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 Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the daily revenue generated per active user on the OpenChat social platform. This metric is calculated by dividing total daily revenue from diamond membership payments by the number of daily active users. ARPU helps evaluate user monetization effectiveness and revenue generation efficiency on the platform.

## 2. Data sources

The calculation uses the following raw data sources:
- **OpenChat event logs**: Timestamped activity records from the OpenChat platform containing user interactions and payment events
- **Diamond membership payments**: Payment transaction records with amounts, token types, and membership duration details stored in JSON format
- **User activity events**: Online user presence indicators tracking when users are active on the platform
- **End-of-day price feeds**: CoinGecko token price data for CHAT and ICP tokens used for USD conversion
- **Token metadata**: Mapping information connecting CHAT and ICP token symbols to CoinGecko identifiers

## 3. Step-by-step calculation

### Component A: Event data preparation
- **Purpose**: Establish clean, deduplicated foundation of all OpenChat platform events
- **Logic**: Source timestamped events from OpenChat's event logging system, including event names, user identifiers, data sources, and JSON payloads. Remove duplicate events by partitioning on timestamp, user, event name, source, and JSON payload content, keeping only the first occurrence of each unique event combination
- **Key fields**: `timestamp` for temporal analysis, `name` for event classification, `user` for user identification, `payload` containing structured event data

### Component B: Daily active user calculation  
- **Purpose**: Count distinct users active each day using efficient probabilistic methods
- **Logic**: Filter events to identify `user_online` activities starting from February 14, 2024. Extract user addresses from these activity events. Group events by day and apply HyperLogLog sketching with 15-bit precision to count distinct users efficiently, handling large user populations. Create dimensional breakdowns across project level, chain level, user type, and product categories using HLL merge operations for flexible aggregation
- **Key fields**: `user_address` from activity events, `timestamp` truncated to daily boundaries, dimensional attributes for categorization

### Component C: Revenue from diamond membership payments
- **Purpose**: Calculate daily revenue from premium membership purchases  
- **Logic**: Filter events to identify `diamond_membership_payment` transactions. Extract payment details from JSON payloads including payment amounts, membership duration, token types, and payment metadata. Scale payment amounts by dividing by 100,000,000 to correct for integer representation. Map token symbols to CoinGecko identifiers where CHAT tokens map to 'openchat' and ICP tokens map to 'internet-computer'. Join payment data with end-of-day token prices from CoinGecko and convert all payments to USD equivalents. Aggregate converted amounts by day to calculate total daily revenue, treating membership payments as platform fees and subsequently as revenue
- **Key fields**: Payment `amount` scaled and converted to USD, `token` type for price mapping, `duration` for membership classification, daily aggregated totals

### Final aggregation
- **Combining components**: Join daily revenue totals with daily active user counts on date boundaries, ensuring both metrics are available for the same time periods
- **Currency conversion**: All revenue converted to USD using end-of-day exchange rates from CoinGecko price feeds, applied to both CHAT and ICP token payments
- **Time aggregation**: Data aggregated to daily boundaries using timestamp truncation, with consistent daily boundary definitions across revenue and user calculations  
- **Final calculation**: Divide total daily revenue by daily active user count to produce daily ARPU values. Apply zero-filling strategy to maintain complete time series for dates with no revenue or no active users, ensuring continuous metric availability for analysis and reporting

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

OpenChatCHAT

Average revenue per user (ARPU)

Average revenue per user (ARPU)

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