Q01
Message
All templates
Knowledge Base Content Effectiveness Survey
Measures article findability, clarity, completeness, and task-completion outcomes for knowledge base users. Use to identify content gaps and prioritize help center improvements.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
24 questions · ~11 min
Q02
Dropdown
When did you most recently read an article in our knowledge base?
- Within the last 7 days
- 8–30 days ago
- 31–90 days ago
- More than 90 days ago
- Not sure
Q03
Multiple Choice
How did you find that article?
- Typed a query in our site search
- Browsed categories or menus
- Used Google or another search engine
- In-product link
- Link from a support agent or email
- Link from a community or forum
- Saved bookmark
- Other (please specify)
Q04
Message
The following statements are about the article you read most recently. Please indicate how much you agree or disagree with each one.
Q05
Multiple Choice
Did this article enable you to complete your task?
- Yes, completely
- Yes, partially
- No
- I was just browsing
Q06
Long Text
What, if anything, was missing or unclear in the article?
Q07
Dropdown
Which best describes your role?
- Individual contributor
- Team lead or manager
- Executive or founder
- Consultant or agency
- Educator or trainer
- Student
- Prefer not to say
Q08
Message
Thank you for your time—your feedback helps us improve the knowledge base for everyone.
Q09
Multiple Choice
Which type of article did you read most recently?
- Getting started / Onboarding
- How-to / Step-by-step
- Troubleshooting / Error resolution
- Reference / API or developer docs
- Release notes / What's new
- Account, billing, or licensing
- Other (please specify)
Q10
Multiple Choice
When you use search on our knowledge base, how often does it return a helpful article?
- Always
- Often
- Sometimes
- Rarely
- Never
- I have not used search
Q11
Opinion Scale
The article was easy to understand.
Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q12
Opinion Scale
Overall, how easy was it to use the article to achieve your goal?
Scale: 1 – 7
Min:Very difficultMax:Very easy
Q13
Multiple Choice
Which improvements would most help you find the right articles? Select all that apply.
- Clearer article titles
- Better search relevance
- More tags or filters
- Better categorization
- More cross-links between articles
- Show article dates and last-updated info
- None of the above
Q14
Dropdown
Which industry best describes your organization?
- Technology / Software
- Finance / Insurance
- Healthcare / Life sciences
- Education
- Manufacturing
- Retail / eCommerce
- Government / Nonprofit
- Media & Communications
- Other
- Prefer not to say
Q15
Dropdown
Approximately how long did it take you to find that article?
- Less than 1 minute
- 1–3 minutes
- 4–10 minutes
- 11–20 minutes
- More than 20 minutes
- I don't remember
Q16
Opinion Scale
The article covered the topic completely.
Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q17
Opinion Scale
How likely are you to use our knowledge base again in the future?
Scale: 1 – 7
Min:Very unlikelyMax:Very likely
Q18
AI Interview
Thank you for your survey responses. I'd like to ask a couple of follow-up questions to better understand your knowledge base experience. What was the main reason you visited the knowledge base on your most recent visit, and how well did it meet your expectations?
Q19
Dropdown
How many employees work at your organization?
- 1–9
- 10–49
- 50–249
- 250–999
- 1,000–4,999
- 5,000+
- Prefer not to say
Q20
Opinion Scale
How easy was it to identify the right article from the search results or category listing?
Scale: 1 – 7
Min:Very difficultMax:Very easy
Q21
Opinion Scale
The information in the article was accurate and up to date.
Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q22
Long Text
Based on your responses in this survey, please share any additional thoughts or feelings about your knowledge base experience.
Q23
Opinion Scale
The article was well-organized and easy to scan.
Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
Q24
Opinion Scale
The article included helpful examples, screenshots, or visuals.
Scale: 1 – 7
Min:Strongly disagreeMax:Strongly agree
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 TemplateSurveyMonkey'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 & TemplateTypeform 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 TemplateSurveySparrow'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 SurveyJotform'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 PracticesQualtrics 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
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.