Every wallet action tells a story; each onchain step leaves behind data that reveals adoption, retention, and monetization patterns. But with so much jargon and fragmented metrics, it’s easy to feel lost. That’s why we created this comprehensive Web3 analytics glossary. Inside, you’ll find over 40 must-know terms that every founder, product manager, and growth marketer should master. From funnel drop-offs to wallet intelligence, this guide is your go-to reference for speaking the language of Web3 growth—and turning raw data into actionable insights.
Key Takeaways
Meaningful growth in Web3 is tied to wallets, transactions, and onchain behavior.
Every onchain transaction is data that reveals adoption, retention, and monetization.
Onchain data is verifiable but raw and scattered. Teams need the right tools to unify and understand it.
Funnels, cohorts, and wallet intelligence transform static numbers into strategies.
Mastering these 40+ terms equips teams to make data-driven growth decisions with confidence.
Why Web3 Analytics Metrics Matter
Unlike Web2, Web3 doesn’t run on cookies and emails — it runs on wallets and transactions. That changes how we measure growth:
Onchain-first: Metrics are tied to wallet actions, not logins.
Transparency and accuracy: Onchain data is verifiable, but can be fragmented without the right tools.
Product-led growth: Success comes from users not just signing up, but actively using features and interacting onchain.
This glossary is designed to give you a solid foundation in both classic growth metrics and Web3-native analytics.
The Essential Web3 Analytics Glossary
A
Abandonment Rate: % of users who start but don’t finish a flow (e.g., wallet connect → fails). High rates signal friction or trust issues.
A/B Testing: Running experiments to compare flows. In Web3: test onboarding with vs. without gas fee subsidies.
Activation Rate: % of wallets hitting their “aha moment” (first swap, mint, or stake). Core for retention
Active User Growth: Growth in DAU/WAU/MAU wallets. Shows the adoption curve.
Adoption Rate: % of users engaging with a new feature (e.g., contract call volume after feature launch).
APIs (Application Programming Interfaces): Gateways to onchain/offchain data, like ERC-20 transfers or wallet history.
App Metrics: KPIs like wallet connects, transaction completion rate, and active smart contracts.
Attribution Modeling: Finding which touchpoints (campaign, channel, referral) drove an onchain action.
Audience Segmentation: Breaking users into groups (e.g., whales vs. minnows, NFT flippers vs. holders).
Average Revenue Per User (ARPU): Revenue ÷ active wallets. Core monetization metric.
B
Behavioral Analytics: Tracking what wallets do — stake, mint, churn.
Behavioral Cohorting: Grouping by actions (e.g., wallets that bridged vs. didn’t). Shows retention patterns.
Benchmarking: Comparing metrics to category leaders (retention vs. top dApps).
Bounce Rate: % who visit but don’t engage (e.g., land on app but never connect wallet).
Button Click Tracking: Micro-metric. Example: CTR on “Stake Now.” Often, the first sign of intent.
C
Churn Rate: % of active wallets that drop out over a period. High churn = retention problem.
Conversion Rate: % of wallets completing a desired action (connect → swap, mint → stake).
Customer Acquisition Cost (CAC): Spend ÷ new active wallet. Harder in pseudonymous Web3.
Customer Lifetime Value (CLV or LTV): Projected revenue per wallet over its lifecycle.
D
Drop-off Rate: % exiting at a funnel step (e.g., approve contract → no transaction).
Decentralized Identity (DID): User ID beyond wallet addresses. Useful for retention analysis.
E
Engagement Rate: % of active wallets performing meaningful actions (swaps, mints, trades).
Event Tracking: Logging specific onchain/offchain actions like “ApproveToken” or “ClaimReward.”
F
Funnel Analysis: Visualizing the steps wallets take toward conversion. Shows where friction lives.
Feature Usage Rate: % of active wallets trying a new feature within X days of launch.
G
Growth Rate: % increase in active wallets over time. Tied to viral loops and adoption.
H
Holders Distribution: Breakdown of token holders by wallet size (e.g., whales vs. minnows). Indicates decentralization and risk of concentration.
Hypothesis Testing: Statistical testing to validate growth ideas (e.g., “subsidized gas fees will improve conversion”).
I
Inflow/Outflow Tracking: Monitoring funds entering or leaving your dApp, pool, or token ecosystem. Vital for liquidity health.
Intent Signals: Early actions showing future conversion (e.g., wallet approves a contract but hasn’t executed a swap).
J
Journey Mapping: Visualizing the sequence of wallet actions across funnels (e.g., connect → approve → swap → stake).
M
Median Transaction Value: Middle point of transaction sizes, often more accurate than averages for skewed distributions.
N
Net Promoter Score (NPS): Wallet-user loyalty metric. In Web3, often measured via surveys tied to wallet actions.
Network Effects: Growth accelerants created when each new wallet increases value for existing users.
O
Onboarding Completion Rate: % of wallets completing setup (connect wallet → sign message → first transaction).
P
Path Analysis: Mapping the most common sequences of wallet actions to uncover friction points.
Protocol Usage Share: % of overall market activity happening on your protocol vs. competitors.
R
Retention Rate: % of wallets returning after X days/weeks. A sticky-product benchmark.
Revenue Attribution: Linking protocol fees or swaps to specific campaigns or referrals.
S
Session Length: Average duration of an active wallet session. Useful for UX benchmarking.
Stickiness: Ratio of DAU/MAU. Shows how habit-forming your dApp is.
T
Total Value Locked (TVL): The sum of assets deposited in your protocol. Core DeFi health signal.
Transaction Success Rate: % of initiated transactions that succeed (failures = gas waste, user churn).
U
User Cohorts: Grouping wallets by onboarding date or behavior to compare retention/adoption.
User Journey Drop-offs: Where wallets consistently exit workflows (e.g., approve but no swap).
V
Volume (Tx Volume): Total value of onchain activity in a period. Used to benchmark dApp adoption.
W
Wallet Cohorts: Segmenting wallets by traits like holdings, activity frequency, or lifecycle stage.
Z
Zero-Transaction Wallets: Wallets that connect but never transact. Indicates failed activation.
Putting It All Together: From Metrics to Growth
It’s tempting to treat metrics like a vocabulary list—something to know, not something to use. But the real power comes when you connect them into a growth stack:
Funnels → Reveal where users drop off in the journey.
Cohorts → Show which behaviors drive retention.
Revenue metrics (ARPU, AOV) → Highlight the levers that boost monetization.
Wallet intelligence → Unlock segmentation and personalization that feels native to Web3.
When combined, these metrics turn into a system of actionable insights—a growth engine you can use to experiment, iterate, and scale. By mastering these 40+ analytics terms, you’re equipping yourself with the language of growth that separates projects that fade from those that scale. And remember: metrics don’t live in spreadsheets. They live in the way you build, measure, and optimize every user journey onchain.
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Additional FAQs
1. Why do Web3 projects need a dedicated analytics glossary?
Web3 growth is fundamentally different from Web2. Instead of measuring email signups or pageviews, teams focus on wallet connections, contract interactions, and transaction success rates. A dedicated analytics glossary ensures founders, product managers, and marketers share the same terminology, making it easier to align strategies, interpret data correctly, and scale growth faster.
2. What makes Web3 metrics unique compared to Web2?
Web3 metrics capture real onchain economic behavior—such as staking, minting, swapping, or bridging—rather than surface-level clicks or impressions. This makes the data far more transparent and verifiable. However, because blockchain activity is scattered across chains, protocols, and wallets, teams need strong analytics tools to unify signals and extract actionable insights.
3. Which Web3 metrics matter most for product-led growth?
Key metrics for product-led growth in Web3 include:
Activation Rate – percentage of wallets reaching their “aha” moment (e.g., first swap or mint).
Retention Rate – how many wallets return over time, a core measure of stickiness.
DAU/MAU Ratio (Stickiness) – whether users make your dApp part of their daily/weekly routine.
ARPU (Average Revenue Per User) – revenue generated per active wallet, essential for monetization.
Together, these metrics show not just how many users connect a wallet, but whether they continue to engage and generate long-term value.
4. How do wallet cohorts improve growth strategy?
Wallet cohorts segment users based on behavior, holdings, or lifecycle stage—such as whales vs. minnows, first-time minters vs. repeat stakers, or early adopters vs. late joiners. This segmentation allows growth teams to:
Personalize onboarding flows
Design targeted campaigns
Offer tailored incentives and rewards
Identify high-value users who drive liquidity and adoption
By analyzing cohorts, teams can uncover patterns that fuel retention, engagement, and revenue growth.
5. How can teams use this glossary beyond just definitions?
This glossary isn’t just a dictionary—it’s a playbook for growth. Teams can use it to:
Spot friction: Funnel drop-off metrics highlight where users abandon the journey.
Optimize revenue: ARPU, AOV, and TVL metrics reveal the strongest monetization levers.
Boost retention: Cohort analysis shows which behaviors keep wallets active long-term.
Align teams: Shared definitions ensure everyone—from founders to marketers—makes decisions using the same data language.
When applied consistently, these metrics evolve from static numbers into a growth engine that powers experimentation, iteration, and scaling.
