Key Takeaways:
AI-powered DeFi analytics makes your data queryable in plain language so growth questions that used to require SQL get answered in seconds.
Before AI can help, your offchain events and onchain transactions must be unified under a single identity and data model. Fragmented data produces incomplete answers.
The four highest-value analyses for DeFi growth teams are activation funnel breakdown, wallet cohort segmentation, retention pattern detection, and onchain attribution. Each one maps directly to a growth decision.
Most DeFi growth teams are not short on data. Onchain transaction logs, wallet connect events, referral sources, smart contract interactions across multiple chains: the raw material is there. What teams are short on is the ability to interrogate it without a data analyst or a long wait for a Dune query.
A question like "which wallets from our Base campaign transacted within seven days, and how do they compare to wallets from Discord?" could take 20 minutes to answer with SQL, assumes someone on the team can write it, and still tells you nothing about offchain behavior. For most teams, the question never gets asked.
That is the gap AI-powered DeFi analytics closes. Not by replacing your infrastructure, but by making it queryable by anyone on the team, in plain language, tied to both onchain and offchain data.
One distinction worth making upfront: this article is not about DeFAI, the category of AI trading agents and autonomous yield optimizers. It is about using AI to understand your own protocol's users:
who they are
where they drop off
which ones stay
and whether your growth spend is producing wallets that transact.
Step 1: Understand Why DeFi Product Data Is Structurally Different
AI analytics tools built for SaaS assume users have accounts, sessions are trackable across visits, and the most important user actions happen in a browser. None of these hold in DeFi.
In DeFi, users are pseudonymous wallet addresses. A single person may operate three wallets across two chains and never create a login. The most meaningful actions, a swap, a deposit, a borrow, happen onchain, outside the reach of Google Analytics or Mixpanel.
The data that would let you understand:
user's quality
their DeFi history
protocol usage
capital profile
lives on the blockchain, not in your product database.
This creates what practitioners call the two-plane problem:
your offchain data (page views, wallet connects, referral sources, UTMs) sits in your analytics platform
your onchain data (transactions, smart contract events, token balances) sits on the chain.
Most teams treat these as separate. That separation is what makes AI analysis unreliable, and what makes unifying them the prerequisite for everything that follows.
Not all data signals in DeFi are equally useful. The table below separates the metrics worth analyzing from those that look informative but routinely mislead.
Signals worth analyzing | Signals that mislead |
Raw wallet connect volume | |
Time to first transaction by acquisition channel | Total transaction count |
Day-7 and Day-30 retention by wallet cohort | TVL (can rise while users churn) |
Onchain wallet history and DeFi experience level | Testnet or incentive-driven activity |
Active wallets (often includes bots) | |
Which channel drove wallets that transacted | Impressions and click-throughs in isolation |
Step 1 checklist: before you start
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Step 2: Get Your Offchain and Onchain Data in One Place
AI-powered DeFi analytics cannot work across data it cannot see. If your offchain event data and your onchain transaction data sit in separate systems with no shared identifier, no AI querying layer can answer the questions that matter most.
In practice, unification means stitching every wallet address to both sides of its journey.
On the offchain side:
which page did this wallet land on first
what referral source brought them
did they click a campaign link before connecting
On the onchain side:
what did this wallet do after connecting
how long did it take to transact
have they returned
Tools that handle this unification at the SDK level, where a single install captures page events, wallet connects, and transaction events in sequence, remove the need to build a custom data pipeline.
Without unification, AI analysis can answer questions about onchain behavior or offchain behavior separately. It cannot answer the questions that drive growth decisions: how acquisition connects to activation, and activation to retention.
Example: What unified data makes possible A DeFi lending protocol wants to know whether wallets from their Arbitrum ecosystem campaign behave differently from wallets that arrived organically. With unified offchain and onchain data, a single query returns:
Without unification, that answer requires three separate data pulls and a manual join that most teams never get around to doing. |
Step 2 checklist: data unification
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Step 3: Remove Bots, Sybils, and Incentive Farmers Before You Analyze
DeFi data has a noise problem that does not exist at the same scale in Web2 analytics. Incentive campaigns and airdrop programs attract wallets that complete the minimum required actions to qualify for rewards and then disappear. Sybil clusters run dozens of wallets through identical flows from the same source. Bots interact with smart contracts at volumes no genuine user could produce.
The scale of this problem is documented.
When LayerZero distributed its 2024 airdrop, it removed over 800,000 wallet addresses as suspected sybils before distributing tokens, representing 59% of all eligible claimants.
The Linea airdrop filtered approximately 517,000 of 1.3 million eligible addresses for the same reason.
A 2025 analysis found that bots and fake wallets skew distributions in 85% of airdrop campaigns (coinlaw.io, Token Airdrop Statistics 2025).
If these wallets appear in your funnel analysis, your activation rate looks higher than it is. If they show up in your retention cohorts, Day-30 looks better than reality. If they inflate your top users view, you are optimizing for noise that will not produce protocol growth.
The most reliable signal for identifying genuine DeFi users is their onchain history outside your protocol. A wallet with:
years of activity across multiple protocols,
real capital positions,
transaction patterns that predate your incentive campaign
is a genuine user. A wallet created the week your campaign launched, with activity concentrated on your protocol and nowhere else, is a farmer.
AI can surface these patterns across your full wallet population at a scale that would take weeks manually, scoring wallets against behavioral signals and producing a filtered dataset that reflects real user behavior.
Step 3 checklist: signal cleaning
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Step 4: Run the Four Analyses That Drive DeFi Growth Decisions
With unified data and a cleaned signal, AI-powered querying can answer questions that most DeFi teams currently leave on the table. The following four analyses are the most decision-relevant for growth teams and the most underused, typically because running them manually required SQL skills and a lot of time.
Each analysis below includes a concrete scenario, copy-paste queries to run, and a checklist for what to do with the output.
Activation funnel analysis
The DeFi onboarding funnel breaks most often between wallet connect and first transaction. According to Blockchain Ads' User Acquisition Trends Report 2026, only 32% of DeFi users who connect a wallet go on to complete a first transaction. Industry data suggests roughly 70% of Web3 wallet users never complete a meaningful second transaction (coinlaw.io, 2026).
Knowing where the break happens in your specific funnel is the prerequisite for fixing it. AI makes this queryable without SQL.
Scenario: Meridian, a DeFi lending protocol on Base (hypothetical) Meridian ran a campaign with three DeFi newsletters and two ecosystem grants. Their wallet connect numbers looked strong at 4,200 in the first two weeks. But the team suspected most wallets were not transacting. They ran an activation funnel query segmented by acquisition source and found that newsletter wallets converted at 34% (wallet connect to first deposit), while wallets from the grant program converted at 9%. The grant program was optimized to produce wallet connects, not depositors. They reallocated the next grant toward a protocol with a different user profile and conversion improved. |
Copy-paste AI queries for this analysis
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What to do with the output
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Wallet cohort segmentation
Not all wallets behave the same way, and treating your user base as a single population produces averages that mask the real story. A wallet with three years of DeFi history and significant assets behaves differently from a first-time user who just bridged to Base for the first time.
Their funnel conversion, retention likelihood, and response to incentives are all different.
AI-powered wallet enrichment can segment your user base by:
onchain history
capital profile
activity recency
chain usage
without any manual classification.
The output is a view of your actual user mix:
experienced DeFi participants
First-timers
multichain users
and likely farmers, each with their own funnel and retention metrics.
Scenario: Meridian (continued) (hypothetical) Meridian ran segmentation on the same 4,200 wallets and found their user base split roughly:
The experienced segment converted at 51% and showed strong Day-30 retention. The first-timer segment converted at 18% but had better retention than expected once they transacted. This told the team their onboarding flow was failing first-timers specifically, not the product itself. |
Copy-paste AI queries for this analysis
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What to do with the output
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Retention pattern detection
Retention in DeFi is harder to define than in SaaS because there is no login, no subscription, and no single action that counts as using the product.
A wallet that deposited once and never returned looks the same in raw data as one that has returned twelve times.
Cohort-based retention analysis takes wallets that first transacted in a given week and tracks what percentage returned in Days 1 to 7, 8 to 30, and 31 to 60. AI can run this across your full user history and, more usefully, can surface which specific early behaviors are most correlated with long-term return: the onchain equivalent of finding your product's activation moment.
Scenario: Meridian (continued) (hypothetical) Meridian ran a retention analysis on all wallets that first deposited in January 2026.
But when the team filtered for wallets that had also enabled a second market position within 48 hours of their first deposit, Day-30 retention jumped to 38%. That behavior, opening a second position quickly, was the activation moment. They redesigned the post-deposit screen to prompt users toward a second market, and Day-30 retention improved by 9 percentage points over the following cohort. |
Copy-paste AI queries for this analysis
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What to do with the output
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Onchain campaign attribution
The core attribution question for DeFi growth teams is rarely answered cleanly: did this campaign produce wallets that transacted, deposited, and returned, or just wallets that connected and left?
Most teams report wallet connect volume and click-through rates. They rarely have a clear view of which campaigns drove wallets that went on to use the protocol. Onchain attribution connects the offchain touchpoint (UTM parameter, referral link, builder code) to the onchain outcome (first transaction, swap, deposit). AI makes this queryable without building a custom attribution model.
Scenario: Meridian (continued) Meridian was spending on three paid channels:
By wallet connect volume, Twitter looked best. When the team ran an attribution query tied to first deposit (not just wallet connect), the newsletter drove 3.4 times more depositing wallets per dollar spent than Twitter. They reduced Twitter spend by 60% and reallocated to two additional newsletter placements. |
Copy-paste AI queries for this analysis
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What to do with the output
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Step 5: Interpret AI Outputs Without Getting Misled
AI surfaces patterns in your data. It does not explain them. Human judgment is required at the interpretation step before any pattern becomes a growth decision.
Four failure modes to know:
Correlation without causation. If a retention analysis shows that wallets that performed a specific action on Day 1 have higher 30-day retention, that pattern may reflect selection bias: experienced DeFi users perform that action because they know what they are doing, not because the action itself drives retention.
Always ask whether the correlation could be explained by a pre-existing user characteristic before changing the product.
Aggregation hiding the real signal. Protocol-level averages mask segment behavior. A 40% wallet connect to transaction conversion rate that looks acceptable on average may be 70% for experienced DeFi wallets and 15% for first-timers. These are two different problems requiring two different interventions.
Always cut your output by segment before drawing conclusions.
Attribution gaps across chains. Multi-chain user journeys, a wallet that connects on Arbitrum but later transacts on Base, can still create attribution gaps. Cross-chain attribution has improved but is not perfect.
Treat cross-chain attribution data as directional, not definitive, until you have verified it against known data points.
Thin data producing confident outputs. Natural language querying tools can return charts and percentage figures on sample sizes of 50 wallets with the same confidence as results from 5,000.
Sanity-check any analysis result by looking at the underlying sample size before sharing it with the team or acting on it.
Step 5 checklist: before acting on any AI output
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Step 6: Connect Every Insight to a Specific Decision
An insight without a decision is a chart. The test for whether an analysis was worth running is whether it changes what the team does next.
The table below maps each of the four analyses to the growth decisions they typically inform:
Analysis | What it tells you | Decision it drives |
Activation funnel | Which step loses the most wallets and from which source | UX priority, channel reallocation |
Which user types your protocol is attracting and converting | ICP refinement, incentive design, messaging | |
Which early behaviors predict long-term return | Activation moment definition, onboarding redesign | |
Onchain campaign attribution | Which channels drive wallets that transact and stay | Budget reallocation, campaign pause or scale |
A useful test to run before any analysis: write down in one sentence what decision you would make if the result showed X, and what decision you would make if it showed the opposite. If both answers are "nothing changes," the analysis is a reporting exercise, not a growth decision.
Tools That Support This Workflow
The DeFi product analytics landscape has matured significantly since 2023. The key question when evaluating tools is whether they see both data planes (offchain and onchain) or only one.
Tool | Strengths for growth teams | Limitations to know |
Unified offchain and onchain analytics from a single SDK. Natural language querying via Ask AI. Wallet Intelligence for enrichment and segmentation. Onchain attribution across 40+ EVM chains. Activation funnel, retention cohorts, and custom dashboards. | Focused on EVM chains. Not designed for Solana-native protocols. (Updated: Formo now offers native integration for the Solana blockchain) | |
Dune Analytics | Deep custom SQL queries on raw onchain data. Large community dashboard library. Supports 100+ chains. | No offchain tracking. No AI-native querying. Requires SQL fluency. No attribution. No wallet enrichment. |
Nansen | Wallet labeling, smart money tracking, strong for market-level onchain analysis. | Not a product analytics tool. No offchain tracking or attribution. Optimized for investors, not growth teams. |
LlamaAI (DefiLlama) | Natural language querying on protocol-level TVL, revenue, and fee data. | Market and protocol-level data only. No user-level product analytics, no attribution, no wallet behavior. |
For teams earlier in the analytics journey: start with a platform that unifies offchain and onchain data before adding SQL tooling.
The ability to ask questions in plain language across both data planes is more useful at the early stage than the ability to write arbitrary onchain queries.
Stop guessing which users matter and why they leave. Formo gives DeFi growth teams unified onchain and offchain visibility with natural language querying across their full product dataset. No SQL required. Setup takes less than a day on any EVM chain. What you get with Formo:
Book a free Formo demo: formo.so |
Frequently Asked Questions
What is AI-powered DeFi analytics?
AI-powered DeFi analytics is the use of AI tools, typically natural language querying, machine learning-based segmentation, and anomaly detection, to analyze product data from DeFi protocols and applications. The focus is on understanding user behavior: who is using the protocol, where they drop off, how long they stay, and which acquisition channels produce wallets that transact. This is distinct from DeFAI, which refers to AI agents that automate trading and yield optimization.
What data do I need before AI can help me analyze my DeFi product?
At minimum, you need offchain event data (page views, wallet connects, referral sources) and onchain transaction data unified under a shared wallet identifier. Without this unification, AI can analyze each data plane separately but cannot connect acquisition to activation or activation to retention. Most crypto-native analytics platforms handle this at the SDK level.
How is AI-powered DeFi analytics different from using Dune Analytics?
Dune Analytics is a powerful SQL query tool for onchain data. It cannot track offchain behavior (referral sources, page events, UTMs), attribute onchain transactions to marketing campaigns, or provide natural language querying. Dune is best for teams with SQL fluency who need deep custom analysis of onchain data. AI-powered product analytics tools are more practical for growth teams that need both data planes unified and queryable without writing code.
How do I identify bots and sybil wallets in my analytics data?
The most reliable signal is onchain wallet history outside your protocol. Genuine DeFi users have transaction history across multiple protocols that predates your campaign, hold real assets, and show usage patterns not concentrated around incentive events. Wallets created recently with activity focused narrowly on your protocol are likely farmers or sybils. AI can score wallets against these signals across your full user population, producing a cleaned dataset for analysis.
What are the limits of AI analysis for DeFi product data?
AI surfaces patterns in your data but does not explain them. Correlation in an AI output requires human interpretation before it becomes a growth decision. Attribution across chains is directional, not definitive. Natural language querying tools can produce confident-looking outputs on small sample sizes. And any analysis is only as reliable as the underlying data: if bots and farmers are not filtered before analysis, the insights will reflect noise, not genuine user behavior.
How long does it take to set up AI-powered analytics for a DeFi protocol?
With a platform like Formo, setup is typically same-day: install the SDK, configure the events you want to track, and data collection begins immediately. The AI querying layer is available from the start. Building a comparable custom pipeline from scratch, including a blockchain indexer, offchain event tracking, a data warehouse, and a query interface, takes weeks to months depending on engineering capacity.
