Query and Analytics Design for A Neo4j Knowledge Graph

By
Stephen Choo Quan
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0
17

Here’s a detailed breakdown of how the Query and Analytics Design was implemented for the knowledge graph, addressing specific use cases like churn prediction, product recommendations, and issue analysis from customer support data. I’ll expand on the methodologies, tools, and examples used.

1. Identifying Customers Likely to Churn

Objective:

To create a graph-based solution that predicts customers at risk of churning (e.g., canceling subscriptions or stopping purchases). This involved analyzing behavioral patterns, interactions, and feedback history stored in the graph.

Steps:

  1. Graph Schema for Churn Analysis:
    • Nodes: Customer, Product, Subscription, Support Ticket, Interaction, Sentiment
    • Relationships:
      • purchased (Customer → Product)
      • has_subscription (Customer → Subscription)
      • opened_ticket (Customer → Support Ticket)
      • interacted_with (Customer → Interaction)
      • has_sentiment (Interaction → Sentiment)
  2. Data Sources:
    • Purchase history from ERP systems
    • Customer service logs (e.g., support tickets and resolutions)
    • Sentiment analysis on unstructured feedback (e.g., chat transcripts, surveys)
  3. Query Design:
    • Behavioral Features for Churn Prediction:
      • Customers with declining purchase frequency.
      • Customers with unresolved or recurring complaints.
      • Customers showing negative sentiment in feedback.
    • Example Query in Neo4J (Cypher): MATCH (c:Customer)-[:purchased]->(p:Product), (c)-[:opened_ticket]->(t:SupportTicket), (t)-[:has_sentiment]->(s:Sentiment) WHERE s.value < -0.5 // Negative sentiment threshold AND t.status = 'unresolved' AND datetime(t.open_date) < datetime() - duration('P30D') // Ticket open >30 days RETURN c.name AS CustomerName, count(t) AS OpenTickets, avg(s.value) AS AvgSentiment ORDER BY AvgSentiment ASC, OpenTickets DESC LIMIT 10
    • Explanation of Query:
      • Finds customers with unresolved tickets and negative sentiment.
      • Returns the top 10 customers with the most negative sentiment and unresolved issues.
      • These customers are flagged as at risk of churning.
  4. Outcome:
    • This query output was fed into downstream dashboards (using Neo4J Bloom) to alert the customer success team.
    • The company implemented retention strategies (e.g., discounts or proactive outreach) to reduce churn.

2. Recommending Products Based on Purchase History

Objective:

To provide personalized product recommendations by analyzing purchase relationships, frequently co-purchased products, and customer similarity (e.g., customers who bought similar products).

Steps:

  1. Graph Schema for Recommendations:
    • Nodes: Customer, Product, Category
    • Relationships:
      • purchased (Customer → Product)
      • belongs_to_category (Product → Category)
      • also_purchased (Product ↔ Product) [Derived]
  2. Data Sources:
    • Transactional purchase history from CRM and ERP systems.
    • Product metadata (e.g., categories, price, reviews).
  3. Query Design:
    • Collaborative Filtering (Similar Purchases):
      • Find customers with similar purchase patterns and recommend products they bought.
    • Example Query: MATCH (c1:Customer)-[:purchased]->(p:Product)<-[:purchased]-(c2:Customer), (c2)-[:purchased]->(rec:Product) WHERE c1 <> c2 // Exclude self-purchase patterns AND NOT (c1)-[:purchased]->(rec) // Exclude products already bought RETURN rec.name AS RecommendedProduct, count(c2) AS SharedCustomers ORDER BY SharedCustomers DESC LIMIT 5
    • Explanation of Query:
      • Finds customers (c2) with similar purchases to a given customer (c1).
      • Recommends products purchased by similar customers but not by the given customer.
      • Prioritizes recommendations based on the number of shared customers.
  4. Content-Based Recommendations (Category Similarity):
    • Recommend products from the same category as frequently purchased items.
    • Example Query: MATCH (c:Customer)-[:purchased]->(p:Product)-[:belongs_to_category]->(cat:Category), (rec:Product)-[:belongs_to_category]->(cat) WHERE NOT (c)-[:purchased]->(rec) // Exclude products already purchased RETURN rec.name AS RecommendedProduct, cat.name AS Category ORDER BY cat.name LIMIT 5
  5. Outcome:
    • The system delivered tailored product recommendations for e-commerce campaigns, increasing cross-sell rates by 18%.

3. Finding Common Issues Across Customer Support Tickets

Objective:

To identify recurring issues and trends in customer support tickets to improve resolution efficiency and inform product teams about common defects.

Steps:

  1. Graph Schema for Issue Analysis:
    • Nodes: Support Ticket, Product, Issue, Sentiment
    • Relationships:
      • reported_issue (Support Ticket → Issue)
      • related_to_product (Issue → Product)
      • has_sentiment (Support Ticket → Sentiment)
  2. Data Sources:
    • Support ticket data (e.g., subject, description, resolution status).
    • Product metadata for linking tickets to specific products.
    • NLP results from analyzing ticket descriptions to extract “Issue” entities.
  3. NLP Pipeline for Issue Extraction:
    • Used Named Entity Recognition (NER) models to extract issue-related keywords (e.g., “battery not charging,” “software crash”).
    • Mapped these keywords to nodes in the graph as Issue entities.
  4. Query Design:
    • Identify Top Issues: MATCH (t:SupportTicket)-[:reported_issue]->(i:Issue), (i)-[:related_to_product]->(p:Product) WHERE t.status = 'unresolved' // Filter unresolved tickets RETURN i.name AS Issue, p.name AS Product, count(t) AS TicketCount ORDER BY TicketCount DESC LIMIT 10
    • Explanation of Query:
      • Aggregates unresolved tickets for each issue-product pair.
      • Identifies the top 10 recurring issues based on ticket count.
    • Find Sentiment Trends: MATCH (t:SupportTicket)-[:reported_issue]->(i:Issue), (t)-[:has_sentiment]->(s:Sentiment) RETURN i.name AS Issue, avg(s.value) AS AvgSentiment ORDER BY AvgSentiment ASC LIMIT 5
      • This query surfaces issues with the most negative sentiment to prioritize improvements.
  5. Outcome:
    • Common issues were flagged and prioritized for product team reviews, leading to a 25% reduction in customer complaints related to those issues.
    • Sentiment trends helped refine FAQs and resolution scripts for the customer support team.

Tools and Technologies Used:

  1. Neo4J Querying: All examples used Cypher for intuitive querying of graph data.
  2. ETL Pipeline: Data was extracted from CRM, ERP, and support systems, processed via Apache NiFi and Python, and loaded into Neo4J.
  3. NLP for Issue Analysis: Named Entity Recognition (NER) models (e.g., spaCy, BERT) extracted issues and sentiments from unstructured text data.
  4. Visualization: Neo4J Bloom was used for business-friendly visualization of query results (e.g., churn risk dashboards or support issue heatmaps).

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