Customer 360: The Holistic Unified View

In most companies, customer data is scattered. The CRM knows their name. The Support tool knows their complaints. The Website knows their clicks. This is the "Silo Problem." To provide truly intelligent support or sales, an AI needs to see the "Whole Person." Graph RAG is the only technology that can merge these silos into a single, high-dimensional Customer 360 Graph.

In this lesson, we will look at how to build a Customer Intelligence Graph. We will learn how to link a "Technical Issue" node to a "Product Usage" node and use an AI agent to answer questions like: "Is this customer complaining because of a software bug, or because they haven't been trained on the feature they bought?"

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1. The Customer 360 Schema

• (:Customer) {LTV, risk_score}
• (:Contact_Point) {Email, Phone, Slack_ID}
• (:Interaction) {Date, Sentiment, Channel}
• (:Product_Metric) {Last_Login, Feature_Usage}

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2. Linking the SILOs

The power of the graph is the Shared Identifier.

• Sync Rule: If a Salesforce record and a Zendesk ticket share the same email_hash (Module 15), they are linked to the same (:Customer) node.

Suddenly, the AI doesn't just see a "Ticket." it sees: "A ticket from a High-LTV customer who hasn't logged in for 3 weeks and recently complained on LinkedIn." This context allows for Proactive Retention reasoning.

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3. Sentiment Topology

In a standard RAG, you search for "Angry" keywords. In a Graph RAG, you look for Sentiment Trajectories.

• Path: (Customer)--[:POSTED]-->(Complaint_1)--[:FOLLOWED_BY]-->(Complaint_2).
• Reasoning: "The customer's anger is escalating across three different interactions. We need to trigger an 'Immediate Management Call'."

graph LR
    C[Customer: Sudeep] -->|Ticket| T[Bug #902]
    C -->|Login| L[Last: 2024]
    C -->|LinkedIn| P[Post: Frustrated]
    T ---|Related| F[Feature: Autopay]
    
    style C fill:#4285F4,color:#fff
    style P fill:#f44336,color:#fff
    note[The AI sees the connection between the sentiment and the usage]

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4. Implementation: Finding Churn Risks in Cypher

MATCH (c:Customer)-[:HAD_INTERACTION]->(i:Interaction)
WHERE i.sentiment = 'Negative'
WITH c, count(i) as trouble_count
MATCH (c)-[:PURCHASED]->(p:Product)
WHERE c.last_active < datetime() - duration('P30D')
RETURN c.name, p.name, trouble_count;

// This query finds high-value customers who are 'Negative' 
// AND 'Inactive'—the ultimate churn signal.

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5. Summary and Exercises

Customer 360 turns "Data points" into a "Human Story."

• Entity Unification breaks the silos between departments.
• Interaction Paths reveal the "Reason" behind user behavior.
• Intent Extraction from chat/email allows for proactive service.
• Risk Scoring (Module 11) helps prioritize who to save first.

Exercises

1. Schema Task: You have "Web Traffic" and "Invoices." What node would you use to link them? (Hint: The 'User Account' or 'IP Address').
2. The "Silent" Churn: How would you write a query to find a customer who has No interactions in the last 6 months?
3. Visualization: Draw a "Customer" node in the center. Surround it with 5 different icons representing 5 different company departments (Sales, Support, Product, Finance, Marketing).

In the next lesson, we will look at technical fields: Legal Intelligence: Case Law Connections.