Chatbot-to-Agent Handoff Fairness & Trust Survey
Measures perceived fairness, transparency, and trust impact when customers encounter a chatbot before reaching a human agent. Designed for post-interaction feedback to diagnose friction in chatbot routing and escalation workflows.
샘플 질문
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When was your most recent customer support interaction?
- Within the last 7 days
- 8–14 days ago
- 15–30 days ago
- 1–3 months ago
- More than 3 months ago
Which of the following occurred before you were offered a human agent? Select all that apply.
- Showed help articles or FAQs
- Walked through troubleshooting steps
- Asked me to rephrase or provide more detail
- Asked me to repeat information I already provided
- Suggested contacting later due to availability
- Promoted premium or priority support
- Presented a long form to complete
- Stated agents were unavailable
- None of the above
- Other
How easy was it to reach a human agent when you wanted one?
How satisfied were you with the final outcome of that interaction?
Rank the following factors by how important they are to you when using a chatbot for support, from most to least important.
- Speed to resolution
- Accuracy of answers
- Clear path to a human agent
- Privacy and data handling
- Low effort (few steps or inputs)
Based on your responses in this survey, please share any additional thoughts or feelings about how the chatbot handled your experience — including anything that felt fair, unfair, clear, or confusing.
Which region do you live in?
- Africa
- Asia
- Europe
- North America
- Oceania
- South America
- Prefer not to say
Thank you for your time — your feedback helps us improve the fairness and transparency of our support experience.
During that interaction, did a chatbot engage with you at any point before you reached a human agent?
- Yes, and it resolved my issue
- Yes, but I asked for a person
- Yes, and I was routed to a person automatically
- No chatbot was involved
- Not sure
Approximately how long did you interact with the chatbot before reaching a person?
- Less than 1 minute
- 1–2 minutes
- 3–5 minutes
- 6–10 minutes
- More than 10 minutes
- I did not reach a person
- Not sure
The chatbot clearly explained what it could and could not help me with.
After this experience, how has your trust in the company's support changed?
What is your age group?
- Under 18
- 18–24
- 25–34
- 35–44
- 45–54
- 55–64
- 65+
- Prefer not to say
In the last 6 months, approximately how many times have you contacted customer support?
- 1 time
- 2–3 times
- 4–5 times
- 6+ times
The chatbot clearly communicated why I was being transferred to a human agent.
How do you describe your gender?
- Woman
- Man
- Non-binary
- Prefer not to say
How comfortable are you using digital chat or messaging for support?
I understood each step the chatbot took before routing me to a person or resolving my issue.
What is the highest level of education you have completed?
- Less than high school
- High school or equivalent
- Some college or vocational training
- Bachelor's degree
- Postgraduate degree
- Prefer not to say
Which channel best describes that interaction?
- Live chat on a website
- Chat in a mobile app
- Messaging app (e.g., WhatsApp, Messenger)
- Social media direct message
- Phone
- In-store or on-site
- Other
My preference for how I wanted to get help (on my own or with a person) was respected.
What is your current employment status?
- Employed full-time
- Employed part-time
- Self-employed
- Unemployed and seeking work
- Student
- Retired
- Not seeking work
- Prefer not to say
What was the main reason for contacting support?
- Billing or payments
- Technical problem
- Account access or password
- Order status or delivery
- Product information or setup
- Cancellation or return
- Complaint or poor service
- Something else
Overall, how fair did the process of being routed to (or away from) a human agent feel?
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자동 리포트
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차별화 포인트
- Combines structured rating scales on chatbot transparency, routing clarity, and preference respect with an adaptive AI follow-up interview that probes the specific reasons behind a respondent's fairness and trust scores
- Captures the full escalation journey with dedicated questions on time-to-human-agent, pre-transfer chatbot actions, and channel type, letting teams diagnose exactly where routing friction occurs
- Uses automated per-response quality scoring and an auto-generated report to surface actionable patterns across chatbot-to-agent handoffs without manual coding of open-ended feedback
- Publishes transparent prompts for the AI follow-up so teams can audit exactly what was asked and why, unlike black-box chatbot or static form tools
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