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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.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

25 questions · ~11 min
Q01
Message

Welcome! This survey asks about your recent experience with a support chatbot, specifically how fair and transparent the process felt when you needed help. Your participation is voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest opinions. All responses are confidential and will be reported in aggregate only. This survey takes approximately 6–8 minutes to complete. Thank you for your time.

Q02
Dropdown

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
Q03
Multiple Choice

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
Q04
Opinion Scale

How easy was it to reach a human agent when you wanted one?

Scale: 1 – 7
Min:Very difficultMax:Very easy
Q05
Opinion Scale

How satisfied were you with the final outcome of that interaction?

Scale: 1 – 7
Min:Very dissatisfiedMax:Very satisfied
Q06
Ranking

Rank the following factors by how important they are to you when using a chatbot for support, from most to least important.

  1. Speed to resolution
  2. Accuracy of answers
  3. Clear path to a human agent
  4. Privacy and data handling
  5. Low effort (few steps or inputs)
Drag to rank
Q07
AI Interview

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.

Q08
Dropdown

Which region do you live in?

  • Africa
  • Asia
  • Europe
  • North America
  • Oceania
  • South America
  • Prefer not to say
Q09
Message

Thank you for your time — your feedback helps us improve the fairness and transparency of our support experience.

Q10
Multiple Choice

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
Q11
Dropdown

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
Q12
Opinion Scale

The chatbot clearly explained what it could and could not help me with.

Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q13
Opinion Scale

After this experience, how has your trust in the company's support changed?

Scale: 1 – 7
Min:Decreased a great dealMax:Increased a great deal
Q14
Dropdown

What is your age group?

  • Under 18
  • 18–24
  • 25–34
  • 35–44
  • 45–54
  • 55–64
  • 65+
  • Prefer not to say
Q15
Dropdown

In the last 6 months, approximately how many times have you contacted customer support?

  • 1 time
  • 2–3 times
  • 4–5 times
  • 6+ times
Q16
Opinion Scale

The chatbot clearly communicated why I was being transferred to a human agent.

Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q17
Multiple Choice

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer not to say
Q18
Opinion Scale

How comfortable are you using digital chat or messaging for support?

Scale: 1 – 7
Min:Not at all comfortableMax:Very comfortable
Q19
Opinion Scale

I understood each step the chatbot took before routing me to a person or resolving my issue.

Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q20
Dropdown

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
Q21
Multiple Choice

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
  • Email
  • Phone
  • In-store or on-site
  • Other
Q22
Opinion Scale

My preference for how I wanted to get help (on my own or with a person) was respected.

Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q23
Dropdown

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
Q24
Multiple Choice

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
Q25
Opinion Scale

Overall, how fair did the process of being routed to (or away from) a human agent feel?

Scale: 1 – 7
Min:Very unfairMax:Very fair

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • AI follow-ups automatically dig deeper when respondents report low satisfaction, uncovering root causes that static surveys miss
  • Academic-grade scale construction with rubric-checked questions—no leading language or attention checks that bias results
  • Every prompt, model, and logic branch is fully transparent and logged for reproducibility, unlike black-box competitor analytics
  • AI interviewer dynamically follows up on churn reasons—if a customer says 'too expensive,' it probes whether that's absolute cost, perceived value, or competitive pricing
  • Separate templates for exit diagnostic vs. win-back capture both the 'why they left' and 'what would bring them back' with distinct methodological approaches

SurveyMonkey

Customer Satisfaction Survey Template

SurveyMonkey's flagship CSAT template is expert-certified and widely used, covering overall satisfaction, NPS, and CES together. It offers solid distribution channels (email, SMS, web links, QR codes) and built-in CSAT score calculation. However, it relies entirely on static pre-written questions with no adaptive probing.

What it does well

  • Expert-certified methodology with built-in CSAT scoring formula and industry benchmarks
  • Extensive distribution options including SMS, email, web links, and QR codes
  • Large ecosystem with 400+ templates and cross-template metric comparison

Where it falls short

  • No AI-powered follow-up questions—open-ended responses are passive, not probed
  • Relies on demographic segmentation after the fact rather than real-time adaptive questioning
  • Paid plans required for advanced features; Team plans range from $25-$75/user/month which adds up fast

Typeform

Top Customer Satisfaction Survey Questions & Template

Typeform emphasizes a conversational, one-question-at-a-time interface designed to feel like a conversation rather than a form. Their CSAT template has good UX advice around avoiding bias and question timing, but ultimately all branching is pre-defined—there's no intelligent adaptation based on responses.

What it does well

  • Beautiful conversational UI that asks one question at a time, boosting completion rates
  • Strong guidance on avoiding biased language and proper survey timing
  • 300+ integrations with tools like Slack, HubSpot, and Google Sheets

Where it falls short

  • No AI follow-up capability—branching logic must be manually pre-configured for every path
  • No prompt or model transparency; the 'conversational' feel is purely visual, not intelligent
  • Limited methodological rigor—templates are light on proper academic scale construction

SurveySparrow

FREE Customer Satisfaction Survey Template

SurveySparrow's CSAT template features a chat-like interface and claims 40% higher response rates. It includes recurring survey scheduling, multi-channel distribution, and conditional logic. However, its AI capabilities are limited to text analytics on collected responses rather than intelligent in-survey probing.

What it does well

  • Chat-like conversational interface with claimed 40% higher response rates
  • Recurring survey scheduling for automated pulse checks over time
  • Conditional logic with skip/display rules to reduce survey fatigue

Where it falls short

  • AI features limited to post-collection text analytics (CogniVue)—no in-survey AI follow-ups
  • No transparency into how their AI text analytics models work or what prompts drive analysis
  • Template questions are generic and not tailored to specific CX touchpoints like chatbot handoffs or checkout friction

Jotform

Online Shopping Survey

Jotform's online shopping survey template is a basic form-builder approach—fully customizable with drag-and-drop, 100+ integrations, and free to use. It's functional but lacks any CX-specific methodology, AI capabilities, or sophisticated survey design principles.

What it does well

  • Completely free with no-code drag-and-drop customization
  • 100+ integrations including Google Drive, Dropbox, and Airtable
  • Report Builder tool for analyzing responses visually

Where it falls short

  • No AI-powered follow-ups or intelligent branching—purely static form fields
  • No built-in CSAT scoring, CES calculation, or CX-specific methodology
  • Generic shopping survey questions with no academic rigor or validated scale construction

Qualtrics

Customer Retention Survey Best Practices

Qualtrics offers enterprise-grade CX measurement with advanced features like Predict iQ for churn prediction and conversational analytics. Their approach is the most sophisticated among competitors, but it comes at enterprise pricing that's prohibitive for academics and small teams, and their AI operates as a black box.

What it does well

  • Predict iQ can analyze research data to predict which customers are about to churn
  • Conversational analytics for understanding emotion, effort, intent and sentiment at scale
  • Enterprise-grade action planning and closed-loop ticketing based on survey triggers

Where it falls short

  • Enterprise pricing is prohibitive for academics, startups, and small CX teams
  • AI analytics operate as a black box—no visibility into prompts, models, or logic flows
  • Templates are gated behind sales conversations; no free self-serve template access for most CX use cases

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