The Problem

Support teams face a critical challenge: maintaining context across customer interactions. Current solutions fall short:

Customer data scattered across multiple systems
No unified view of past interactions and resolutions
Manual correlation of tickets, orders, and refunds
Context lost between agent handoffs
Difficulty identifying patterns in customer behavior

This leads to slower resolution times, repeated questions, and frustrated customers.

How AttentionDB Solves It

AttentionDB revolutionizes support operations with intelligent memory management:

1. Unified Customer Context

AttentionDB aggregates and correlates:

Support tickets
Order history
Past resolutions
Product interactions
Payment/refund history
Agent notes

2. Intelligent Attention Scoring

Our system uses multiple attention types to rank customer interactions:

recency: Weight: 0.7
- Recent tickets prioritized
- Time-decay function
- Urgent issues boosted
co_access: Weight: 0.6
- Related ticket clusters
- Common resolution patterns
- Product associations
ownership: Weight: 0.5
- Agent assignments
- Team specializations
- Resolution success rates

3. Automated Memory Injection

When a new ticket arrives, AttentionDB:

Identifies the customer
Scores relevant historical context
Injects memory into agent view
Suggests similar past resolutions

// Example: Retrieving customer context
const customerContext = await attentionDB.getContext({
  customerId: "cust_123",
  attention: {
    recency: 0.7,
    co_access: 0.6,
    ownership: 0.5
  }
});

// Returns ranked history and suggestions

4. Integration Features

Ticket System Integration
- Automatic context injection
- Similar case suggestions
- Resolution templates
Analytics Dashboard
- Customer interaction patterns
- Resolution success rates
- Agent performance metrics
Automation Rules
- Auto-categorization
- Priority scoring
- Agent routing

Real World Impact

Case Study: E-commerce Support Team

A major online retailer implemented AttentionDB in their support workflow:

Before: 12min average context gathering time
After: 3min context retrieval, 45% faster resolution
ROI: Handled 60% more tickets with same team size

Success Metrics

75% reduction in context gathering time
45% faster ticket resolution
30% improvement in first-contact resolution
90% customer satisfaction increase

Getting Started

# Install AttentionDB Support Module
npm install @attanix/support-memory

# Initialize with your support system
npx attanix init --source=zendesk --history=90d

# Start tracking customer context
npx attanix track-support

Best Practices

Configure attention weights for your use case
Set up automated context injection
Train agents on memory utilization
Regular pattern analysis review

Integration Examples

// Zendesk Integration
import { AttentionDB } from '@attanix/support-memory';

// Initialize with your config
const attentionDB = new AttentionDB({
  source: 'zendesk',
  attention: {
    recency: 0.7,
    co_access: 0.6,
    ownership: 0.5
  }
});

// Auto-inject context on ticket view
attentionDB.on('ticket.viewed', async (ticket) => {
  const context = await attentionDB.getCustomerContext(ticket.customer);
  ticket.addSidebarData(context);
});

Support Agent Memory: Customer Context at Scale