Web3 Analytics Stack: How to Build an Attribution System Combining Web2 and On-Chain Data

Attribution is the backbone of any performance-driven marketing strategy. In Web2, it’s relatively straightforward to track user journeys from the first ad impression to the final conversion using tools like Google Analytics, Facebook Pixel, or Mixpanel. These platforms rely on cookies, device identifiers, and centralized login data to create a unified view of the customer journey.

But in Web3, those assumptions break down.

Most interactions are pseudonymous, wallets replace emails, and on-chain actions happen outside the visibility of traditional analytics platforms. Web3 users might discover a project on Twitter, join a Discord server, visit a landing page, and complete a smart contract interaction, all without ever creating an account or leaving a trackable identity.

This presents a new and complex attribution problem: How do you measure campaign effectiveness when you can’t follow users in the traditional sense?

The answer lies in a new kind of analytics stack, one that is privacy-first, wallet-native, and flexible enough to span both on-chain and off-chain behavior.

In this article, we’ll walk through how to design a Web3-native attribution system without relying on tools like Google Analytics. We’ll break down the core components of a modern Web3 analytics stack, review key tools like Dune, Spindl, and The Graph, and offer actionable advice for tracking real user behavior in decentralized environments.

If you’re a Web3 marketer looking to move beyond vanity metrics and build a truly data-driven growth engine, this guide is for you.

Understanding the Attribution Problem in Web3

Attribution in Web2 relies on a tightly controlled ecosystem of user identifiers. Marketers track behavior using browser cookies, authenticated sessions, device IDs, and pixels embedded in every touchpoint, allowing them to build detailed multi-channel funnels. This infrastructure has enabled everything from retargeting ads to granular cohort analysis.

But in Web3, nearly all of that breaks.

Instead of email addresses or usernames, users operate through wallet addresses; cryptographic identities that are anonymous by default and not tied to traditional personal identifiers. Moreover, users interact with decentralized applications (dApps) and Discord communities that are often disconnected from any central analytics layer.

Here are a few of the core challenges Web3 marketers face (more detail is provided in a later section):

1. Lack of Persistent Identifiers

Wallet addresses can be reused, rotated, or abandoned. Users often operate multiple wallets for privacy, compartmentalization, or sybil farming. Without a stable identifier like an email address or login session, it becomes difficult to attribute user behavior across time and touchpoints.

2. Disjointed User Journeys

A single conversion might span multiple environments: a user sees a campaign on Twitter, joins a Discord server, connects a wallet to a landing page, and finally mints an NFT on-chain. None of these steps are naturally linked in a standard analytics pipeline.

3. Minimal Front-End Instrumentation

Web3 dApps are often built without robust analytics infrastructure. Many avoid tracking scripts entirely to maintain user trust, leaving key user actions (like wallet connect, staking, or voting) invisible to conventional tools.

4. On-Chain ≠ Attributed

Even though all on-chain activity is public, it’s hard to determine why a wallet performed an action. Did the user find your protocol through a paid campaign, word-of-mouth, or a community referral?

Without a link between on-chain outcomes and off-chain intent, attribution remains speculative.

5. Privacy Expectations and Compliance

Web3 users often expect (read: demand) higher standards of privacy. Any attempt to track behavior must be transparent, ethical, and ideally opt-in.

This rules out many legacy analytics practices and calls for a new approach built around pseudonymity and consent.

These challenges don’t mean attribution in Web3 is impossible, but they do require rethinking your stack from the ground up. Rather than retrofitting Google Analytics into your dApp, it’s more effective to build an analytics system designed specifically for decentralized ecosystems.

In the next section, we’ll break down the core components of that system and show you what a modern, wallet-native analytics stack looks like in practice.

Key Components of a Web3 Analytics Stack

Building a reliable analytics system for Web3 requires a modular approach. Instead of relying on one centralized platform, marketers must assemble a stack of tools and data flows that can collectively answer key questions:

• Where are users coming from?
• What are they doing off-chain (on your website, Discord, socials)?
• What are they doing on-chain (minting, staking, voting, transacting)?
• Which campaigns, referrals, or community efforts are driving conversions?

To answer these questions, a complete Web3 analytics stack typically consists of the following components:

1. Event Capture

The first step in any analytics system is capturing user behavior, both off-chain and on-chain.

Off-Chain Event Tracking

To monitor actions like page visits, button clicks, wallet connections, or email signups, Web3 teams can still use tools like:

• Segment: Offers event tracking that can feed into multiple destinations (e.g., analytics dashboards, CRMs, CDPs). Some Web3 companies use Segment to collect site events tied to anonymized wallet addresses or referral codes.
• Custom JS Tracking: Lightweight scripts on landing pages or mint sites can log wallet connect events, page flow, or specific interactions.
• Discord & Social Integrations: Tools like Commsor or natively configured Discord bots can log joins, community participation, or verified wallet actions.

On-Chain Event Tracking

Smart contracts emit events whenever a user interacts with them from minting an NFT to staking tokens or submitting a DAO vote.

To track these events in a structured way, marketers can use:

• Dune Analytics: For querying on-chain activity and creating dashboards using SQL-like syntax.
• The Graph: For indexing smart contract events using custom subgraphs.
• Footprint Analytics or Nansen: For visualizing trends across protocols or addresses with more pre-built templates.

This combination of front-end tracking and blockchain event indexing forms the raw behavioral dataset that feeds the rest of the attribution stack.

2. Identity Resolution

Once you’ve captured events, the next challenge is tying them to a single user or, in the Web3 context, to a specific wallet (or set of wallets).

Challenges

• Many users operate multiple wallets
• Wallet addresses change over time
• Off-chain behaviors may not map cleanly to on-chain actions

Solutions

• Addressable: Helps marketers associate wallet addresses with broader user profiles using social, behavioral, and off-chain indicators.
• Privy: Offers opt-in identity layers for connecting wallets to emails or Discord handles, with privacy controls baked in.
• Spindl: Tracks wallet journeys from ad click to smart contract interaction using fingerprinting and blockchain data.
• Lit Protocol, Worldcoin, Sismo: Provide on-chain attestations of identity or uniqueness that can help tie behaviors across different wallets.

This layer doesn’t always need to achieve perfect identity resolution but by correlating wallets with known behaviors or opt-in user data, marketers can start to segment and track with more confidence.

3. On-Chain Data Aggregation

Similar to onc-chain event tracking, a robust Web3 analytics stack requires structured access to on-chain data. While blockchain data is public, it’s not indexed in a marketer-friendly format by default. However, there are some tools to make this easier.

• Dune Analytics: The most popular tool for building custom dashboards and querying protocol-specific behavior (e.g., mint events, staking participation, LP activity).
• The Graph: Ideal for building custom data pipelines to track events from your own smart contracts in near real-time.
• Footprint Analytics: Offers no-code dashboards, historical data trends, and useful DeFi + NFT segmentation.
• Nansen: Helps marketers analyze wallet cohorts, behavioral trends, and token flows especially useful for understanding whales or community influencers.

This layer acts as your “database” for on-chain events feeding into attribution models and reporting tools downstream.

4. Attribution Modeling

Now that you’re capturing events and resolving identity to some degree, the next step is connecting the dots: which touchpoints or campaigns actually drove the desired outcomes?

Web3 Attribution Techniques

• Referral codes: Still one of the simplest and most reliable methods to associate wallets with acquisition sources.
• Signature-based referrals: Users sign a message to claim a reward or referral, linking identity to a source wallet.
• Time-based attribution: Tie wallet actions to recent site visits or campaign windows (e.g., wallet X minted within 12 hours of clicking a paid ad).
• Smart contract event chaining: Use event logs to track full user journeys across different stages (e.g., mint → stake → vote).

Tools

• Spindl: Purpose-built for Web3 attribution. Connects on-chain outcomes to off-chain marketing events via lightweight SDKs.
• Cookie3: Leverages both on-chain and off-chain data to model user behavior and cohort engagement.
• Custom Data Pipelines: Many growth teams build attribution logic internally using BigQuery, Snowflake, or even Google Sheets fed by Dune exports.

Attribution modeling is where raw data becomes actionable insight. It helps determine which channels are working, which users are engaged, and which behaviors signal long-term value.

5. Reporting and Visualization

The final layer of your analytics stack is turning data into decisions.

Whether you’re sharing weekly KPIs, monitoring real-time mint activity, or reporting on campaign ROI to stakeholders, you’ll need dashboards that translate data into insights.

Reporting Tools

• Dune Dashboards: Highly customizable, real-time visualizations of on-chain data
• Notion or Airtable: Embedding simplified dashboards or campaign summaries
• Tableau / Looker / Metabase: For teams integrating Web3 data with broader company analytics
• Custom Dashboards: Many Web3 teams build internal frontends (React-based) to display user-level analytics or token engagement metrics

Key Metrics to Track

• Wallet connects and bounce rate
• Conversion rates by campaign or referrer
• Token/NFT holding retention by cohort
• Referral effectiveness (wallets sourced, % retained)
• User behavior progression (mint → stake → vote)

A well-structured reporting layer ensures your team isn’t just collecting data you’re using it to guide decisions, optimize spend, and refine user experience.

Case Study: A Web3 Analytics Stack in Action

To make this more concrete, let’s walk through a hypothetical campaign and show how a modern Web3 analytics stack could be used to track and attribute user behavior from initial discovery to on-chain conversion.

Scenario: NFT Mint Campaign with Community and Paid Channels

A Web3 project is preparing to launch a new NFT collection. The marketing team wants to drive traffic from a mix of community channels and paid media, then track how users engage across the funnel: landing page visits, wallet connects, NFT minting, and post-mint staking.

Goals

• Attribute mints to source campaigns
• Segment users by wallet behavior and retention
• Measure community vs. paid media performance
• Identify high-value users for follow-up marketing

Let’s break down how the stack would be implemented across each layer.

1. Event Capture

Off-Chain Touchpoints

• UTM-tagged landing pages for paid media, Twitter links, and Discord announcements
• A lightweight front-end script tracks wallet connect events, referral clicks, and button interactions
• Segment is integrated to feed data into downstream analytics tools (e.g., BigQuery, internal dashboards)

On-Chain Touchpoints

• The smart contract powering the mint is designed to emit specific events (Minted, ReferredBy)
• The staking contract also emits events (Staked, Unstaked) for post-mint engagement tracking

2. Identity Resolution

• Wallet connects are optionally enriched using Privy, allowing users to associate their wallet with an email or Discord handle
• Referral codes are signed and submitted with mint transactions, linking wallets to known promoters or acquisition channels
• Users are given the option to claim a “Verified Supporter” badge via Sismo, which also acts as a unique identifier across campaigns

3. On-Chain Data Aggregation

• A custom Dune dashboard is created to monitor key events: wallet mints, staking participation, referral activity
• The marketing team also uses The Graph to index contract events into a custom subgraph, making it easier to generate daily performance reports
• Wallet-level engagement data is periodically exported to BigQuery for deeper analysis and cohort modeling

4. Attribution Modeling

• First-touch attribution is determined by UTM data + wallet connect timestamp
• Last-touch attribution is refined using mint timestamps and the embedded referral code
• Multi-touch modeling is implemented using custom logic that weighs campaign interactions over a 7-day window
• A subset of users who mint and stake are analyzed to identify which source channels produce the most long-term engagement

5. Reporting and Visualization

• A weekly report is generated in Notion, embedding a Dune dashboard showing:
• Total mints by channel
• Conversion rate by traffic source
• Retention curve (mint → stake) by cohort
• Top referrers and earned mints
• Stakeholder reports include wallet segmentation showing which campaigns produced “power users” who engage in DAO voting or secondary marketplace activity

Key Outcomes

By the end of the campaign, the marketing team is able to answer critical performance questions:

• Which traffic sources drove the most wallet connects?
• Which channels led to actual mint transactions?
• Which mints were followed by deeper protocol engagement (e.g., staking, DAO activity)?
• Which community members brought in the most high-value users through referrals?

Most importantly, all of this was achieved without relying on Google Analytics or traditional user tracking preserving user privacy while enabling data-informed decision making.

This case study illustrates that attribution in Web3 is entirely possible, it just requires a purpose-built stack and a strategic understanding of the wallet-based user journey.

Challenges and Limitations

While building a wallet-native analytics stack opens the door to more ethical, decentralized marketing, it also comes with real-world constraints. Many of the tools enabling attribution in Web3 are still maturing, and decentralized systems introduce complexity that Web2 marketers have never had to consider.

Here are the five common challenges teams encounter when implementing this type of stack.

1. Identity Fragmentation and Wallet Churn

As previously mentioned: in Web2, a user’s email or login credentials are often stable over many years. In Web3, users may rotate between multiple wallets for privacy, compartmentalization, or similar reasons. This fluid identity model makes it difficult to build cohesive user profiles or measure lifetime engagement accurately.

While some protocols offer on-chain attestations or wallet-linked identity layers, these require user opt-in and aren’t yet universally adopted. As a result, attribution models may struggle to distinguish between unique users and duplicate wallets, especially in high-volume campaigns like airdrops or NFT mints.

Marketers should approach wallet identity probabilistically, not definitively, and build systems that can handle anonymous, fragmented engagement as a feature of the space, not a bug.

2. Incomplete Attribution Across Chains and Protocols

Web3 users are highly mobile. They bridge assets across chains, interact with different smart contracts, and participate in multi-chain ecosystems. Unfortunately, many analytics setups are isolated to a single chain or protocol instance, missing significant parts of the user journey.

To mitigate this, teams should prioritize:

• Chain-agnostic data indexing (via subgraphs or multi-chain queries)
• Cross-contract event standardization
• Campaign attribution logic that isn’t tied to just one deployment or platform

This type of cross-environment data visibility is still difficult but increasingly necessary for accurate insights in an increasingly cross-chain environment.

3. Developer Dependency

Many of the most powerful Web3 analytics tools are designed for technical users. Writing custom subgraphs, querying smart contracts, managing APIs, and stitching together multiple data sources often fall outside the capabilities of a typical marketing team. This creates a dependency on engineering support for even modest attribution needs. Teams without dedicated analytics teams or Web3-aware developers can find it difficult to: trigger structured events from dApps, customize attribution models, and maintain real-time dashboards tied to on-chain behavior.

The best solution is to design analytics requirements into your product or campaign workflow from the beginning, rather than bolting them on after launch. Early planning can reduce tech debt and prevent gaps in your data layer.

4. UX and Consent Expectations

Privacy is a core cultural and technical value in Web3. Many users are wary of tracking mechanisms, even ones that seem harmless in Web2, like UTM parameters or event logging. This creates a tension: marketers need insight into user behavior, but heavy-handed tracking can degrade trust and participation.

Unlike in Web2, you can’t assume tracking is passive or invisible. Instead, you need to design opt-in, transparent mechanisms that respect user autonomy. That means:

• Using wallet signatures instead of hidden cookies
• Offering rewards or perks in exchange for enriched identity
• Clearly communicating why you’re asking for wallet data or on-chain engagement

Web3 marketing requires a shift in mindset: you’re not just collecting data, you’re asking users to contribute it willingly. The best-performing campaigns bake that value exchange into the core experience.

5. Tool Fragmentation and Data Silos

The Web3 analytics landscape is still highly modular. Different tools specialize in different parts of the stack, on-chain indexing, user segmentation, attribution modeling, dashboarding, but few offer comprehensive, integrated solutions.

As a result, marketing teams often juggle:

• A query tool for on-chain data
• A platform for attribution modeling
• A separate service for wallet enrichment
• A reporting interface for stakeholders

This patchwork architecture introduces data fragmentation, inconsistent metrics, and added overhead for syncing tools. Without strong internal processes and shared definitions, reporting can become unreliable or overly manual.

To address this, teams should:

• Consolidate tooling where possible (e.g., choose platforms that handle both data and visualization)
• Define shared metrics and attribution rules across teams
• Invest in documentation and internal dashboards that reduce tool-switching

Bottom Line

The limitations of Web3 attribution are real, but they are solvable, especially for teams willing to experiment, collaborate with devs, and invest in structured data flows. Unlike Web2, where analytics is mature but intrusive, Web3 offers the opportunity to build measurement systems that are both powerful and privacy-aligned.

Final Thoughts and Recommendations

Web3 marketing is still in its formative stages, and so is the infrastructure required to measure and optimize it. While traditional tools like Google Analytics fall short in this new environment, wallet-native analytics stacks are emerging to fill the gap. These stacks prioritize user privacy, leverage on-chain transparency, and empower marketers to understand decentralized behavior without compromising trust.

If you’re a marketer building in Web3, attribution may feel like a moving target. That’s because it is. But with the right mindset and toolkit, it’s possible to build a system that aligns with both your growth goals and the core values of the space.

Here are key recommendations for getting started:

1. Start Simple, Then Scale

You don’t need to launch a multi-layer analytics stack on day one. Begin with foundational elements:

• Add wallet connect tracking to your landing pages
• Tag referral links using wallet-friendly parameters or signed messages
• Track key on-chain actions (mints, stakes, votes) via a basic Dune dashboard

Once that’s in place, you can layer on attribution models, identity resolution tools, and cohort analysis as your team matures.

2. Design for Attribution at the Smart Contract Level

Many teams overlook the opportunity to build analytics into their product’s technical architecture. If your smart contracts emit structured events, with context like referral codes, session IDs, or campaign tags, your ability to analyze behavior increases dramatically.

Work with your developers early in the lifecycle to define:

• What you want to measure
• How you’ll surface those events later (subgraphs, queries, etc.)
• How those events connect to your user journey

This makes retroactive analysis easier and enables cleaner attribution models.

3. Prioritize Ethical, Consent-Based Data Collection

In Web3, the user is in control. Respecting that is not only the right thing to do, it’s also better for long-term engagement.

• Make it clear when and why you’re collecting wallet activity
• Offer incentives or value for sharing additional context (email, social, etc.)
• Use privacy-preserving techniques like wallet signatures and on-chain badges instead of passive tracking

This fosters trust, increases opt-in rates, and aligns your analytics approach with the ethos of decentralization.

4. Invest in Interoperability and Internal Alignment

With multiple tools handling different layers of your stack, internal consistency is critical. Define your metrics, cohort definitions, and attribution models clearly, and make sure everyone on your team is working from the same playbook.

• Create a shared analytics documentation hub
• Build standardized templates for reporting
• Align with product, growth, and dev teams on data definitions

This will reduce noise, speed up experimentation, and make your marketing efforts far more effective.

5. Stay Agile. The Stack Will Evolve

New tools are being released monthly, new identity protocols are emerging, and user behavior is constantly shifting. Rather than locking into one rigid solution, build your stack with flexibility in mind:

• Use modular tools with APIs and integrations
• Avoid overengineering attribution logic too early
• Keep an eye on innovations in ZK-powered analytics, identity aggregation, and multi-chain attribution

Being agile allows you to adopt best practices as the ecosystem matures, and stay ahead of the curve.

Looking Ahead

Attribution in Web3 is more than a technical challenge, it’s a philosophical one. It forces marketers to rethink how value is measured, how users are respected, and how insights are earned rather than extracted.

The opportunity here is to build something better than what came before: analytics that are transparent and designed for the Web3-focused future.

If you’re ready to build a Web3 attribution stack that works Coinbound is here to help.