ColdChain Agency

Web3 Growth & Digital Marketing Agency

Web3 Analytics, Measurement, Data & Attribution

Without data you are just another opinion. We help you get your measurement strategy in shape to track accurately your users and make smarter data-driven marketing decisions.

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Data-informed Decisions at the Heart of your Marketing.

Data Analytics is an indispensable tool for businesses of all sizes, playing an essential role in shaping strategic decision-making and driving growth. In a world where data is king, web analytics provides a comprehensive understanding of customer behaviour, offering key insights into the impact of marketing efforts, the user journey, and overall business performance. By leveraging the power of web analytics, businesses can make informed decisions, identify areas for improvement, and optimise their marketing strategies for maximum impact.

With the rise of blockchain technology and the decentralisation of data, traditional web analytics tools are not effective in tracking user behaviour in the new web. We have decades of building data and analytics frameworks for multi-billion turnover businesses, and can help you plan and build customised analytics solutions for web3 applications, providing you with the insights and data you need to optimise your platform to drive growth.

Our analytics services often include:

  1. User Tracking: Track user behaviour on your web3 application, including page visits, clicks, and conversions.
  2. Conversion Funnel Tracking: Analyse the user journey from initial visit to conversion, identify areas of improvement and optimise your conversion funnel.
  3. Audience Insights: Gain a deeper understanding of your audience, including demographics, interests, and behaviours.
  4. Web3 Data Integration: Integrate web3 data sources, such as blockchain transactions, smart contract events, and on-chain analytics, into your data analytics pipeline.
  5. Custom Reporting: Customise and automate your reporting, with tailored reports that meet the specific needs of your business.

What Is Difference Web3 Analytics & Attribution?

While Analytics measures what your users do and what actions they take, attribution identifies patters and trends across marketing channels to understand the effectiveness of your marketing. Its output is to show how valuable and influential a various marketing channels are to the customer’s decision-making process.

Attribution tools analyse and track touchpoints to determine the most effective ways to influence the audience’s journey towards key outcomes such as developer contributions, dAPP usage and wallet activity. The goal with any attribution model is to provide simple data that helps marketers optimise their campaigns to prevent wasted resource and money.

You’ll get a true understanding of how your content works together to form user journeys. And from there, you can make data-driven decisions to allow you to optimise your campaigns.

This allows web3 marketers to prioritise their efforts and become more efficient with their marketing campaigns and budgets. Using attribution generally leads to higher knowledge, higher return on investment, lower costs and more efficient work.

Why Is Analytics & Attribution So Important In Web3?

Marketing attribution is crucial in helping businesses understand their target audience. Marketers rely on research and assumptions about their audiences, including their preferred content channels, topics of interest, and tone of voice. Attribution helps businesses confirm or re-evaluate the information they have collected about their audience, providing a deeper understanding of how to reach them effectively. For example, if attribution data shows that a video performed better on social media than expected and an article performed better in an email campaign than anticipated, it means the business has gained a new understanding of their audience and can adjust their strategy accordingly.

The Web3 Attribution Challenge

In the Web2 landscape, companies have relied on the use of cookies and device IDs to connect data to better understand their customers to provide better products, experiences and services. This is now changing, with increasing privacy concerns and regulations such as GDPR and CCPA that mean the even matured Web2 businesses are looking at cookieless tracking and marketing mix models to continue to innovate while access to data becomes more challenging.

Web3 brings new challenges for data collection and attribution with an even more fragmented user journey than web2. The decentralised nature of Web3 introduces complexities that were not present in the traditional Web2 landscape. Innovation is required to bridge onchain and offchain user journeys to form a clear understanding of marketing and product impact on target audiences.

Data analytics and attribution are key factor in the success of any web 3 organisation, as it provides valuable insights for informed decision-making. With the decentralised nature of blockchain networks, user data can be spread across multiple sources and become challenging to track, making attribution even more critical for web3 companies. By accurately attributing user actions, web3 companies can get a clearer picture of their users and the impact of their marketing efforts.

Frequently asked.

What is web3 analytics?

Web3 analytics refers to the process of collecting, processing, analysing and interpreting data related to website traffic and decentralised applications (dApps) and their usage on blockchain networks.

What kind of data is collected in web3 analytics?

Typical data in web3 analytics will include traffic, actions and other behaviour on site as well as various aspects of dApp usage including transactions, smart contract interactions, token transfers and other actions that are deemed important.

How is web3 analytics different from traditional web analytics?

Traditional web analytics often focuses on centralised websites and their usage, whereas web3 analytics focuses on decentralised applications and their interactions with blockchain networks. This results in differences in the type of data collected and the methods used for analysis.

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