# NovaBright Commerce: AI Customer Experience Platform | DSM.promo Case Study

> **Key Result:** 24/7 — Customer Support Coverage

## Client Overview

| Field | Value |
|-------|-------|
| Client | NovaBright Commerce |
| Industry | E-Commerce |
| Location | Austin, TX |
| Size | $12M Revenue, 45 Staff |
| Timeline | 8 Weeks |
| Services | Digital Twin AI, Customer Service Automation |

## The Challenge

NovaBright Commerce was scaling rapidly but their customer service team couldn't keep pace. Support tickets were piling up, response times were growing, and CSAT scores were declining.

- Support ticket backlog growing 15% month-over-month
- Average first response time of 6 hours during business hours, no coverage nights/weekends
- CSAT score dropped to 3.2 out of 5 over the previous quarter
- Returns processing taking 5-7 business days
- Product recommendation engine was rule-based and underperforming

## The Solution

### Phase 1: Customer Journey Mapping
Mapped every customer touchpoint from browsing to post-purchase. Identified support, returns, and product discovery as the three highest-impact areas.

### Phase 2: Digital Twin Deployment
Built a Digital Twin AI trained on 50K+ support tickets, product catalog, and company policies. Deployed across chat, email, and SMS channels.

### Phase 3: Learning & Expansion
Continuously improved the AI using customer feedback loops, expanded to proactive outreach, and integrated smart product recommendations.

## Key Results

- **80%** — Tickets resolved by AI
- **24/7** — Support coverage
- **4.6★** — CSAT score (from 3.2)
- **28%** — Revenue increase from AI recs

## What They Said

> "The Digital Twin handles 80% of our customer inquiries without any human intervention. Our customers get instant answers at 2 AM on a Sunday. That's not something we could ever staff for."
> — Rachel Kim, VP Customer Experience, NovaBright Commerce

## FAQ

**Q: What percentage of tickets does the AI handle?**
A: The AI resolves 80% of customer inquiries automatically. The remaining 20% are escalated to human agents with full conversation context.

**Q: How does the Digital Twin learn?**
A: It was initially trained on 50K+ historical support tickets and the full product catalog. It continuously improves through customer feedback and agent corrections.

**Q: Does it work across all channels?**
A: Yes. The same AI handles chat, email, and SMS inquiries with consistent quality and brand voice across all channels.

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*Read the full case study at [https://dsm.promo/case-study-ecommerce](https://dsm.promo/case-study-ecommerce)*
*DSM.promo — AI-Powered Automation for Enterprise*