> 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/2.-market-structure.md).

# 2. Market Structure

#### 1) Already Existing Markets

The sale of user behavioral data is not an experimental concept; it is a long-established and active market.

**Web2 Analytics Market**

The Web Analytics and Product Analytics markets are already mature, multi-billion dollar industries. Numerous commercial SaaS providers, such as Google Analytics, Adobe Analytics, Amplitude, and Mixpanel, already exist; they sell behavioral reports based on page views, sessions, conversions, funnels, and events.\
What they are selling is not just raw logs. It is the interpreted results, such as: "This user entered through this specific path, bounced here, and has a high probability of converting at this point."

\
**Web3 Analytics Market**

The on-chain behavioral analysis market is also growing rapidly. Platforms like Dune, Nansen, Flipside, and Arkham aggregate wallets, protocols, and transactions to provide behavioral reports and dashboards. While on-chain data is transparent, it remains difficult to interpret. Therefore, the interpretation itself becomes the product.

**Investment Markets and Alternative Data**

Hedge funds, Private Equity (PE) firms, and Venture Capitalists (VC) purchase Alternative Data. They use satellite imagery of parking lots to predict retail sales, aggregate credit card transactions to read consumption trends, and track airline ticket prices and booking trends via web scraping to adjust corporate valuations.

This data is not just for reference; it is directly integrated into valuation models.<br>

#### 2) Common Market Limitations

As this market grows, it repeatedly faces the same issues.<br>

**First, Difficulty in completely eliminating noise and abuse.**&#x20;

Bots perform clicks, automation generates traffic, and the inclusion of rewards leads to increased duplicate participation. While "filtering" is being done, it is difficult for outsiders to clearly know to what extent it has been removed.

Second, Difficulty in explaining what the behavior signifies.&#x20;

Numbers like CTR, CVR, and Retention are provided, but the reason "why" those numbers appeared and which behaviors are actually valuable changes depending on the platform and the analyst.

**Third, Different platforms use the same terms differently.**&#x20;

Even for the same inflow, attribution differs by tool; for the same "active" status, the criteria differ; and for the same "engagement," the definition differs. Ultimately, reports cannot be compared against each other.<br>

**Fourth, Difficulty in trusting and utilizing analysis results.**&#x20;

Particularly in external decision-making, "how explainable this result is" is key. Just showing numbers is insufficient. An explanation of what process created those numbers, where they might be distorted, and what was removed is required.

There is an overflow of data, but only unstable outputs—unfit for immediate decision-making—are accumulating.<br>