Review Management Agent

🤖 Explicar con IA

The Review Management Agent is an automated reputation management tool designed to maintain a positive brand image across review platforms. It acts as a first line of defense, ensuring that happy customers receive immediate appreciation while critical feedback is instantly routed to human support teams for careful handling, preventing PR issues from escalating.

Capabilities & Features¶

The Review Management Agent is optimized for the following reputation tasks:

• Sentiment Classification: Instantly categorizes incoming reviews as positive, neutral, or negative based on star rating and semantic analysis.
• Automated Gratitude: Posts polite, brand-aligned responses to positive reviews (e.g., 4 or 5 stars) to boost engagement and customer loyalty without human intervention.
• Risk Escalation: Identifies negative reviews or specific complaints and flags them immediately for a human support representative.
• Draft Assistance: Pre-generates suggested apology or resolution drafts for negative reviews to speed up the human agent’s workflow.

Integration Guide¶

Unlike standard API-integrated tools, the Review Management Agent is deployed and managed directly through 4Geeks’ support and engineering teams.

1. Request Access: Contact your assigned 4Geeks account manager to request the Review Management Agent.
2. Configuration: A 4Geeks staff member will work with you to define approval workflows, response templates (for positive reviews), and escalation paths for negative feedback.
3. Deployment: The agent is connected to your review aggregators (e.g., Google Business, Trustpilot, Yelp) by 4Geeks technicians.

Workflow Scenarios & Token Usage¶

The following scenarios illustrate how the agent interacts with users and estimates the associated costs based on the 4Geeks token model.

Scenario 1: Auto-Response to Positive Review¶

A satisfied customer leaves a 5-star review on Google Maps praising the fast service. The agent detects the high rating and positive sentiment, then posts a thank-you note.

sequenceDiagram
  autonumber
  actor Customer as Happy Customer
  participant Platform as Review Site
  participant Agent as Review Agent

  Customer->>Platform: Posts 5-Star Review
  Note right of Customer: Input: ~40 words

  Platform->>Agent: New Review Trigger

  Agent->>Agent: Analyze Sentiment (Positive)
  Agent->>Agent: Select Tone & Generate Reply

  Agent-->>Platform: Posts Public Response
  Note left of Agent: Output: ~30 words

Cost Estimation¶

• Input Data: Customer name and review text (~40 words).
• Output Data: Personalized thank you message (~30 words).
• Total Volume: ~70 words.
• Token Calculation: 70 words ÷ 0.75 words/token = ~93 tokens.
• Estimated Cost: ~90 - 100 Tokens

Scenario 2: Negative Review Escalation¶

A customer leaves a detailed 1-star complaint regarding a billing error. The agent detects the negative sentiment, blocks automated replying, and creates a support ticket for the human team.

sequenceDiagram
  autonumber
  actor Customer as Upset Customer
  participant Agent as Review Agent
  participant Support as Support Ticket System

  Customer->>Agent: Posts 1-Star Review
  Note right of Customer: Input: ~150 words

  Agent->>Agent: Analyze Sentiment (Negative)
  Agent->>Agent: Extract Order ID/Issue Details

  Agent-->>Support: Create High-Priority Ticket
  Note left of Agent: Output: ~100 words

Cost Estimation¶

• Input Data: - Full review text containing the complaint (~150 words).
• Output Data:
  • Internal Ticket Summary (Key issues extracted).
  • Suggested Draft Response (for human review).
  • Output Total: ~100 words.
• Total Volume: ~250 words.
• Token Calculation: 250 words ÷ 0.75 words/token = ~333 tokens.
• Estimated Cost: ~330 - 350 Tokens

• Aún con dudas? Pregunte en la comunidad.
• Consulte el changelog.