Business-Driven Analytics: How Clusters and Graphs Help in Smarter Marketing and Fair Airdrops

1. Understanding Cluster and Graph Analysis

Cluster analysis and graph analysis are two of the most powerful tools in modern data processing (especially in crypto) — and two of the most commonly confused. While they are often used together, each serves a distinct role in understanding complex systems, behaviors, and relationships.

Cluster analysis is the process of grouping objects — like users, wallets, or transactions — based on similar characteristics. It does not analyze relationships between entities; instead, it focuses on identifying patterns in behavior or structure. For example, users who interact with the same types of DeFi protocols at similar times or with similar frequency might be grouped into one cluster.

There are several types of cluster analysis:

• Hierarchical clustering organizes data into a tree of nested clusters without needing a predefined number of groups — useful when you want to understand natural structure.
• K-means clustering partitions data into a set number of clusters by minimizing the variance within each group — efficient for quick segmentation.
• Density-based clustering identifies groups of high data density, which is particularly good at finding irregular or scattered groupings (e.g., niche wallet behaviors in onchain activity).

Graph analysis, on the other hand, is all about relationships. Instead of looking for similarity, it examines how different entities are connected — like who interacts with whom, who transacts with whom, or which wallets are funded by the same source. In a graph, nodes represent the entities (wallets, users, etc.), and edges represent the relationships between them (transactions, common contracts, referrals).

Graph analysis powers:

• Social network analysis (who’s interacting within a community),
• Link analysis (how different wallets or accounts may be related),
• Routing/path algorithms (optimizing flows through a network — in logistics, finance, or data structures).

While cluster analysis answers “Who behaves similarly?”, graph analysis answers “Who is connected to whom, and how?”

2. Applying Cluster and Graph Analysis in Practice

These methods become especially useful when applied together. In blockchain and marketing analytics, clusters can show patterns of behavior, while graphs show structural or transactional relationships.

Let’s say you’re running a Web3 campaign. Cluster analysis can tell you: “Here are the groups of users who behaved similarly — e.g., all interacted with your product within 24 hours, or used the same tool across different chains.” That gives you segments to work with.

But once you apply graph analysis, you gain a second dimension: “Are these users related? Do they share a funder, or are they part of a transaction loop? Are they just acting similarly — or acting together?” This distinction is critical when identifying real communities vs. coordinated or artificial behavior.

At Nomis, we apply this combined approach. We first use cluster analysis, based on what we call the Nomis action hash — a behavior fingerprint across wallets. We use it to group addresses based on similar onchain actions. Then, we construct graphs within each cluster, drawing the transaction links between wallets and clusters to build a full picture. This way, we can explore whether a cluster is just random behavior — or whether it reflects real coordination, synergy, or, in some cases, Sybil structures.

But most importantly: clusters don’t equal fraud. Clustering shows similarity, not intent. That’s why graph analysis is essential to dive deeper — to contextualize, compare, and interpret the actual structure of the activity.

3. How Cluster and Graph Analysis Help In Improving ROI

For both traditional businesses and crypto-native projects, the goal is the same: spend resources wisely, target the right users, and generate sustainable returns. But without understanding how users behave and how they’re connected, campaign strategies often rely on guesswork.

This is where cluster and graph analytics unlock the map.

With cluster analysis, projects can segment users based on their actual behavior — not vague demographic guesses or surface-level metrics. You might discover that a small but highly active group of wallets consistently participates in governance, liquidity mining, or cross-chain bridging. This insight lets you tailor offers, messages, and campaigns specifically to high-value behavior patterns — not just wallet age or transaction count.

Then, graph analysis takes this a step further. Maybe two high-performing clusters appear separate, but graphing them reveals a strong transactional connection — suggesting a community or referral loop you didn’t see before. Or maybe graph analysis shows that certain clusters are feeding rewards into a central wallet, helping you detect reward farming setups or Sybil networks.

This dual lens helps:

• Optimize campaign targeting,
• Cut wasted budget, and
• Spot patterns before they become problems.

We suggest to make process simple: don’t just track what users do — track how they relate to each other, and how those relationships affect value creation across a blockchain/project.

4. Fair Airdrops: Identify Fraud by Using Cluster Analytics

Airdrops, loyalty programs, and incentive systems are powerful — but they’re also vulnerable. One of the most difficult challenges for any crypto project is ensuring that rewards go to real contributors, not to those gaming the system.

The most common problem? Sybil behavior — users creating multiple wallets to farm airdrop rewards. This inflates numbers, drains budgets, and damages the trust of your real community.

Some projects try to fight this with aggressive gatekeeping — KYC, multi-step verifications, or extremely tight filters. But those often backfire, pushing away real users while letting sophisticated farmers slip through.

Cluster and graph analysis offer a smarter path.

By clustering wallets based on behavior, and analyzing how they are connected through transactions or shared patterns, it becomes possible to detect suspicious behavior without making assumptions about identity.

And at Nomis, we take a neutral approach:

“One wallet = one user” is a foundational crypto principle, and we respect it. We don’t accuse — we analyze.

We identify unusual overlaps, link structures, or loops that suggest coordination — so you can adjust reward mechanics, define smarter eligibility rules, or simply understand how your campaign is being used (or misused).

Used right, cluster and graph analytics ensure fairer airdrops, smarter incentives, and better user alignment — helping projects focus their resources where it truly matters.