped together
- **Standardization**: The `finalize_metric_user_registration_count_referred` macro applies schema validation, ensures consistent field naming, fills gaps in the time series with zeros where no referred registrations occurred, and standardizes the output format with metadata fields including `market_sector_id` (social), `business_segment_id` (social), `chain_id` (internetcomputer), and `timestamp_grain` (day)
- **Final calculation**: The final metric value represents the count of unique user registration events per day where the user was referred by an existing platform user, providing insight into the viral growth and referral program effectiveness of the OpenChat social platform

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

Count of new user registrations attributed to referrals.

OpenChatCHAT

User registration count (referred)

User registration count (referred)

Competitive landscape