> 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/.-platform-architecture-and-technical-framework/2.-architecture-overview/4-layer-4-metric-structuring-layer.md).

# (4) Layer 4: Metric Structuring Layer

**Purpose of Metrics**

WGA is not an organization focused on popularizing new metrics. The primary issue with existing analytical metrics lies not in their mathematical formulas, but in the inability to explain the reliability of the input data. Rather than the "metrics themselves," WGA prioritizes the standardization of input conditions necessary for metrics to be valid.

**Metric Design Principles**

All WGA metrics are governed by the following principles:

* Proof-Only Principle

Unverified events are excluded from metric calculations.

* Action-Based Measurement

Evaluation is centered on substantive user actions rather than passive impressions or simple clicks

* Cross-Channel Verifiability

Metrics must allow for comparison between Web2 and Web3 environments using identical standards.

* Time-Invariant

Results must be reproducible over time as long as the same criteria are applied.

* Composable

The architecture must allow metrics to be combined with external data for re-interpretation.

**Metric Classification Framework**

WGA provides six categories of metrics based on verified behavioral data.

| Category                          | Description                                                                   | Primary Objective                                                |
| --------------------------------- | ----------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| User Validity Metrics             | Verification of whether documented user actions actually occurred.            | Mitigation of distorted data; identification of authentic users. |
| Participation Depth Metrics       | Measurement of the frequency and recurrence of user behaviors.                | Assessment of engagement depth and immersion levels.             |
| Action Integrity Metrics          | The ratio of valid to invalid events within the total dataset.                | Evaluation of overall data quality.                              |
| Engagement Quality Metrics        | Distribution of behaviors that meet specific trust and reliability standards. | Qualitative assessment of engagement levels.                     |
| Retention & Consistency Metrics   | Recurrence of verified behaviors over specific time intervals.                | Evaluation of behavioral persistence and loyalty.                |
| Cross-Channel Reliability Metrics | onsistency of data across disparate environments (Web2, Web3, etc.).          | Verification of cross-channel data integrity.                    |

Each category is comprised of multiple detailed metrics, which are combined differently depending on the specific industry segment and analytical objective.<br>

**Metric Output Format**

WGA metrics are not provided as isolated numerical values.&#x20;

They are designed to be interpreted through distributions, ratios, and trends. For instance, "Valid User Count" is not presented as a single integer, but rather as:

* Distribution by Trust Interval: (e.g., Score 90–100: X users, Score 70–90: Y users...)
* emporal Trends: (Daily, weekly, or monthly changes)
* Contribution by Behavioral Category: (Exploration-centric, Participation-centric, Consistency-centric, etc.)

Unlike traditional marketing metrics that are easily distorted by short-term events or incentive structures, this framework provides a longitudinal lens for interpreting the underlying structure of verified user behavior.<br>

Furthermore, all metrics are predicated on explainability:

* Mitigation Rate(The ratio of events that passed verification versus the total event volume.)
* Confidence Intervals(User distribution across High, Medium, and Low reliability segments.)
* Behavioral Attribution(The proportional weight of categories such as Exploration, Reaction, Participation, and Amplification.)

**Public Disclosure and IP Protection**

WGA publicly discloses its metric classification systems and interpretive frameworks. However, the specific thresholds, weights, and detailed formulas used in actual operations are managed as internal logic. Access to these specifics is controlled within the scope of services provided to B2B clients. This is because the metrics are not merely "marketing copy," but the core engineering that dictates the quality of the analytical outcomes.

**Extensible Metric System**

The metric system is not static. As novel behavioral patterns are identified, new metric categories can be integrated. Similarly, when the network expands into new industry segments, specialized metrics tailored to those specific sectors can be developed.