Discovering Communities with Knowledge Graphs

Have you ever wondered why some products sell particularly well together, or why certain customer segments respond similarly to marketing campaigns? Often, these patterns exist, but they're hidden deep inside your data.
The more deeply you understand who buys your products, why they buy them, and how customers relate to each other, the better you can deliver personalized experiences and drive sales.  Traditional BI and relational database reporting can answer some of these questions, but viewing your data as a network of interconnected relationships can really open up deeper insights more specifically actionable insights.

In this post, we'll explore how retailers can uncover hidden customer communities by leveraging knowledge graphs and community detection algorithms. We'll illustrate this clearly through an in-depth example from a grocery retail scenario, without technical jargon, just clear explanations.

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What Is a Knowledge Graph?

A knowledge graph is a structured way to represent your business data as interconnected entities and relationships rather than separate tables or disconnected records. It helps you clearly visualize and explore relationships among entities, providing deep context about your customers, products, stores, and transactions.

Think of it like creating a detailed network map:

• Each entity (such as a customer, product, or store) is represented by a node.
• Each relationship (like "purchased," "visited," or "belongs to") forms a connection between nodes.

Knowledge graphs make complex data interactions easy to understand, uncovering insights that traditional data models might overlook.  There are a number of knowledge graph offerings that enable not only querying graph based data possible but also simplify the application of sophisticated graph algorithms.  Setting aside any specific technical recommendations, let’s focus on a common use case that a graph based approach enables - community detection.

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Example: Discovering Shopper Communities at a Grocery Store

Imagine you're managing a large grocery retail chain called "FreshMart". 

FreshMart has a vast assortment of products, millions of transactions, and thousands of regular customers. FreshMart already tracks sales data by persisting it in relational databases across their systems.  Standard BI dashboard reports are helpful in terms of understanding basic sales aggregation across customers, products and channels. But there’s valuable information hidden within the connections between customers and products that traditional reporting doesn’t reveal. Using knowledge graphs, FreshMart can clearly visualize these connections and discover hidden customer communities based on purchasing habits.

Here's exactly how this would work step by step:

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Step 1: Building the Knowledge Graph from Retail Data

Modeling data as a graph is an important first step, and it also happens to be a very intuitive way to think about the nouns and verbs of a business.

FreshMart starts by identifying and defining key entities (nouns) clearly:

• Customers: Represented by loyalty card numbers or transaction IDs.
• Products: Grocery items, such as organic vegetables, dairy, snacks, beverages.
• Stores: Physical or online locations where transactions happen.

The relationships (verbs) between entities include:

• Customer buys Product
• Customer shops at Store

Product belongs to Product Category (e.g., organic produce, dairy-free, gluten-free, snacks)

By implementing this model in their graph DB of choice, FreshMart is now able to view these relationships as a highly interconnected graph of customers, products, stores, and transactions.

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Step 2: Creating Connections Between Customers

Next, FreshMart is able to use common graph queries to enrich its knowledge graph by establishing customer-to-customer relationships based on shared behaviors and preferences. For example, two customers become connected if they show similar buying habits, such as frequently purchasing products from the same categories, brands, or at similar times or store locations.

Consider the following scenario:

• Customer Anna: Often buys organic produce, gluten-free pasta, almond milk, vegan snacks, and plant-based proteins.

Customer Brian: Regularly buys organic produce, almond milk, vegan snacks, and gluten-free bread.

Although Anna and Brian may not know each other, the knowledge graph identifies that they share several shopping habits. This overlap indicates they belong to a similar shopper segment.

FreshMart can clearly apply logic to define meaningful customer connections. For instance, inclusion in a shopper segment might be established only if two customers share at least three product categories or regularly purchase similar items at similar intervals.

These rules ensure that connections aren't based on random chance but represent true similarities in purchasing behavior.

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Step 3: Detecting Communities in the Knowledge Graph

Once customer-to-customer connections are established, FreshMart can apply community detection algorithms like Louvain to discover groups of closely related customers. The capability to run these types of algorithms is present in most commercial graph offerings, as well as many open source options for those so inclined.

Community detection algorithms systematically analyze the strength of connections throughout the graph. The algorithm identifies groups where connections within a group are strong and dense compared to connections between different groups.

By applying the algorithm, FreshMart might identify communities such as:

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Step 4: Using These Communities to Drive Actionable Insights

Having discovered these customer communities, FreshMart can now leverage these insights to enhance its business:

• Personalized Marketing: FreshMart can design marketing campaigns specifically for each community. Instead of generic promotions, FreshMart tailors messages to each community's preferences, significantly increasing customer engagement.
• Product Recommendations: Using community insights, FreshMart recommends relevant products, ensuring customers receive offers aligned with their interests and increasing basket size.
• Assortment and Supply Chain Optimization: A better understanding of relevant products allows FreshMart to improve their assortment and better anticipate product demand, ensuring shelves are stocked appropriately for each community's preferences, reducing waste and improving sales efficiency.
• Influencer Identification: Various graph algorithms exist to help FreshMart identify influential shoppers within each community (e.g. PageRank or Eigenvector Centrality)—customers whose buying habits strongly influence peers. Engaging these influencers can amplify marketing effectiveness.

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Technologies Supporting Knowledge Graphs and Community Detection

FreshMart’s knowledge graph can be built and managed easily using platforms like Snowflake, which support graph-based analytics within their existing data infrastructure (see RelationalAI). 

At Syntaxia, we're technology-agnostic, but we recognize some tools handle knowledge graphs and community detection more effectively than others. You can check our breakdown of the most prominent technologies in this space in our post The Top Graph Database Companies to Watch.

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What's Next Beyond Community Detection?

Community detection is just the start. Once the knowledge graph is in place, FreshMart can extend its analytics to achieve:

• Trend Forecasting: Spot emerging trends in shopper behavior, enabling faster response to market changes.
• Anomaly Detection: Identify unusual purchasing patterns or fraudulent transactions quickly.
• Cross-Selling and Upselling: Determine which products naturally fit together, optimizing product placement and promotions.

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Putting It All Together

By applying knowledge graphs and community detection, FreshMart transforms simple transactional data into deep insights about customer communities. This enables highly targeted marketing, optimized operations, and ultimately a stronger competitive advantage in retail.

At Syntaxia, our mission is turning complexity into clarity. We help companies implement powerful solutions like knowledge graphs to unlock the true potential of their data.

Ready to reveal the hidden communities in your customer data? Let’s talk.