> 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/.-market-analysis-and-core-problems/established-analytical-methodologies.md).

# Established Analytical Methodologies

Analytical techniques in this market are highly standardized.

In Web2 marketing and product analysis, funnel analysis tracks attrition and conversion from initial visit to final transaction. Cohort analysis tracks retention, LTV, and revisit patterns based on acquisition timing or specific behaviors. 

Attribution modeling has evolved from single-touch methods to multi-touch models (Linear, Time-decay, U-shape, W-shape) and machine-learning-driven attribution, which estimates incremental lift by including non-conversion paths.

In Web3 on-chain analysis, wallet clustering is used to group multiple addresses into single entities for pattern recognition. On-chain funnel analysis tracks the progression from wallet creation to long-term balance retention. Combined on/off-chain attribution verifies if campaigns result in specific actions such as minting, swapping, or staking.

Investment and valuation analysis integrates alternative data indicators into traditional DCF and multiple models. Non-financial features like foot traffic, transaction trends, price sensitivity, and social sentiment are used to enhance predictive power. NLP-driven analysis of earning calls and news for ESG risks or demand signals has also reached a stage of general adoption.