On-chain Marketing Analytics¶

Key Takeaways
- Four-step pipeline from wallet ingestion to 1:1 personalised activation: ingest, score behaviour, segment, then generate per-wallet LLM messaging
- Same 23M+ wallet dataset powers both Fraud/Risk products and Growth products - one integration, two buyer profiles
- Intent scoring classifies wallets by product intent (lending, yield-seeking, trading, exploratory) from 2+ years of on-chain pair history
- CB Insights AI Fraud Prevention Market Map; AWS Global Fintech Accelerator; $250K Google Cloud award

The activation pipeline: turn wallet behavioral signals into personalized growth campaigns at scale - intent scoring, churn detection, and 1:1 LLM messaging per wallet.

For the analytics dashboard - seeing who is connecting, segment distribution, and cohort metrics - see Web3 User Analytics.

23M+
Wallet Personas

8
Chains Covered

2yr+
Pair History Depth

1:1
LLM Personalization per Wallet

The Pipeline¶

Step 01 - Wallet Ingestion
Connect a wallet list, contract address book, or live on-chain event feed from your protocol.

Step 02 - Behavioral Scoring
Each wallet is scored for DeFi experience, intent (borrow / yield / trade), and risk tolerance.

Step 03 - Segmentation
Wallets cluster into actionable growth segments - churn risk, win-back, high-value, exploratory.

Step 04 - Personalized Activation
Growth Agents generate 1:1 messaging calibrated to each wallet's sophistication and intent.

Core Capabilities¶

Intent Scoring¶

Classifies wallets by product intent - lending, yield-seeking, trading, or exploratory - so growth teams target the right CTA to the right user. Intent is derived from on-chain transaction patterns across 2yr+ of pair history, not from self-reported data or page clicks.

Experience Grading¶

Distinguishes DeFi-native power users from newcomers, calibrating messaging sophistication automatically. A Power Trader who sees a beginner tutorial is lost. A newcomer who sees a fee-tier comparison is overwhelmed. Experience grading removes both failure modes.

Risk Profiling¶

Surfaces conservative vs. aggressive risk appetite across a user base, informing both messaging and product positioning. High risk willingness and capability unlocks high-yield framing; conservative wallets see stability-first messaging.

Churn and Win-back Detection¶

Identifies dormant, at-risk, and reactivation-ready wallets that standard on-chain monitoring misses. 70% of DeFi users never return after their first transaction - churn detection surfaces which of those are worth a win-back campaign and what message will work for each cohort.

One Dataset, Two Product Lines¶

On-chain Marketing Analytics runs on the same behavioral intelligence platform as ChainAware's Fraud and Risk products. One dataset, two buyer profiles:

	Fraud and Risk Line	Growth Line
Buyer	Security / Compliance	Growth / Product / Marketing
Core output	Rug pull and fraud probability, Agent Trust Score	Intent, experience, and risk segmentation
Underlying dataset	Same 23M+ wallet persona graph, same 8-chain coverage, same 2yr+ history
Benchmarked against	Chainalysis, Elliptic, TRM Labs	Traditional Web3 growth / CRM tooling

Both product lines compound in value as the dataset grows - every new wallet scored and every prediction validated against real outcomes makes the model more accurate for both buyers.

External Validation¶

Validator	Recognition
CB Insights	Placed on the AI Fraud Prevention Market Map
BNB Chain Kickstart	Validated marketing services use case
AWS and Google Cloud	Global Fintech Accelerator and $250K Cloud award

Frequently Asked Questions¶

What is on-chain marketing analytics and how is it different from web analytics?
On-chain marketing analytics derives user intent, experience, and risk tolerance from blockchain transaction history - not page clicks, cookies, or self-reported data. Web analytics tells you what users did on your site after they arrived. On-chain analytics tells you who they are and what they are likely to do before they click anything - enabling personalisation at the point of wallet connect, not after sessions accumulate.

What does intent scoring classify?
Intent scoring classifies each wallet by its primary DeFi product intent: lending/borrowing, yield-seeking, trading, or exploratory browsing. Intent is derived from 2+ years of on-chain transaction pair history across 8 blockchains - not inferred from a single session. A wallet with 18 months of Aave interactions is reliably lending-intent regardless of what page they land on.

How does churn detection work for DeFi wallets?
ChainAware identifies dormant and at-risk wallets by scoring inactivity patterns against the full behavioural baseline. 70% of DeFi users never return after their first transaction. Churn detection surfaces which dormant wallets share behavioural characteristics with wallets that previously re-engaged - making win-back campaigns targeted rather than broadcast.

Does the growth product share data with the fraud product?
Both product lines run on the same 23M+ wallet persona graph with the same 8-chain coverage. The fraud product surfaces negative signals (rug pull history, AML flags, fraud probability); the growth product surfaces positive signals (intent, experience, risk appetite). One integration feeds both, which is why every Growth Agent automatically runs a Fraud Tech check internally before generating any recommendation.