In this guide
Crypto teams often report impressions, followers, Telegram members, connected wallets, transactions, and total value as separate achievements. Those numbers can all rise while the product remains unclear about which audience arrived, what they tried, whether they returned, or whether the campaign created a qualified business conversation.
A useful funnel does not require a perfect identity graph. It requires consistent campaign definitions, observable milestones, privacy-aware cohorts, and an honest boundary between correlation and attribution.
Define the valuable action and its prerequisites
Choose one outcome for the campaign: complete onboarding, use a product feature, supply liquidity, register for an event, request a partnership call, or return during a defined period. Work backward through the information, account, wallet, and transaction steps required.
Name the owner and data source for every event. “Active community member” and “activated wallet” need definitions that another team can reproduce.
Instrument off-chain and onchain events
Use campaign parameters and referrer data for landing pages, event names for meaningful product steps, community tags for controlled cohorts, and contract or indexer events for onchain actions. Keep environment, chain, contract version, wallet type, and campaign dates in the analysis.
Avoid collecting wallet addresses or linking identities unless the use is necessary, disclosed, secured, and legally reviewed. Aggregated cohort analysis is often enough to decide which message and journey work.
- Source and campaign
- Landing-page and content engagement
- Verified product milestone
- Wallet or transaction success and failure
- Seven- and thirty-day return signal
- Qualified inquiry or partner handoff
Separate attribution from useful evidence
A wallet may see a KOL post, join Telegram, read the website later, and use a different device. Do not assign false precision to the last click. Compare cohorts, timing, geography where appropriate, and behavior before and after the campaign. Label inferred relationships as inferences.
Inspect quality: unique actors, repeated behavior, contract interactions that reflect the actual product, support questions, and suspicious automation. One large sybil cluster should not become a growth case study.
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Frequently asked questions
Can we identify every wallet that came from a KOL campaign?
Usually not with certainty and without privacy tradeoffs. Use campaign links, opt-in flows, cohorts, timing, and onchain events, and clearly distinguish observed data from inference.
Which metric matters most?
Choose the metric closest to real product or business value, then track the few prerequisite steps that explain why users do or do not reach it.
How should sybil activity be reported?
Define detection rules, separate suspected automated clusters from verified users, keep raw and adjusted views, and avoid claiming precision the method cannot support.
Research
Sources & further reading
Primary references reviewed for this guide. Rules and draft standards can change; confirm the current text before acting.
