> For the complete documentation index, see [llms.txt](https://web3-growth-agent-wga.gitbook.io/whitepaper_ver2.0_en/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://web3-growth-agent-wga.gitbook.io/whitepaper_ver2.0_en/.-business-model-and-data-products/5.-data-products.md).

# 5. Data Products

WGA provides three core products.

#### 1) Core Product 1: Behavior Asset Report

**Format: PDF Report + On-chain Proof Link**

**Provided Content:** 

* Analysis of verified behavioral data for specific periods, channels, and campaigns 
* Authentic user counts and distribution following the removal of artificial metrics 
* Engagement levels by behavioral category (Exploration, Response, Participation, Diffusion, Retention) 
* Diagnostics of attrition points and friction zones 
* Chronological trend analysis and benchmarking

**Operational Objectives:**

* Measurement of marketing campaign efficacy
* Product UX optimization 
* Investment decision-making support 
* Competitor benchmarking

**Target Customers: Web2/Web3 service operators, investment institutions, research organizations**

#### 2) Core Product 2: Analytics Dashboard

**Format: Web-based real-time dashboard**

**Provided Content:**

* Real-time verified user behavioral metrics
* Funnel analysis (Step-by-step attrition and conversion tracking) 
* Cohort tracking (Behavioral shifts over designated timeframes) 
* Behavioral asset distribution (Ratios of Exploration, Response, Participation, Diffusion, and Retention) 
* User distribution by confidence intervals 
* Custom filters and segmentation

**Operational Objectives:**

* Daily operational monitoring 
* Real-time campaign performance tracking 
* Proactive anomaly detection 

**Target Customers: Web2/Web3 service operators, community managers**

#### 3) Core Product 3: Behavior Intelligence API

**Format: RESTful / GraphQL API**

**Provided Content:**

* Retrieval of verified behavioral data 
* Retrieval of calculated metric results 
* Filtering, sorting, and aggregation functions 
* Real-time notifications via Webhooks 
* Clean dataset export

**Operational Objectives:** 

* Integrate your own analytics system
* Integrate with BI tools
* Input data for investment models
* Machine learning training data
* Integration with proprietary analytical systems
* BI tool synchronization 
* Input data for investment modeling 
* Machine learning training datasets 
* Target Customers: Enterprise clients, data purchasers, research institutions