60+ Customer Service Survey Questions (+ Free Template)

96% of customers say customer service is important in their choice of loyalty to a brand (Microsoft). Yet most support teams fly blind: they resolve tickets without ever learning what the customer actually thought about the experience. It costs 5-25x more to acquire a new customer than retain one, so measuring service quality is not optional.

This guide gives you 60+ customer service survey questions organized by category, with question type recommendations and effectiveness ratings for each one. You also get best practices for post-interaction surveys, common mistakes that kill response rates, analysis frameworks, and a free template you can deploy in minutes.

What you will find in this guide:

• 60+ customer service survey questions organized into 7 categories
• Question type and effectiveness rating for each question
• Best practices for timing, length, and delivery of support surveys
• Common mistakes that silently sabotage your feedback quality
• How to analyze results by channel, agent, and issue type
• A free customer service survey template ready to deploy

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What Is a Customer Service Survey?

A customer service survey is a structured feedback tool sent after a support interaction to measure the quality of the experience. It captures how satisfied the customer is with the resolution, the agent, the process, and the channel they used.

These surveys typically combine quantitative metrics (CSAT, CES, NPS) with open-ended questions for context. CSAT measures satisfaction with the specific interaction. CES measures how much effort the customer had to put in. NPS measures overall loyalty. Each metric serves a different purpose, and the best support teams track all three.

The most effective customer service surveys share three traits: they are short (under 5 questions for post-interaction), they are triggered immediately after resolution, and they route low scores to follow-up workflows. Without structured feedback, support teams rely on ticket volume and resolution time alone, missing the human side of service quality entirely. A strong voice of the customer program starts with the right questions asked at the right time.

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Types of Customer Service Survey Questions

Before jumping to the questions, it helps to understand which question types work best for different support feedback goals.

Question Type	Best For (Customer Service Context)	Pros	Cons
Likert Scale (1-5)	CSAT, agent rating, resolution satisfaction	Easy to trend, benchmarkable across agents	Can feel repetitive in short surveys
CES Scale (1-7)	Measuring effort to get help	Strong predictor of loyalty and churn	Less intuitive than satisfaction scales
Rating Scale (0-10)	NPS, overall support experience	Granular measurement, widely understood	Can feel arbitrary for single interactions
Open-Ended	Context behind scores, improvement ideas	Surfaces issues you did not anticipate	18% nonresponse rate (Pew Research)
Binary (Yes/No)	Issue resolved, would use again, quick checks	Fastest for respondents after a support call	No nuance or degree of satisfaction
Multiple Choice	Channel used, issue type, contact reason	Easy to analyze and segment	Limited to predefined options

Key guideline: For post-interaction surveys, keep to 3-5 questions. Lead with a CSAT or CES rating, add one specific question about the agent or resolution, and close with an open-ended question for context. This formula captures quantitative benchmarks and qualitative insights without exhausting a customer who just spent time getting support.

Matching Question Types to Support Goals

• Measuring satisfaction with the interaction -> Likert scale (CSAT 1-5)
• Measuring effort to resolve an issue -> CES scale (1-7)
• Measuring overall loyalty post-support -> NPS (0-10)
• Understanding what went wrong or right -> Open-ended (limit to 1-2)
• Confirming resolution -> Binary (Yes/No), then branch if "No"
• Identifying channel preferences -> Multiple choice
• Screening before detailed questions -> Binary, then conditional logic

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60+ Customer Service Survey Questions by Category

Each question below includes a recommended question type and an effectiveness rating: Essential (include in every survey), Recommended (include when relevant), or Nice-to-have (include if survey length allows). Customize the bracketed text for your specific context.

Overall Support Experience (Questions 1-10)

Start with big-picture questions that establish a satisfaction baseline for the entire support interaction. These work for any support channel and give you the most trackable metrics over time.

1. How satisfied are you with the support you received today?

• Type: Likert (1-5, CSAT) | Essential
• Your primary benchmark metric. Track this weekly by agent, channel, and issue type to spot trends before they become problems. Use the CSAT survey template to get started.

2. How easy was it to get the help you needed?

• Type: CES Scale (1-7) | Essential
• Customer Effort Score. Research from Gartner shows that 96% of high-effort customers become disloyal, compared to only 9% of low-effort customers. Effort predicts retention more reliably than measuring customer satisfaction alone. Start measuring with a CES survey template.

3. How likely are you to recommend our support team to a friend or colleague?

• Type: Rating (0-10, NPS) | Recommended
• Net Promoter Score applied to support. Segment into Promoters (9-10), Passives (7-8), and Detractors (0-6). Track this separately from your product NPS to isolate support impact.

4. Overall, how would you rate the quality of your support experience?

• Type: Likert (1-5) | Essential
• Broader than CSAT, this captures the holistic experience including wait time, agent quality, and resolution. Compare against your CSAT score to see if specific elements drag down the overall rating.

5. Did our support team meet your expectations?

• Type: Scale (Exceeded / Met / Below) | Recommended
• Identifies expectation gaps. Consistent "Below" responses warrant deeper investigation into what customers expected versus what they received.

6. How has your perception of our company changed after this support interaction?

• Type: Scale (Much worse / Worse / Same / Better / Much better) | Recommended
• Support interactions are brand moments. This question reveals whether your support team is building or eroding brand equity with every ticket.

7. Compared to other companies, how would you rate our customer service?

• Type: Scale (Much worse / About the same / Better / Much better) | Nice-to-have
• Competitive positioning data. Customers compare you to every service experience they have had, not just your direct competitors.

8. How would you rate the overall communication during the support process?

• Type: Likert (1-5) | Recommended
• Covers clarity, tone, and proactive updates. Low scores here often point to template language issues or gaps in status communication.

9. In one word, how would you describe your support experience?

• Type: Open-ended | Nice-to-have
• Captures raw emotional response. Aggregate responses into word clouds for quick visual analysis in team meetings.

10. What score (1-10) would you give the support you received, and why?

• Type: Rating + Open-ended | Essential
• Combines a quantitative score with qualitative context. The "why" often surfaces the most actionable insights that a number alone cannot reveal.

Agent Performance and Professionalism (Questions 11-20)

These questions isolate agent-level performance from process-level issues. Use them to coach individual agents, recognize top performers, and identify training gaps.

11. How knowledgeable was the support agent who helped you?

• Type: Likert (1-5) | Essential
• Knowledge gaps are a training problem, not an individual problem. Track this by agent and by issue category to pinpoint where training is needed most.

12. Did the agent communicate clearly and in a way that was easy to understand?

• Type: Likert (1-5) | Essential
• Clarity matters especially for technical support. Low scores may indicate jargon overuse, language barriers, or insufficient explanation of next steps.

13. How courteous and professional was the support agent?

• Type: Likert (1-5) | Recommended
• Professionalism scores tend to be high across the board. When they drop, it is a strong signal of burnout, understaffing, or a specific agent issue.

14. Did the agent show genuine interest in resolving your issue?

• Type: Likert (1-5) | Recommended
• Empathy perception. Customers can tell the difference between someone following a script and someone who genuinely cares. This is difficult to train but critical to measure.

15. How well did the agent listen to and understand your problem?

• Type: Likert (1-5) | Essential
• Active listening is the foundation of effective support. Low scores here often correlate with repeat contacts because the original issue was misunderstood.

16. Did the agent provide accurate information?

• Type: Binary (Yes / No / Not sure) | Recommended
• Inaccurate information erodes trust faster than slow responses. Track "No" and "Not sure" responses to identify knowledge base gaps or misinformation patterns.

17. How would you rate the agent's ability to explain the solution to your issue?

• Type: Likert (1-5) | Recommended
• Knowing the answer and communicating it effectively are two different skills. This question isolates the communication ability from raw knowledge.

18. Did the agent set clear expectations about next steps and timelines?

• Type: Binary (Yes / No) | Recommended
• Expectation setting prevents repeat contacts and reduces anxiety. "No" responses often indicate a process gap rather than an individual failure.

19. Would you want to work with this agent again for future support needs?

• Type: Binary (Yes / No) | Nice-to-have
• A strong proxy for overall agent performance. "No" responses warrant a review of the specific interaction to identify what went wrong.

20. What could the agent have done differently to improve your experience?

• Type: Open-ended | Essential
• Agent-specific open-ended feedback. Route responses to the agent's manager for coaching conversations. Focus on patterns across multiple responses rather than single data points.

Issue Resolution and Effectiveness (Questions 21-30)

Resolution quality is the core of customer service. These questions measure whether the problem was actually solved and how the customer feels about the outcome.

21. Was your issue fully resolved?

• Type: Scale (Yes / Partially / No) | Essential
• Gate question. "Partially" and "No" responses should trigger an automatic escalation or follow-up workflow. First contact resolution rate is one of the most important support metrics. Measure task completion alongside resolution with a task accomplishment survey.

22. How satisfied are you with the resolution provided?

• Type: Likert (1-5) | Essential
• A resolved issue is not always a satisfying one. A customer might get their refund but still feel the process was painful. This captures outcome satisfaction separately from resolution status.

23. How many times did you have to contact us to resolve this issue?

• Type: Multiple choice (1 / 2 / 3 / 4+) | Essential
• Repeat contact rate. Every additional contact doubles frustration and cost. Customers who contact you 3+ times are at high churn risk. Combine with other signals to reduce churn rate.

24. How long did it take to resolve your issue from first contact?

• Type: Multiple choice (Same day / 1-2 days / 3-5 days / More than 5 days) | Recommended
• Perceived resolution time matters more than actual resolution time. Compare customer perception against your ticket data to identify disconnect.

25. Was the solution provided relevant to your specific problem?

• Type: Likert (1-5) | Recommended
• Relevance separates personalized support from template responses. Low scores suggest agents are applying generic solutions without fully diagnosing the issue.

26. Did you have to repeat information you already provided?

• Type: Binary (Yes / No) | Recommended
• Repeating information is one of the top customer frustrations. "Yes" responses indicate poor internal handoff processes, missing context in tickets, or inadequate CRM usage.

27. How confident are you that the issue will not happen again?

• Type: Likert (1-5) | Recommended
• Confidence in lasting resolution. Low scores suggest the root cause was not addressed or that the customer received a workaround rather than a real fix.

28. Was the resolution delivered within the timeframe you were given?

• Type: Binary (Yes / No) | Recommended
• Promise fulfillment. "No" responses signal either unrealistic expectation setting by agents or process bottlenecks that prevent timely resolution.

29. If your issue was escalated, how smooth was the handoff between agents or departments?

• Type: Likert (1-5) | Nice-to-have
• Escalation experience. Conditional on escalated tickets only. Poor handoffs are a common pain point that customers feel acutely.

30. What would have made the resolution process better?

• Type: Open-ended | Essential
• Resolution-specific improvement feedback. Responses often surface concrete process improvements like "let agents issue refunds directly" or "provide status updates via email."

Response Time and Accessibility (Questions 31-40)

Speed and accessibility set the tone for the entire support experience. These questions measure whether customers can reach you when they need to and how long they wait.

31. How would you rate the initial response time?

• Type: Likert (1-5) | Essential
• First response time shapes the entire perception of the interaction. Even a brief acknowledgment ("We received your request") reduces perceived wait time significantly.

32. Was it easy to find how to contact our support team?

• Type: Likert (1-5) | Recommended
• Accessibility friction. If customers struggle to find the support page, chat widget, or phone number, frustration starts before the interaction even begins.

33. How long did you wait before being connected to an agent?

• Type: Multiple choice (Less than 1 min / 1-5 min / 5-15 min / 15-30 min / More than 30 min) | Recommended
• Objective wait time data. Cross-reference with queue metrics to validate customer perception against actual wait times.

34. Did we meet your expected response time?

• Type: Scale (Faster than expected / As expected / Slower than expected) | Recommended
• Expectation-relative measurement. A 2-hour response might delight a customer who expected 24 hours, or frustrate one who expected 30 minutes.

35. Were you kept informed about the status of your request while waiting?

• Type: Binary (Yes / No) | Recommended
• Proactive updates reduce perceived wait time and inbound "any update?" follow-ups. "No" responses indicate a gap in your status communication workflow.

36. How would you rate the availability of our support team during the hours you needed help?

• Type: Likert (1-5) | Recommended
• Availability perception. Low scores from specific time zones or hours reveal coverage gaps in your support schedule.

37. Were you able to reach support through your preferred channel?

• Type: Binary (Yes / No) | Nice-to-have
• Channel availability. "No" responses indicate demand for channels you do not currently offer or that are difficult to find.

38. If you were placed on hold or in a queue, was the wait time acceptable?

• Type: Likert (1-5) | Nice-to-have
• Hold experience quality. Includes factors beyond raw wait time: hold music, position-in-queue updates, and callback options.

39. How would you rate the speed of the overall resolution process from start to finish?

• Type: Likert (1-5) | Essential
• End-to-end speed perception. Distinct from first response time, this captures the total time investment the customer made.

40. What could we do to make reaching our support team faster or easier?

• Type: Open-ended | Recommended
• Accessibility-specific feedback. Responses frequently suggest concrete improvements like "add live chat," "extend weekend hours," or "make the support number easier to find."

Self-Service and Knowledge Base (Questions 41-48)

Self-service reduces ticket volume and gives customers faster answers. These questions measure whether your help center, documentation, and automated tools are actually working.

41. Did you try to find an answer on your own before contacting support?

• Type: Binary (Yes / No) | Essential
• Self-service attempt rate. High "Yes" rates combined with support contact mean your self-service content is failing. Low "Yes" rates mean customers do not know self-service exists.

42. Were you able to find what you needed in our help center or knowledge base?

• Type: Scale (Yes / Partially / No / Did not try) | Essential
• Self-service success rate. "Partially" responses are the most actionable since they mean the content exists but is incomplete or confusing.

43. How helpful was our documentation or FAQ section?

• Type: Likert (1-5) | Recommended
• Content quality score. Low ratings suggest articles need updating, better search functionality, or clearer step-by-step instructions.

44. How easy was it to navigate our help center?

• Type: Likert (1-5) | Recommended
• Findability is separate from content quality. Great articles that no one can find are effectively useless. This isolates the navigation and search experience.

45. If you used a chatbot or automated support, how helpful was it?

• Type: Likert (1-5) | Recommended (conditional)
• Chatbot effectiveness. Conditional on chatbot usage. Low scores reveal where automated responses fail and human handoff should be triggered earlier.

46. What information were you looking for that you could not find?

• Type: Open-ended | Essential
• Content gap identification. Every response is a potential new help center article or an update to an existing one.

47. Would you prefer to resolve issues through self-service if the right resources were available?

• Type: Binary (Yes / No) | Nice-to-have
• Self-service demand signal. High "Yes" rates justify investment in knowledge base expansion and better search tooling.

48. How would you rate the accuracy of the information in our self-service resources?

• Type: Likert (1-5) | Nice-to-have
• Accuracy is non-negotiable for self-service. Outdated or incorrect articles damage trust and generate tickets instead of deflecting them.

Channel Satisfaction and Preferences (Questions 49-55)

Different customers prefer different channels, and satisfaction varies by channel. These questions help you optimize your channel mix and invest in the right places.

49. Which support channel did you use for this interaction?

• Type: Multiple choice (Live chat / Email / Phone / Social media / In-app / Help center / Other) | Essential
• Channel identification. Cross-tabulate with satisfaction scores to identify which channels perform best and worst.

50. How satisfied are you with the support you received through this channel?

• Type: Likert (1-5) | Essential
• Channel-specific CSAT. Compare across channels to see where you excel and where you underperform. Some channels may need more investment or better staffing.

51. What is your preferred channel for contacting customer support?

• Type: Multiple choice (Live chat / Email / Phone / Social media / In-app / Self-service) | Recommended
• Preference data shapes resource allocation. If 60% of customers prefer live chat but you staff 60% for phone, there is a mismatch. For more on channel strategy, see our guide on survey distribution methods.

52. Was the channel you used appropriate for the type of issue you had?

• Type: Binary (Yes / No) | Recommended
• Channel-issue fit. Complex technical issues may require screen sharing, while billing questions work fine over chat. "No" responses reveal routing problems.

53. Have you used multiple channels to resolve a single issue?

• Type: Binary (Yes / No) | Recommended
• Channel switching is a strong friction indicator. Customers who switch channels are significantly less satisfied. Each switch means the system failed at the previous touchpoint.

54. How would you rate the consistency of service across different channels?

• Type: Likert (1-5) | Nice-to-have
• Omnichannel consistency. Customers expect the same quality whether they reach you via chat, email, or phone. Inconsistency erodes confidence.

55. Would you use a video call option for complex support issues if it were available?

• Type: Binary (Yes / Maybe / No) | Nice-to-have
• Demand validation for new channels. "Maybe" and "Yes" responses gauge appetite for richer support formats before you invest in them.

Open-Ended and Future Intent (Questions 56-62)

Open-ended questions surface insights that structured questions miss entirely. Future intent questions predict behavior and identify at-risk customers.

56. What could we have done better to improve your support experience?

• Type: Open-ended | Essential
• The broadest improvement question. Responses here often surface issues not covered by any of your structured questions.

57. What did our support team do well that you appreciated?

• Type: Open-ended | Recommended
• Positive feedback is as actionable as negative feedback. Use it to recognize agents, reinforce good behaviors, and document what works.

58. Based on this support experience, how likely are you to continue using our product or service?

• Type: Likert (1-5) | Essential
• Direct retention predictor tied to the support interaction. Low scores are an early warning system for churn. Combine with other signals to reduce churn rate.

59. Is there anything about our support process that frustrated you?

• Type: Open-ended | Recommended
• More specific than "what could we do better." The word "frustrated" gives permission to share negative emotions that customers might otherwise hold back.

60. If you could change one thing about how we handle customer support, what would it be?

• Type: Open-ended | Essential
• The "one thing" constraint forces prioritization. More actionable than general improvement prompts because respondents must choose their top issue.

61. Would you be willing to participate in a brief follow-up interview about your experience?

• Type: Binary (Yes / No) + optional email field | Nice-to-have
• Recruits customers for deeper qualitative research. Willing respondents are a goldmine for understanding the "why" behind survey scores.

62. Is there anything else you would like us to know about your support experience?

• Type: Open-ended | Recommended
• The catch-all. Always place this as your final question. Some of the most valuable feedback comes from things you did not think to ask about. For more on extracting value from open-ended responses, see our guide on analyzing customer feedback.

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Customer Service Survey Best Practices

Writing good questions is half the battle. How you time, structure, and deliver your survey determines whether you get actionable data or noise.

Send within 24 hours of the interaction. Customer memory decays fast. Trigger surveys immediately after ticket resolution or chat session end. For phone support, send within 1 hour while the experience is still fresh.

Keep post-interaction surveys under 5 questions. Your customer just spent time and energy getting support. Respect that. A CSAT rating, one specific question, and an open-ended follow-up covers the essentials. Save longer surveys for quarterly relationship check-ins.

Use CES for effort-focused measurement. Customer Effort Score is often a better predictor of loyalty than satisfaction alone. Customers tolerate imperfect outcomes when the process is easy. They churn when the process is painful, even if the outcome is good.

Combine CSAT with open-ended for context. A score of 2/5 tells you something is wrong. An open-ended "why" tells you what to fix. Always pair at least one quantitative metric with one qualitative question.

Route low scores to immediate follow-up. A customer who rates you 1 or 2 out of 5 is at risk of churning, leaving a negative review, or both. Set up automated workflows that alert a manager when low scores come in. Follow up within 24 hours. Closing the feedback loop transforms detractors into retained customers.

Track per-agent performance over time. Individual survey responses are noisy. Aggregate scores over 30+ responses to get a reliable picture of each agent's strengths and development areas. Use this data for coaching, not punishment.

Optimize for mobile. Many customers complete post-support surveys on their phone. Test your survey on mobile devices. Avoid matrix questions and keep text input fields minimal. For more on timing and channel strategy, see our guide on survey distribution methods.

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Common Customer Service Survey Mistakes

These mistakes silently sabotage your data quality. Each one is common, and each one is fixable.

Mistake 1: Sending surveys too late

Bad: Emailing a survey 5 days after the support interaction.

Better: Trigger the survey within 1 hour of resolution or chat session end.

After 48 hours, customers forget details and give less accurate feedback. Automate survey triggers based on ticket status changes, not manual sends.

Mistake 2: Making surveys too long after a support interaction

Bad: A 15-question survey after a 3-minute chat.

Better: 3-5 targeted questions that take under 2 minutes.

Your customer already invested time getting support. A long survey feels like additional work. Completion rates drop dramatically past 5 questions for post-interaction surveys.

Mistake 3: Not following up on low scores

Bad: Collecting a 1/5 CSAT score and doing nothing.

Better: Auto-route scores of 1-2 to a manager for same-day follow-up.

Negative feedback without follow-up is worse than not asking at all. The customer took time to tell you something is wrong and was ignored. This accelerates churn.

Mistake 4: Asking about things the agent cannot control

Bad: "How satisfied are you with our pricing?" in a post-support survey.

Better: Focus questions on the interaction, the agent, and the resolution.

Agents cannot change pricing, product features, or company policy. Asking about these in a support survey frustrates agents and produces data that belongs in a different survey.

Mistake 5: Generic questions that do not reference the specific interaction

Bad: "How do you feel about our customer service in general?"

Better: "How satisfied are you with the support you received today regarding [issue type]?"

Specific questions yield specific answers. When possible, personalize the survey with the ticket topic, agent name, or channel used. Personalization increases both response rates and data quality. For more strategies to boost participation, see our guide on how to increase survey response rates.

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How to Analyze Customer Service Survey Results

Collecting data is step one. Turning it into decisions is where the value lives.

Calculate your key scores. Start with headline metrics. CSAT is the percentage of respondents who selected 4 or 5 on a 5-point scale. CES is the average effort score (lower is better on a 1-7 scale). NPS is the percentage of Promoters (9-10) minus Detractors (0-6). Calculate these weekly for a real-time health check. You can cross-check aggregates with our free CSAT calculator and CES calculator.

Segment by channel, agent, and issue type. Averages hide problems. A 4.0 CSAT average might mask the fact that phone support scores 4.5 while email support scores 3.2. Break results down by support channel, individual agent, issue category, and resolution status. Use cross-tabulation to find patterns: satisfaction by channel, effort by issue complexity, or resolution rate by agent tenure.

Track trends over time. A single survey snapshot tells you where you are. Longitudinal data tells you where you are headed. Compare scores week over week, month over month, and quarter over quarter. Spot emerging issues before they become crises. Measure whether process changes actually moved the needle. For a deeper framework, see our guide on customer experience analytics.

Cross-tabulate satisfaction by resolution time. Plot CSAT scores against resolution time buckets. This reveals your customers' tolerance threshold: the point where longer resolution times start significantly impacting satisfaction. Use this data to set SLA targets that balance speed with quality.

Analyze open-ended responses by theme. Group responses into categories: agent knowledge, wait time, resolution quality, communication, process friction. Count frequency (how many people mention each theme) and assess intensity (how strongly they feel). Focus first on themes with both high frequency and high intensity.

Close the loop. Share findings with your support team weekly. Communicate changes back to customers: "Based on your feedback, we have extended chat support hours" or "We have updated our knowledge base articles on [topic]." This builds trust and increases future response rates. See our guide on closing the feedback loop for a detailed framework.

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Free Customer Service Survey Template

Skip the blank page. Formbricks offers free, open-source survey templates you can deploy in minutes, including a dedicated customer service survey template. For SaaS teams, trigger post-chat or post-ticket surveys directly inside your app so customers respond in context, without email open rates to worry about.

How to get started:

1. Sign up at formbricks.com (free tier available, no credit card required)
2. Choose a customer service survey template or start from scratch
3. Customize the questions from this guide for your support workflow
4. Set targeting rules to trigger surveys after ticket resolution, chat end, or specific support events
5. Launch and monitor responses in real time from your dashboard

Formbricks is open source, privacy-first, and supports self-hosting for teams that need full data control. With granular targeting, you can show surveys to specific user segments based on ticket type, plan tier, or support channel. It is built for support teams who want to collect targeted feedback without heavy engineering lift. For teams operating in regulated industries, Formbricks is a GDPR-compliant survey tool that keeps data on your infrastructure.

Get Your Free Customer Service Survey Template ->

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Frequently Asked Questions