Satisfaction Survey Agent

🤖 Explain with AI

The Satisfaction Survey Agent is a voice-based feedback tool designed to capture immediate, high-fidelity customer sentiment after a service interaction. By conducting automated calls shortly after an appointment or support ticket resolution, it gathers richer qualitative data than standard email surveys, allowing businesses to identify improvement areas and measure Net Promoter Score (NPS) accurately.

Capabilities & Features¶

The Satisfaction Survey Agent is optimized for the following feedback tasks:

• Automated Outbound Polling: Triggers a call immediately after a service event (e.g., delivery completion, tech support call) to capture fresh impressions.
• Quantitative Scoring: Asks customers to rate their experience on a numerical scale (1-5 or 1-10) using keypad input or voice recognition.
• Qualitative Data Collection: Prompts users for open-ended feedback (e.g., “What could we have done better?”) and records the spoken response for transcription and sentiment analysis.
• Negative Feedback Alerting: Detects low scores or negative keywords in real-time and flags the interaction for immediate manager review.

Integration Guide¶

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

1. Request Access: Contact your assigned 4Geeks account manager to request the Satisfaction Survey Agent.
2. Configuration: A 4Geeks staff member will work with you to design the question flow, record the voice prompts, and define the triggers (e.g., “Call 10 minutes after Ticket Closed status”).
3. Deployment: The agent is connected to your CRM or Service Desk software 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: Quick NPS Check¶

A customer just finished a call with a support representative. The agent calls back to ask a single question: “On a scale of 0 to 10, how likely are you to recommend us?”

sequenceDiagram
  autonumber
  actor Customer as User
  participant Agent as Voice Agent
  participant DB as Analytics DB

  Agent->>Customer: "Hi, quick question about your recent call..."
  Note right of Agent: Output: ~10 seconds

  Agent->>Customer: "How likely to recommend us (0-10)?"

  Customer->>Agent: "9" (Voice or Keypad)
  Note right of Customer: Input: ~3 seconds

  Agent->>DB: Log Score: 9 (Promoter)
  Agent->>Customer: "Thanks! Goodbye."

Cost Estimation¶

• Voice Interaction: ~20 seconds total duration.
• Token Calculation: 20 seconds ÷ 0.06 seconds/token = ~333 tokens.
• Estimated Cost: ~300 - 350 Tokens

Scenario 2: Detailed Feedback (Low Score)¶

A customer rates a delivery experience as “2 out of 5”. The agent dynamically asks a follow-up question to understand the reason.

sequenceDiagram
  autonumber
  actor Customer as User
  participant Agent as Voice Agent
  participant Manager as Support Manager

  Agent->>Customer: "Rate your delivery (1-5)."
  Customer->>Agent: "2"

  Agent->>Agent: Detect Low Score -> Trigger Branch B

  Agent->>Customer: "Sorry to hear that. Please tell us why."
  Customer->>Agent: "The driver was rude and late."
  Note right of Customer: Input: ~10 seconds (Recording)

  Agent-->>Manager: Send Alert + Transcript
  Agent->>Customer: "Thank you, a manager will review this."

Cost Estimation¶

• Voice Interaction: ~45 seconds total duration (Question + Open-ended Record).
• Token Calculation: 45 seconds ÷ 0.06 seconds/token = ~750 tokens.
• Estimated Cost: ~700 - 800 Tokens

• Login or signup into Console.
• Check out the changelog.
• Contact our enterprise support team.