Introduction

Amid the rapid development of cryptocurrency and blockchain technology, on-chain data analysis tools have become indispensable in the digital asset market. These tools reveal transaction flows, asset allocations, and market sentiment and assist market participants by providing investors, researchers, and regulatory institutions with real-time, transparent, and precise data support, thereby promoting a more efficient and fair market operation.

Source: FinTechFrontiers

According to the 2024 report “Digital Currencies and On-Chain Financial Infrastructure” published by the Bank for International Settlements (BIS), the total global market capitalization of crypto assets surpassed $4.2 trillion in the first quarter of 2024, with on-chain transaction volume accounting for 67%—nearly triple the 23% recorded in 2020. Behind this surge in data flows, an on-chain data analysis market has emerged, growing at a compound annual growth rate (CAGR) of 28.3% (data source: MarketResearchFuture, MRFR, 2024).

Source: Official

Based on the above, the commercialization process of on-chain data analysis tools is also gradually entering a new stage. In particular, with Arkham launching its spot trading platform, the business model of on-chain data tools has undergone a significant transformation, shifting towards data-driven financial platforms. This article will analyze the current development of on-chain data tools and explore the evolution of future business models.

The Market Landscape of On-Chain Data Tools

Definition and Role of On-Chain Data Analysis

On-chain data analysis involves leveraging cross-platform, automated, and visualized methods to conduct in-depth analysis of blockchain transactions, smart contract executions, and user behavior. It serves as a “digital x-ray” of the crypto market, enabling real-time tracking of fund flows, trading activities, and market trends on the blockchain, while revealing the behavioral patterns of traders and institutional investors.

The value of on-chain data tools lies in their ability to provide decentralized and transparent market data, which starkly contrasts traditional financial markets’ opacity. As a result, on-chain data helps retail investors obtain accurate market signals and assists institutions and regulatory bodies in market supervision and risk control.

Overview of Mainstream On-Chain Data Tools

Currently, multiple well-known on-chain data platforms exist, offering various services tailored to different user needs. While these platforms differ in functionality and business models, they collectively drive the adoption and application of on-chain data.

Evolution of On-Chain Data Analysis Technology

First Generation (2016-2019): Represented by Etherscan, these early blockchain explorers provided basic transaction query functionalities.

Second Generation (2020-2022): Led by Dune Analytics, this era introduced SQL-based custom queries, allowing users to create personalized dashboards.

Third Generation (2023–Present): Characterized by AI-driven intelligent analysis platforms, exemplified by the Arkham Ultra engine and Nansen 2.0’s oracle network.

According to the “On-Chain Data Analysis Tools Technology Maturity Curve” report published by the Stanford University Blockchain Research Center in June 2024, the fourth generation marks the industry’s entry into a phase where “intelligent automation” and “trading integration” evolve in parallel. The report surveyed 312 institutional users, with 89% indicating that “real-time trade signal generation” is the primary factor in choosing an analysis tool.

Leading On-Chain Data Tools

Dune Analytics: Known for its powerful query and visualization capabilities, Dune allows users to customize queries freely and present analysis results through intuitive charts. Its openness and flexibility make it the preferred platform for data scientists and developers.

Source: Cryptonary

Footprint Analytics: Footprint focuses on providing users with easy-to-use interactive data dashboards. By streamlining processes and offering a user-friendly interface, it enables users to effortlessly grasp on-chain data, making it particularly suitable for investors without a technical background.

Source: Official

Nansen: Nansen specializes in analyzing on-chain behavior related to smart contracts and “whale” investors. Its “Smart Money” tracking feature provides users with insights into institutional fund flows and trading strategies, helping investors identify potential market trends.

Source: Official

These platforms play a crucial role in on-chain data analysis, but their business models differ. For example, Dune and Footprint operate on a subscription-based model, whereas Nansen relies more on customized data reports, offering tailored solutions for institutional clients.

Arkham’s Market Positioning

Arkham is one of the few innovative platforms in the market that integrates on-chain data analysis with financial trading. Unlike traditional platforms such as Dune, Footprint, and Etherscan, Arkham is positioned not merely as a data provider. Launching its spot trading platform has broken the traditional barriers between data tools and financial trading. Users can utilize the platform’s data to track “Smart Money” addresses and make investment decisions directly based on this information. In the future, data and trading will be seamlessly connected, enabling more efficient market operations.

Exploration of On-Chain Data Tools’ Business Models

Arkham Launches a Spot Trading Platform: A Milestone in Business Model Innovation

The launch of Arkham’s spot trading platform marks a significant innovation in its business model. As one of the leading platforms in on-chain data analysis, Arkham no longer relies solely on data subscriptions or trading market revenue. Instead, it has integrated data tools with a trading platform, forming a closed-loop business model. By offering “Smart Money” tracking features, the platform enables users to access real-time fund flow data of high-net-worth investors and execute trades directly on the platform. This “data + trading” integrated model brings unprecedented convenience to the market.

Leveraging a “zero-fee limited-time promotion” strategy, Arkham experienced a surge in user growth in the second half of 2024. However, its ARPU (Average Revenue Per User) remains only one-third of Nansen’s, reflecting the relatively weak willingness of retail investors to pay for such services (data source: ARK Invest 2025 Annual Crypto Industry Report).

The Person In the Figure-Raoul Pal (Source: RealVision)

However, renowned cryptocurrency investment expert Raoul Pal remains optimistic, stating on his YouTube channel: “In the future, data will not just be an analytical tool—it will directly shape investment decisions. Arkham’s initiative is undoubtedly at the forefront of the industry.” This comment reinforces the trend of integration between on-chain data platforms and financial markets. Moving forward, more platforms may follow Arkham’s path, gradually merging data analysis with financial trading.

The Impact of the Subscription Model on Revenue

One of the most common business models for on-chain data tools is the subscription model. Many platforms, such as Nansen and Glassnode, rely on subscription-based revenue, where users pay a monthly or annual fee to access premium data and analysis reports.

This model has the advantage of ensuring stable platform revenue but also faces challenges from increasing market competition and evolving user demands. For institutional users, subscription-based services often require further customization, forcing platforms to provide additional services and support, which increases development costs.

Although the subscription model has become dominant in the market, Arkham’s diversified revenue structure significantly enhances its risk resilience compared to traditional platforms like Nansen, where subscriptions account for 92% of total revenue. However, a key concern is that Arkham’s trading business depends on market maker subsidies.

According to Kaiko’s liquidity monitoring report, the BTC/USDT trading pair on Arkham experiences a bid-ask spread of up to 0.8% during non-promotional periods, significantly higher than Coinbase’s 0.1%.

The Integration of Data Platforms with Trading Platforms

As demand for data analysis tools continues to grow, an increasing number of on-chain data platforms are exploring integrating data analysis with financial trading platforms, particularly in the crypto sector. Arkham’s spot trading functionality is a clear reflection of this trend. Through this model, Arkham deeply integrates user trading needs with on-chain data analysis, fulfilling investors’ trading demands while simultaneously providing data-driven insights. This approach enhances user engagement and retention, strengthening the platform’s overall competitiveness.

The person in the figure is Andreas Antonopoulosl (Source: Decrypt)

This business model can help platforms expand their revenue streams and increase market share. Bitcoin expert Andreas Antonopoulos has also publicly stated, “The integration of data and trading will be an inevitable trend in the development of the crypto industry, and platforms like Arkham are paving the way for this trend.”

Customized Data Services

On-chain data platforms often need to provide customized data analysis services for large institutional clients. These services typically involve real-time monitoring of high-frequency trading, large transactions, and asset allocation optimization. Nansen is a prime example, catering to institutional investors by offering tailored data products.

According to Cointelegraph, institutional spending on on-chain data tools has been increasing annually, with particularly strong demand for real-time data analysis, risk management, and liquidity monitoring. Unlike retail investors, institutional clients require more granular and sophisticated data services to gain a competitive edge in the market.

For instance, in November 2024, it was revealed that the world’s largest hedge fund, Bridgewater, had signed a customized service contract with Arkham, which included:

• Annual Fee: $2.4 million
• Services Provided: Proprietary on-chain risk indicators, including TVL anomaly alerts and stablecoin flow velocity tracking
• Additional Terms: Data latency must not exceed 50ms

Source: Official

This competition for high-end clients is driving on-chain data tools toward a “low-latency infrastructure” model. In response to this shift, Bloomberg Terminal acquired real-time data provider Covalent in January 2025 to strengthen its position in the market.

Risks and Challenges

Although customized data services bring high-value clients and long-term revenue to on-chain data platforms, this sector still faces numerous challenges:

3.5.1 Intensifying Homogenized Competition Among Platforms

As more data analytics platforms enter the market, the differentiation among providers is gradually diminishing. Leading platforms such as Nansen, Arkham, and Glassnode offer customized data analytics, leading to increasing market competition.

Additionally, traditional fintech giants (e.g., Bloomberg Terminal) are rapidly expanding into the on-chain data sector, leveraging acquisitions and integrations to strengthen their competitive edge. The 2025 Bloomberg-Covalent acquisition mentioned earlier is a clear example of this trend, aimed at enhancing low-latency data capabilities in the crypto market. For smaller data service providers, the key challenge is how to maintain uniqueness and profitability amid this highly competitive landscape.

Privacy Breaches and the Risk of Losing User Trust

The core of on-chain data services lies in analyzing transactional information, but privacy concerns remain a top priority for both institutional and retail users. Some data platforms have faced controversy over improper handling of user data, leading to privacy-related risks.

For example, in mid-2024, a well-known on-chain data platform was exposed for leaking institutional trading addresses and portfolio holdings. This breach resulted in malicious arbitrage attacks against affected hedge funds, leading to significant financial losses within a short period.

Such incidents harm the impacted institutions and undermine trust in the entire industry. Moving forward, data platforms must ensure they deliver high-precision on-chain analytics while strictly adhering to data privacy compliance standards to prevent user attrition due to security concerns.

Evolution of On-Chain Data Tools

Integration of AI and Big Data

With advancements in artificial intelligence (AI) and big data, AI can process and analyze massive amounts of on-chain data, helping investors uncover potential market opportunities.

For example, Arkham employs machine learning algorithms to identify key data patterns within blockchain networks, allowing users to anticipate potential market trends. This technological integration moves beyond traditional chart analysis, enabling intelligent and automated investment recommendations based on deep learning.

However, the effectiveness of such approaches is not always ideal. Arkham’s Ultra Engine has made multiple misjudgments, partly due to a lack of transparency regarding training datasets and algorithm details. A notable case occurred in August 2024 when the system incorrectly flagged Vitalik Buterin’s charitable donation address as a “sell signal,” triggering a flash crash in ETH prices.

These incidents highlight the need for Explainable AI (XAI) in on-chain analytics. Overcoming such challenges is essential for the continued evolution of on-chain data tools.

Privacy-Preserving Computation and Data Security

Data privacy and security are crucial concerns for on-chain data tools. The challenge lies in maintaining data transparency while ensuring user confidentiality. By employing encryption and anonymization techniques, platforms can analyze blockchain data without exposing sensitive user information. This allows them to provide valuable insights while upholding privacy standards.

However, despite Arkham’s claims of using zero-knowledge proof (ZKP) technology to protect user privacy, its March 2025 vulnerability report highlighted significant risks. The report found that 87% of anonymized addresses could be deanonymized through transaction sequence analysis. Additionally, the accuracy of linking Tornado Cash mixing service users to real identities remained as high as 43%. These findings emphasize the ongoing privacy risks in on-chain data analysis and the need for more advanced privacy-preserving measures.

Regulatory Challenges

Regulatory Trends

As the crypto market expands, on-chain data tools face increasing regulatory scrutiny. Regulatory policies for crypto assets and on-chain data vary across jurisdictions, creating significant challenges for global data platforms.

According to the Financial Stability Board (FSB) 2025 Work Plan, future on-chain analytics tools will be required to integrate:

• Real-time Anti-Money Laundering (AML) scanning modules, ensuring compliance with the FATF Travel Rule
• Cross-jurisdiction regulatory reporting generators
• DeFi protocol risk assessment matrices

Source: PYMNTS

Arkham has allocated 23% of its R&D budget for this purpose. However, its CEO has admitted that “compliance costs may erode the dividends of innovation.”

Future Outlook and Challenges

The Data-Driven Financial Future

With technological advancements and evolving market demands, the business model of on-chain data tools will continue to transform. From their initial role as simple data query tools to today’s multifunctional platforms integrating trading and customized analytics, these tools are set to play an increasingly vital role in the future financial markets.

In the future, as regulatory policies become more refined and technological innovations progress, on-chain data tools may emerge as an essential part of the digital asset market’s infrastructure. They will provide investors, institutions, and regulatory bodies with real-time, transparent, and efficient services, reinforcing trust and efficiency in the ecosystem.

Ongoing Innovation and Competition

While Arkham has pioneered the integration of data and trading, other platforms are actively pursuing advancements to remain competitive. Industry players such as Nansen, Glassnode, and Dune Analytics are continuously exploring new commercialization strategies. In the future, they may introduce innovative features and services to challenge Arkham’s market position and expand their user base.

Conclusion

Overall, the business model of on-chain data tools is transforming profoundly. As technological progress accelerates, market demands shift, and regulatory frameworks solidify, these tools will assume an increasingly significant role in the digital asset market, shaping the next phase of financial innovation.