Workflow: Suggested Customer Replies Based on Previous Inquiries (AI-powered)

1. User Benefits

This workflow analyzes past customer messages and the replies your team wrote. Based on that, the AI generates response suggestions for new inquiries — aligned with your tone, wording, and previous resolutions.

• Consistent communication style across the whole support team
• Faster handling of repeat questions with auto-suggested replies
• AI learns from historical messages and improves over time
• Uses product and order data to pre-fill relevant information
• Reduces manual writing — support only edits or sends the suggestion

Result: Your customer support becomes faster, more consistent, and highly efficient.

2. How the Workflow Works

2.1 Import of Historical Data

The workflow loads past customer inquiries and the corresponding replies:

• Message history from marketplaces (eBay, Amazon, Kaufland, OTTO)
• Email communication
• Helpdesk tickets
• Internal support notes

AI builds a communication model that reflects your typical tone, vocabulary, and issue resolution patterns.

2.2 AI Analysis & Pattern Learning

The system identifies:

• Recurring questions and common problems
• Typical answers your team provides
• Product- and order-related correlations
• Tone of voice: formal, friendly, neutral, short/long

These patterns become the foundation for reply suggestions.

2.3 New Inquiry Processing

For every new message, the workflow:

• Understands the intent (delivery, return, product question, invoice, etc.)
• Searches similar historical messages
• Extracts the most effective previous answers
• Generates a reply suggestion matching your tone

Suggestions can include:

• Tracking status
• Return instructions
• Warranty details
• Product-specific explanations

2.4 Human Review or Full Automation

You choose how replies are delivered:

• Semi-automatic: support agent reviews and sends the suggestion
• Automatic: workflow sends the reply if confidence is high

2.5 Delivery to Customer

Responses are sent via:

• Marketplace messaging systems
• Email
• Helpdesk / CRM tools

All suggested and sent replies can be logged for QA and future training.

3. Time Savings for the User

Typical manual effort without automation:

• Reading & understanding message: 1–2 minutes
• Searching previous replies: 1–3 minutes
• Writing new answer: 1–3 minutes

Total: 3–8 minutes per message

With this workflow:

• Effort per message: 0–1 minute (approve or edit suggestion)

Typical weekly time savings:

• 50 inquiries → approx. 3–7 hours saved
• 200 inquiries → approx. 12–28 hours saved
• 500+ inquiries → 30–60+ hours saved

The workflow transforms your message history into a fully automated knowledge system — support agents no longer write repetitive replies from scratch.