Blockchain Analytics for Web3 Apps
.png){kind=avatar}
-
July 15, 2025
When you’re building a Web3 product in 2025, the hardest part isn’t the blockchain integration itself—it’s getting fast answers to questions like:
- How can we spot, in real time, that a major investor is pulling assets off the platform?
- Which new users failed KYC or got stuck in compliance?
- Why did a token suddenly become more liquid—because of a fresh listing or market manipulation?
If you’re new to launching Web3 products or want a bigger-picture playbook, check out our guide: Web3 for Startups: How to Build and Scale a Decentralized Business.
For teams operating under tight regulation and growing competition, these questions pop up every day. Traditional off-chain analytics no longer cut it; most problems surface only after they’ve already hurt the business.
Last year we helped launch CanDoo (RWA), a European marketplace for tokenised real-world assets. By adding advanced blockchain analytics—live trade tracking, automated alerts on suspicious wallets, and deep integrations with Chainalysis and Nansen—the team watched user behaviour shift:
- Liquidity jumped by almost 40 %.
- Manual KYC reviews dropped by half.
- Users returned more often for repeat trades.
This article focuses on the analytics features and tools that actually drive growth, ensure compliance, and keep a Web3 product resilient. We’ll outline the functions that become standard in 2025, show how to roll them out quickly, and reveal the hidden growth levers that never show up in a basic dashboard.
Must-Have Web3 Analytics Features in 2025
The biggest turning points for a Web3 platform don’t happen in user-facing features — they happen in the background, at the exact moment you spot an anomaly, freeze a suspicious trade, or deliver a report to regulators without delay. For this, analytics isn’t just a feature; it’s a foundation.
Key Web3 Analytics Features and How They Work
On-Chain Data Tracking
Index every smart contract event as soon as it happens. With tools like The Graph and custom subgraphs, platforms get a live feed of mints, transfers, or listings. For example, on CanDoo, every asset trade and user action is instantly reflected in the UI, helping both admins and investors see changes the moment they occur.
(See CanDoo’s implementation for a working example.)
Wallet & Transaction Monitoring
Knowing who’s active on your platform is critical. Integrations with Nansen or Arkham let teams track whales, funds, and trading bots in real time. At CanDoo, wallet labels and risk scores flag potentially suspicious trading, which builds transparency and protects honest users.
Compliance / AML / Tax Reporting
Automated tools like Chainalysis KYT, Elliptic, or TRM perform instant sanctions screening and AML scoring on every transaction. CanDoo uses a webhook integration to trigger compliance checks whenever a new user registers or trades, letting the platform stay ahead of regulatory risk and making audits simple.
For a step-by-step legal playbook that covers KYC, GDPR, and store-review rules, see our Mobile App Compliance Checklist: Legal Essentials Before Launch.
Cross-Chain Insights
Assets now move across Ethereum, Polygon, and emerging L2s. Tools like Dune multi-chain and LayerZero help teams follow activity wherever it happens. CanDoo is preparing a Layer-2 rollout to expand asset tracking and analytics across networks.
For a practical guide on setting up cross-chain crypto payments, KYC, and bridge fees, see our How to Integrate Crypto Payments in Your App or Website.
NFT / DeFi / Token Analytics
Monitoring floor prices, pool liquidity, and token supply helps spot risks early. With DeFi Llama and Nansen God Mode, teams can see when a collection is losing traction, or when a DeFi pool needs closer review.
AI-Powered & Privacy-First Solutions
2025 is the year when fraud detection goes AI-first. Platforms now deploy ML models to catch wash trades and hidden collusion, while privacy-first design—using zero-knowledge proofs—keeps user data compliant. CanDoo uses an AI-based fraud alert system to spot abnormal activity without storing sensitive personal data.
Curious what that level of protection actually costs? See the real numbers in our Mobile App Fraud Prevention Cost in 2025 study — it breaks down ROI vs. risk and shows why skimping on anti-fraud is never cheaper in the long run.
2025 Tool Landscape: APIs, SDKs & Platforms
In 90% of Web3 projects we’ve helped build or audit since 2024, teams ended up combining 3–5 of these tools. The Graph and Dune usually cover the basics, but as soon as you need automated risk flags or compliance-grade audit trails, you’ll be reaching for Chainalysis, Elliptic, Nansen, or Arkham.
None of the 30+ teams we’ve worked with got away with “just one tool.” Each added specific layers for compliance, growth, or reporting — based on what questions they needed answered, not just what was easy to track.
The Graph
The Graph is what almost every serious Web3 product uses to keep track of what’s happening on-chain. You set up a “subgraph” to track events from your smart contracts, so whenever a user does something — mints an NFT, lists a token, makes a trade — you can show that instantly in your app.
This is the backbone for most NFT marketplaces, DeFi platforms, and tokenized asset apps. It’s fast and easy to update as your product changes.
Getting started can be tricky if no one on your team has worked with GraphQL, but once you’ve got a subgraph up, everything — analytics, user activity, notifications — is much smoother than trying to build it from scratch.
Chainalysis / Elliptic / TRM
These are the go-to services for compliance and anti-money-laundering (AML) checks in crypto and tokenized asset projects.
Any company handling real assets, investor money, or planning to operate in regulated markets (EU, UK, Asia) basically has to use them.
How it works: your app sends wallet addresses or transactions to their API, gets a risk score or a flag if there’s a problem, and logs everything for your compliance team or the regulator.
It’s expensive for small projects, but if you want to attract serious users or raise investment, these tools are expected. No regulator will accept homegrown scripts.
Nansen and Arkham
Nansen and Arkham are used by teams who need to know who’s really using their platform.
They automatically tag wallets belonging to funds, whales, trading bots, or other known entities, so you can spot when big players show up, or when there’s suspicious coordinated activity.
This helps for risk management, but also for growth: you see who’s driving volume, and can catch wash trading or market manipulation early.
Downsides: subscriptions are pricey, and some wallet clusters update with a delay, but for high-volume projects, these tools pay for themselves.
Dune & Open-Source Analytics
Dune Analytics lets you build your own dashboards and reports directly on blockchain data using SQL.
You can track anything — token flows, cross-chain activity, unusual patterns — and borrow (or fork) other people’s queries if you’re not a SQL expert.
Great for data scientists or when you need to answer product-specific questions the “off-the-shelf” tools can’t cover.
If you need more privacy or want to run everything in-house, there are open-source solutions like Ethereum ETL, but then you’ll need to manage your own infrastructure.
Glassnode
Glassnode aggregates market metrics — things like network activity, liquidity changes, profit/loss ratios — and puts it all in one place.
Funds and trading desks use it to get a sense of where the market is going, or when there are shifts in investor sentiment.
Useful for making portfolio decisions or reporting to stakeholders. Some data is free, some is behind a paywall, and there’s a slight delay, but for market overviews it’s more than enough.
For a technical deep dive into fast, scalable AI-powered search for in-app dashboards, check out our review: Best In-App AI Search 2025: Algolia vs OpenAI vs Elasticsearch.
How Teams Actually Integrate Blockchain Analytics (Step-by-Step, No Bullshit)
Make analytics a real part of the product — not an afterthought. Start with the decisions you need to make, not just what you could measure. And don’t trust any tool to solve your problems for you — they’re just shovels, you still have to dig.
Step 1. Decide What You Need to See
Before any code, the team sits down and lists the questions they actually want answered. Not “let’s measure everything,” but “what will help us act faster or avoid disaster?”
For example: “We want to see when a new big holder shows up, when liquidity drops fast, or if any transaction fails KYC.” It sounds obvious, but half the projects forget this and end up swimming in useless charts.
Step 2. Add Events in Your Contracts
The devs go through the smart contracts and make sure every important action fires an event: new listing, sale, withdrawal, KYC result, whatever matters for your app. If you forget something here, it’s a pain to fix later — usually means a full redeploy or ugly workarounds.
Step 3. Set Up a Subgraph (or Equivalent)
Whoever’s done The Graph before sets up a subgraph to pull all those events out in real time. This is the layer that actually feeds your dashboards and triggers — so you can show users live data and track things without polling nodes all day.
Step 4. Start With the Basics on the Frontend
Pull in just enough data to show real user impact: recent sales, failed trades, new assets, whatever matters. Don’t build a “pro dashboard” right away; launch with the essentials and add more as you see what people actually use.
Step 5. Plug in Compliance if You Need It
If you’re dealing with real assets, money, or investors — plug in Chainalysis or Elliptic early, even if it hurts the budget. Hook it up so every transaction and user gets checked automatically, and save the risk scores/logs somewhere safe (you’ll need them).
Step 6. Alerts Before Dashboards
Most problems don’t show up on dashboards — they show up as sudden weird activity. Set up alerts for things like big trades, failed KYC, liquidity drops, or repeated bot activity. Make sure alerts go where someone will see them — Telegram, Slack, whatever your team actually checks.
Step 7. Keep Adjusting
Every month, check what’s getting tracked versus what people actually care about. Cut the noise, add missing stuff, update documentation so the next dev doesn’t have to dig through code or config files.
Where Most Teams Screw Up
- Trying to build everything at once: It’s a time sink. Start simple, grow later.
- Forgetting about cross-chain: Users move money everywhere now. If your app touches multiple chains, set up analytics for each from the start.
- Leaving compliance to the end: Huge mistake. Regulators don’t care that “the feature wasn’t ready.”
- No alerts: If nobody gets pinged, nobody acts. Dashboards are for trends, not emergencies.
What actually matters in blockchain analytics right now?
Detecting fraud and bots is not optional.
If you don’t have at least a basic ML module catching suspicious patterns—like sudden volume spikes, self-trading, or new networks of linked wallets—you’re simply exposing your platform to risk. Modern teams wire these alerts directly into the dev chat or admin panel, so the response is instant, not after the damage.
Quick check: Do you have even one machine learning model running on your own data? If not, start with any open-source toolkit. You’ll see the difference right away.
No personal data, or you lose serious clients.
Compliance for Europe and Asia is strict. You need analytics that can surface insights without exposing individual user details. Zero-knowledge or at least strong aggregation is not a “nice to have” — it’s the barrier to entry for working with institutional money.
How to check your vendor: Ask them to show you real anonymization or ZK implementation—not “we’re planning it,” but something already running.
Cross-chain visibility is required, not futuristic.
Users move assets between L2s, bridges, and different blockchains all the time. If your analytics only see one chain, you’re missing major risks (and, frankly, cash leaks). Top platforms already merge LayerZero, Dune, and other cross-chain sources to see the full picture.
Always ask: Which cross-chain integrations are live, and how is this data actually stitched together in your dashboards?
Automated triggers, not manual reports.
If your system relies on a person reviewing logs or dashboards to spot trouble, you’re already too slow. Reliable teams set up auto-triggers—blocking or freezing activity instantly if compliance fails, suspicious volumes appear, or a new bot pattern emerges.
Critical rule: If your scenario needs a human to react, automate it instead.
Why does all this matter right now?
- So you don’t lose funds to obvious exploits.
- So you’re not blindsided by privacy audits or regulatory action.
- So you can answer investor questions about risk and fraud, and not lose deals.
- So your next audit takes days, not months.
Everything else—features, dashboards, pretty charts—is secondary.
If your team or vendor says “we’ll do this later,” that’s a red flag: they’re not protecting your business.
Real-World Use-Cases & Case Studies
Real-world analytics isn’t theory — it’s what separates growing platforms from those that stall or get shut down. Here are four cases from the last year where smart use of blockchain analytics made a clear, measurable difference for teams and their users. These examples cover RWA markets, NFT trading, DeFi protocols, and institutional onboarding — showing how the right data, used at the right moment, changes outcomes.
Case 1. NFT Marketplace: Stopping Wash Trading in Its Tracks
A top-20 NFT marketplace was losing user trust due to inflated volume from self-trading and bot activity.
- They rolled out an in-house ML model that detected typical wash trading loops (same wallets trading with themselves, or using rings of related addresses).
- Within two weeks, suspicious volume dropped by over 40%.
- Sellers caught “gaming the charts” were automatically de-prioritized in listings, protecting genuine artists and buyers.
Case 2. DeFi Lending Protocol: Early Detection of Liquidity Drains
A DeFi protocol suffered from rapid liquidity outflows during market stress, often spotted too late for meaningful action.
- The team implemented real-time cross-chain analytics (using Dune, LayerZero integrations), tracking large, sudden withdrawals and bridging activity.
- When abnormal flows hit a threshold, the system triggered instant risk alerts and temporarily raised collateral requirements for new loans.
- The protocol avoided two potential “bank run” scenarios in Q1 2025, protecting users and the protocol’s reserves.
Case 3. Institutional Onboarding: Privacy-First Analytics
A new on-chain fund platform wanted to onboard EU/Asia institutions but faced tough privacy requirements.
- Instead of just anonymizing reports, they used zero-knowledge modules for KYC and transaction scoring—only a “pass/fail” flag is shown to ops teams.
- This approach allowed them to pass two major compliance audits and onboard their first regulated fund without delays.
Case Study: CanDoo — Unlocking RWA Liquidity with End-to-End Web3 Analytics
CanDoo’s founders came in blunt: “Tokens listed, buyers interested, but trades stall. Liquidity’s frozen and manual KYC is killing us.”
We saw the same in the data:
- 50 % of tokens inactive — no bids, no views.
- KYC over Telegram — days, not minutes.
So we wired analytics into every critical path and let the numbers steer the workflow.
1. On-Chain Tracking & Asset Nudges
Every listing, trade, or transfer now fires an event to a subgraph. If a token sleeps for 30 days, the admin gets a Slack ping: “Re-list or highlight?”
A single typo fix (Berlin → Bern) revived the first “dead” asset within 24 hours.
2. KYC / KYT Automation
Sumsub + Chainalysis webhooks validate users in real time. Legit docs clear in ≈ 2 min; only red-flag cases escalate to humans, halving compliance load.
3. Wallet & Trade Surveillance
An ML rule set flags wash-trading loops (same asset, same wallet cluster, ≥ 3 hops/week). Offending sales auto-freeze and notify ops — no leaderboard gaming.
4. Real-Time, Investor-Facing Dashboard
Live history, ownership chain, and risk tags are visible to every buyer. One German investor’s feedback: “First marketplace where I can audit the asset myself.”
Tangible Results (4 Months)
- +38 % liquidity on the secondary market — our system flagged dormant assets automatically(internal data, Q1 2025).
- –57 % manual KYC workload, <2 min onboarding — automated webhook checks handle most reviews (CanDoo internal data, March 2025).
- 0 major fraud incidents.
- Sub-2-minute onboarding for clean users.
Charts are nice, but instant signals + automated blocks = growth. Without live tracking and KYT, CanDoo would still be waiting for its first real wave of liquidity. Next up: cross-chain rollout and smarter asset curation — built on the same analytics spine.
CTO / Product Checklist: Blockchain Analytics Integration
Before you sign off on any analytics integration, make sure your vendor or dev team can answer these — and show it in action, not just in docs:
1. On-Chain Data Tracking
- How exactly will every listing, trade, or transfer be indexed and surfaced?
- Can the system alert you when an asset is inactive or starts moving abnormally?
- Ask: “Show me how you’d spot a dormant token or catch a sudden volume spike.”
2. Wallet & Transaction Monitoring
- Does your setup flag suspicious patterns—like self-trading, wallet rings, or bot-driven spikes?
- What tools or heuristics are used for wallet labeling?
- Ask: “Walk me through how the system identifies and freezes a wash trading attempt.”
3. Compliance, KYT, and Automated Reviews
- Is KYC/AML 100% automated, or do manual reviews slow you down?
- What happens when a trade is flagged—who gets notified, and how fast?
- Ask: “How many manual KYC reviews did your last launch actually require?”
4. Cross-Chain Analytics
- Which chains and bridges are covered?
- Can you see user activity and flows across networks, not just in your mainnet?
- Ask: “Show me one user’s wallet activity across at least two chains in your dashboard.”
5. NFT, DeFi, and Token Metrics
- How are you tracking sudden drops in liquidity, suspicious minting, or unusual price changes?
- Can you alert users (or the team) in real time?
- Ask: “What’s your fastest alert-to-action time for a pool drain or supply spike?”
6. AI & Privacy-First Analytics
- Do you use machine learning or just fixed rules?
- How do you keep user data private—even while flagging suspicious events?
- Ask: “Is any personally identifying info ever exposed to admins or 3rd parties?”
7. Automation, Alerts, and Manual Intervention
- Are all critical triggers automated, or does someone have to check logs?
- What gets frozen or blocked automatically?
- Ask: “If a regulatory threshold is crossed, show me the exact alert and what happens next.”
8. Audit Trails & Transparency
- Can you export a full log of actions for audit or dispute?
- Are critical actions (freezes, unlocks, KYC passes/fails) fully logged and tamper-proof?
- Ask: “Export and share a real sample of your audit logs.”
9. Blind Spots & Edge Cases
- What does your analytics not see yet? (e.g. off-chain deals, certain bridges)
- How will the team update coverage when new risks appear?
- Ask: “What blind spots did you find in past projects, and how did you fix them?”
If a vendor can’t quickly walk you through real scenarios like CanDoo’s — finding dormant assets, catching wash trading, auto-freezing compliance risks — they’re not ready for production.
Push for working demos, real data, and clear answers on automation, coverage, and privacy.
Competitor Blind Spots & Differentiation
Even the biggest names in blockchain analytics — Nansen, Dune Analytics, Elliptic — and high-profile RWA or NFT marketplaces like RealT, Republic, OpenSea Pro, still run into the same roadblocks. Here’s what we kept seeing in practice, and what’s already working better in live projects like CanDoo.
1. Missing Cross-Chain Activity
Most dashboards follow a single chain well, but lose sight when assets move via Polygon, Arbitrum, or bridges like LayerZero. The result: deposits and trades get fragmented, and risk monitoring stops at the edge of the mainnet.
At CanDoo, cross-chain analytics now catch duplicate deposits and suspicious bridge flows before any damage happens.
2. Manual KYC Slowdowns
Plenty of platforms talk about “fast onboarding,” but as soon as larger sums or new countries come in, the process falls back to manual review. Deals stall, users wait, and admins get buried in paperwork.
Automated compliance checks have now replaced most manual KYC at CanDoo, letting users pass onboarding in minutes, not hours.
3. Dormant Assets Left Untouched
On OpenSea, Rarible, Republic and others, it’s common to see tokens sitting idle for weeks or months. This quietly drains liquidity and can make any marketplace look more active than it really is.
With automatic flagging of inactive assets, admins can spot and relist them quickly — a change that unlocked a 38% liquidity boost on our own platform.
4. Weak Controls Against Wash Trading
Many NFT and DeFi dashboards still struggle to reliably flag fake trading volumes or wash trading loops. It’s easy for bad actors to climb the leaderboards or create false activity.
Using wallet behavior analytics, fake volume and suspicious trading patterns now get spotted and removed from public stats before they distort the market.
5. Privacy Overlooked
Some analytics tools still let staff or third parties see sensitive user data, which creates compliance and reputational risks — especially in Europe and Asia.
By limiting compliance visibility to just a pass/fail indicator, sensitive info stays protected and onboarding for institutions speeds up (this is already confirmed by external audits).
6. Slow or Missed Risk Alerts
Too many platforms still wait for someone to check a dashboard or email before acting on fraud or compliance issues. By then, funds may be lost or damage is done.
Automated real-time alerts for fraud, failed checks, or odd liquidity spikes have helped catch problems before users even notice.
FAQ: Blockchain Analytics Features for Web3 Apps (2025)
What are blockchain analytics tools?
Blockchain analytics tools are platforms that track and analyze blockchain data, helping teams monitor transactions, wallets, and network activity for compliance, security, and growth.
What are the key features of blockchain analytics?
Key features include real-time on-chain data tracking, wallet and transaction monitoring, compliance/AML screening, cross-chain analytics, DeFi and NFT metrics, and privacy-first or AI-powered insights.
How do you integrate blockchain analytics into an app?
Connect your app to analytics APIs or SDKs (like The Graph or Nansen), fetch blockchain events, and use built-in modules for compliance, reporting, and dashboards.
What is blockchain data analytics?
Blockchain data analytics means extracting insights from blockchain transactions, such as tracking token movements, identifying suspicious activity, and measuring user behavior.
Which blockchain is best for analytics?
Ethereum and EVM-compatible chains (Polygon, Arbitrum, BNB Chain) are most widely supported for analytics tools, but the best choice depends on your app’s needs and user base.
What does a blockchain analytics API do?
A blockchain analytics API provides indexed blockchain data—like token transfers, wallet histories, risk scores, and compliance flags—for integration into your product or dashboard.
Why is cross-chain analytics important?
Cross-chain analytics let you track assets and users across multiple blockchains, detect duplicate funds, and close compliance gaps that single-chain tools might miss.
How does analytics help with compliance (KYC/AML)?
Analytics platforms automate KYC/AML by checking wallets against watchlists, scoring risks, and generating reports for regulators in real time.
What are the top trends in blockchain analytics for 2025?
AI-based fraud detection, privacy-first analytics (zero-knowledge proofs), quantum-resistant tools, omnichain coverage, and plug-and-play APIs for compliance.
Next Steps: Estimate Your Web3 Analytics Build
If you're building a Web3 product in 2025, strong analytics aren’t optional — they’re the layer that keeps your platform safe, compliant, and ready to scale. Real-time tracking, wallet intelligence, cross-chain visibility, and automated reporting should be part of your core stack from day one.
Don’t wait until you hit a wall with fraud, KYC delays, or investor questions.
To get a realistic sense of how much it’ll cost to implement blockchain analytics into your product — from APIs to dashboards to compliance logic — use our AI-driven cost estimator. It’s based on real-world builds and gives you accurate numbers in minutes.
Meet Our Expert Flutter Development Team
Our full-cycle Flutter development team at Ptolemay specializes in building high-quality, cross-platform apps from start to finish. With expert skills in Dart, backend integrations, and seamless UX across iOS and Android, we handle everything to make your app launch smooth and efficient.
.png)
