Web2 analytics refers to collecting, analyzing, and creating business intelligence from data generated by websites and web-based applications. This data is typically collected using cookies and device IDs to personalize product experiences, target promotions, and gain insights into customer behavior. Web3 analytics, on the other hand, refers to collecting, analyzing, and creating business intelligence from data generated by decentralized applications (dApps) on blockchain networks. The decentralized nature of Web3 data creates new challenges for data collection and tracking. Customer identities are fragmented across multiple wallets and chains, making it challenging to collect accurate data and understand customer behavior. Additionally, Web3 applications often run on Web2 interfaces, which adds to the fragmentation and hinders accuracy.
Despite these differences, there are also similarities between web2 and web3 analytics. Both types of analytics involve collecting, analyzing, and creating business intelligence from data. Additionally, web2 and web3 analytics face similar challenges regarding data fragmentation and privacy concerns. The hybrid data stacks are being developed to unify data fragmented across web3 and trace it back to web2 to create an accurate picture of customer profiles while preserving their privacy.
Web2 & Web3 Data Collection Steps
A consolidated list of steps and actions businesses need to take to collect, analyze, and create business intelligence in both Web 2.0 and Web3 environments is as follows:
- Set up tracking: For Web 2.0, this typically involves installing a tracking code on the website, such as Google Analytics. Setting up tracking typically involves installing a tracking code on the website, such as Google Analytics. This code is a small snippet of JavaScript that is placed on every page of the website. Once the code is installed, it begins tracking user behavior, such as page views, bounce rates, and user demographics. Some web analytics tools also provide tracking codes in languages like PHP, python, etc. In addition, businesses may also use other tracking tools such as heat maps, session replay, and A/B testing.
For Web3, this typically involves using a blockchain explorer, such as Etherscan for Ethereum, to track on-chain transactions, smart contract data, and token usage. Setting up tracking typically involves using a blockchain explorer, such as Etherscan for Ethereum, to track on-chain transactions, smart contract data, and token usage. A blockchain explorer is a tool that allows users to view and search the blockchain for specific transactions, addresses, and other data. Businesses can also use other blockchain-based tools, such as smart contract analyzers, that help track their smart contracts’ activity.
- Collect data: For Web 2.0, this can include website traffic, page views, bounce rates, user demographics, and more. For Web3, this can consist of on-chain transaction data, smart contract data, token usage data, and other relevant blockchain-based metrics. Collecting data typically involves using web analytics tools such as Google Analytics to track user behavior on the website. This can include website traffic, page views, bounce rates, user demographics, and more. Businesses can also use tools such as heat maps, session replay, and A/B testing to collect more granular data on user behavior. Some websites also use server logs to collect data which can be used to understand the user behavior, device, browser type, location, and so on.
- Analyze data: For Web 2.0; this can include using tools such as Google Analytics to analyze data, key metrics such as website traffic and conversion rates, and more detailed analyses of user behavior and demographics. For Web3, this can include using various tools and platforms to analyze the data, looking at key metrics such as transaction volume and token usage, as well as more detailed analyses of smart contract usage and on-chain activity.
- Create business intelligence: For Web 2.0 and Web3, this can include creating reports, dashboards, and other visualizations that allow the business to gain insights into performance and usage and make data-driven decisions.
- Implement and test: After analyzing the data for both Web 2.0 and Web3, the business can implement changes based on the insights gained and test how the changes affect performance and usage.
- Continual monitoring: For Web 2.0 and Web3, the business should continually monitor the performance and usage, updating the tracking and analysis as necessary to ensure that the data remains accurate and relevant. In Web 2.0, continual monitoring typically involves tracking key performance indicators (KPIs), such as website traffic, conversion rates, and customer engagement from tools like Google Analytics. In Web 3.0 it is tracking KPIs such as smart contract usage, tokenomics, smart contract transactions, and user engagement
The main similarity between the two is the overall process of collecting, analyzing, and creating business intelligence, which is ongoing and requires continuous monitoring. The main difference is the type of data collected and analyzed, and the tools and platforms used to do so. Web2 mainly deals with traditional website data, while Web3 deals with blockchain-based platform or application data.
Challenges in Web2 & Web3 Analytics
Both in Web2 and Web3, data fragmentation, data storage, and customer identify to make it challenging to collect, secure, and analyze the data for business intelligence.
Data Fragmentation
In Web2, data collection typically utilizes cookies and device IDs to personalize product experiences and target promotions through retargeting ads. However, as privacy concerns have grown with regulations such as GDPR and CCPA, companies like Apple and Google have announced plans to phase out third-party cookies, creating data collection and tracking challenges.
On the other hand, Web3 introduces the concept of decentralization, which further complicates data collection and tracking by making it harder for product and marketing teams to accurately track customer behavior and attribute it to specific actions or campaigns. The decentralization of customer identities across multiple wallets and chains in Web3 makes it challenging to collect accurate data and understand customer behavior.
Additionally, Web3 applications often run on Web2 interfaces, adding to the fragmentation and hindering accuracy. For example, a Web3 decentralized marketplace application may be built on a blockchain platform, but the users access and interact with the marketplace through a standard web browser. The data collected on the user’s browsing behavior and interactions within the marketplace would be fragmented between the decentralized blockchain platform and the standard web browser. This fragmentation makes it difficult for the application to track user behavior and create personalized experiences accurately.
Data Storage
In web 2.0, data is typically stored in centralized servers and databases controlled by a single entity, such as a company or organization. This allows for easy access and control of the data but raises concerns about privacy and security. In contrast, web 3.0 stores data in decentralized systems such as blockchain and IPFS (InterPlanetary File System). These systems use distributed networks to store and manage data, which can improve security and privacy. However, the decentralized nature of these systems can make it more difficult to access and control the data.
In web2, analytics is typically performed on centralized data stored in servers or databases controlled by a single entity, such as a company or organization. This centralization allows for easy access and control over the data and the ability to use traditional analytics tools and techniques.
In contrast, in web3, data is stored decentralized using blockchain and IPFS. This decentralized data storage can make it more challenging to perform analytics compared to web2. Some of the challenges include the following:
- Data Access: In web3, data is spread across different network nodes, making it more challenging to collect and access the data required for analytics.
- Data Quality: In web3, data is stored decentralized and distributed, making it more difficult to ensure data quality and consistency.
- Data Governance: In web3, the control and ownership of data are distributed among different entities, making it more challenging to establish and enforce data governance policies and procedures.
- Data Security: In web3, data is stored decentralized and distributed, making it more challenging to secure and protect the data from unauthorized access and manipulation.
- Analytics Tools: In web3, the decentralized nature of data storage can make it challenging to use traditional analytics tools and techniques, typically designed for centralized data storage.
However, despite these challenges, web3 analytics still has some advantages over web2 analytics, such as the ability to perform analytics on a global scale without the need for centralized control and providing more transparency and trust in the data. New tools and techniques are also being developed to overcome these challenges and make web3 analytics more accessible and practical.
Customer Identity
In both web2 and web3, uniquely identifying a customer can be a difficult task. However, the methods used for identification in web2 and web3 are different. In web2, customers are typically identified through a combination of personal information, such as email addresses, IP addresses, and device fingerprints. This information is collected to create a unique identifier for each customer, which can be used to track their behavior across different platforms and websites.
In web3, customers can be identified through their public wallet addresses on the blockchain. These addresses contain transaction data, such as amounts and other metadata, which is publicly available and immutable on the blockchain. However, unlike web2, where the user identifiers are usually public, in web3, the identity can be kept private, and it’s up to the user if they want to link their identity to their wallet.
Additionally, web3 provides more options for customers to create multiple wallets for different use cases, which makes it more difficult to reconcile a set of actions to one identity. Overall, while web2 and web3 have unique challenges when it comes to identifying customers, web3 is more challenging due to the decentralized nature of the platform and the ability for users to create multiple wallets.
Both web2 and web3 have their own set of challenges in terms of analytics; web3 presents a new set of challenges due to its decentralized nature of data storage. Using technologies such as blockchain and IPFS in web3 creates new opportunities for analytics but also requires developing new techniques to analyze this decentralized data effectively. Additionally, privacy and security concerns must be addressed, as well as the need to maintain the decentralized ethos of web3 to ensure the integrity of the data. As web3 adoption continues to grow, it will be necessary for companies to stay up to date with the latest developments in web3 analytics to leverage the data stored on the blockchain effectively.