顧客体験アンケートテンプレート

A short customer feedback survey for people who just chose a paid plan, written to avoid common question mistakes: general questions about the app before specific ones about today's checkout; exact numbers for how often and how long, with a seven-day window instead of "often"; balanced 5-point scales that name what they rate instead of agree/disagree; price and quality asked separately; past behavior (when they last paid for an app like this) instead of "would you pay"; and answer ranges that never overlap. Replace "the app" and "plan" with your own product.

Captures how customers rate your drinks, wait time, staff, and atmosphere on their most recent visit, plus their likelihood to return. An AI follow-up digs into the specific moment that shaped their rating so you know exactly what to fix or protect.

A voice-first interview for customers who recently bought your product, capturing why they chose it over alternatives, how the first-use experience went, and whether they'd recommend it. Short closed questions anchor the context; the AI voice interview does the real work, reconstructing the purchase story and probing the reasoning behind their recommendation score. Built for CX and product marketing teams.

For podcast hosts and producers to learn how guests actually experienced booking, recording, and the finished episode. Covers pre-show communication, recording-day comfort, and post-production follow-through, with an AI follow-up interview that digs into the specific moments behind a guest's overall rating.

Measures whether employees have the tools, authority, training, and backing they need to do their jobs well and serve customers confidently. Built for CX and people leaders who want to trace weak customer experiences back to gaps in frontline support, with an AI follow-up that reconstructs a real moment where an employee felt blocked or empowered.

Captures what happened when a customer raised a complaint — how easy it was to submit, how the resolution process felt, and whether it was actually fixed. An AI follow-up interview digs into the specific moment satisfaction dropped (or rose), surfacing what a closed-ended score alone would miss. Built for support, CX, and quality teams reviewing complaint handling.

Measures how guest bloggers and outside contributors experienced your pitching, editorial, and publishing process — from response times to backlink fairness. Built for content marketing and editorial teams that manage a guest-posting program. The AI follow-up interview digs into the specific friction points behind low satisfaction or referral scores so you know exactly what to fix before your next contributor.

Captures how a specific customer support interaction went — channel used, resolution, effort, and how the agent performed on key behaviors — with an AI follow-up that digs into the real reason behind the satisfaction score, not just the number. Built for support ops and CX teams tracking agent quality over time.

Measures how often and how strongly your product or service exceeds expectations, which specific drivers (speed, personalization, empathy, unexpected perks) matter most, and captures a real delight moment in the respondent's own words. The AI follow-up interview reconstructs what actually happened in that moment so you know what to replicate, not just that delight occurred.

Checks whether the information you hold on a customer — contact details, product usage, communication preferences — still matches reality, and surfaces which channels and message types they actually want. An AI follow-up interview digs into what's changed in their life or business that your records haven't caught up with yet. Ideal for CRM data hygiene and preference-center refreshes.

Captures how customers experienced a recent request to their bank — the channel used, whether the issue was resolved, and how the interaction felt — with an AI follow-up that digs into the specific moment satisfaction rose or fell. Built for banks and credit unions tracking service quality after calls, chats, branch visits, or app contacts.

Captures how customers rate their recent purchase or service experience, which specific aspects drove that rating, and whether they'd buy again — with an AI follow-up interview that digs into the real moment behind their score instead of a generic star rating.

Tracks how often and through which channels customers actively engage with your brand, what's pulling them in or pushing them away, and which levers would deepen engagement — with an AI follow-up that digs into the real reasons behind a customer's current engagement level.

Captures how guests rate their visit across cleanliness, staff service, value, and amenities, plus their likelihood to recommend and return. An AI follow-up interview digs into the specific moment behind their score — what went right or wrong and why — so you get root causes, not just numbers.

Surfaces customers whose results are strong enough to feature in a case study — capturing the before/after problem, quantifiable impact, and a quotable moment. The AI follow-up interview reconstructs the full narrative arc so marketing and customer success teams can identify and write up the best stories.

Captures how guests experienced a recent stay — room quality, staff service, check-in/out, value, and amenities — and uses a best-worst trade-off to show which amenities actually drive satisfaction. An AI follow-up digs into the specific moment behind the guest's lowest-rated aspect instead of settling for a generic complaint.

Measures how easily customers or employees got their issue resolved through your help desk — first-contact resolution, effort, agent quality — for one specific support interaction. An AI follow-up interview reconstructs exactly what caused friction (or what went smoothly) so support leaders know what to fix, not just how people rated it.

Measures how a specific customer service interaction went — channel used, resolution outcome, and how the representative came across on knowledge, courtesy, and communication — with an AI follow-up that reconstructs exactly what happened during the interaction when something went wrong. Built for support and CX teams scoring individual reps or interactions.

Captures how dealers rate a supplier across order fulfillment, rep responsiveness, training, and marketing support, then uses a best-worst trade-off to surface which support gaps matter most. An AI follow-up interview digs into the reasoning behind the dealer's lowest rating or recommendation score, uncovering specific incidents rather than vague dissatisfaction.

Measures how well a recent technical support interaction resolved the customer's issue, how much effort it took, and how the agent performed — built for support and CX teams tracking ticket quality beyond a single CSAT number. An AI follow-up interview reconstructs what actually happened on the ticket instead of just the rating given.

A fast, 2-3 minute check-in that captures overall satisfaction, likelihood to recommend, and satisfaction with specific touchpoints — with an AI follow-up that digs into the real reason behind each score instead of just the number. Built for post-purchase, post-support, or quarterly relationship pulses.

Adapts the classic loyalty acid test — would they recommend, give again, defend you, forgive a mistake — for nonprofit donors and supporters. Includes an AI follow-up interview that digs into the real reasons behind a donor's loyalty score, especially for lukewarm or at-risk givers, so fundraising teams know what to fix before the next campaign.

Measures how easy, fast, and frustration-free your sign-up or account creation flow actually is, pinpointing which steps cause drop-off. An AI follow-up interview reconstructs the exact moment a respondent almost gave up, going beyond satisfaction scores to the specific friction that needs fixing.

Measures how consistent and satisfying your customer experience is across channels — web, app, phone, chat, and in-store — and where customers hit friction switching between them. An AI follow-up interview digs into the specific moment a channel switch broke down, surfacing detail that closed-ended ratings alone miss.

Evaluates a recent online support interaction — channel used, resolution, effort, and satisfaction — for teams running live chat, email, or chatbot support. An AI follow-up interview digs into what actually happened during the contact so low scores come with a specific, fixable story instead of just a number.

Evaluates how easily customers can find products, complete purchases, and navigate your retail website — covering search, checkout, speed, and design. An AI follow-up interview digs into what happened during the visit itself, especially for shoppers who hesitated or abandoned their purchase, for teams optimizing conversion and usability.

A periodic pulse check for Mailchimp customers covering feature usage, satisfaction with core tools, support experience, and likelihood to recommend. An AI follow-up interview digs into the story behind each customer's recommendation score, surfacing the specific moment or friction point that drove it instead of a flat number.

Turns a single piece of customer feedback into a structured signal — capturing what prompted the comment, rating the specific aspects of the experience behind it, and using an AI follow-up interview to reconstruct the exact moment, cause, and fix. Built for support, CX, and product teams triaging open-ended comments.

Gathers star ratings, structured feedback on quality, service, and value, and a publishable written review from recent customers. An AI follow-up interview digs into the specific moment behind their score, turning vague ratings into quotable, credible reviews for your site, app store, or marketplace listings.

Captures Net Promoter Score right after a specific purchase, support call, delivery, or service visit — revealing whether that single interaction is building loyalty or quietly eroding it. Built for CX, support, and operations teams who need transaction-level signal instead of annual survey noise, with an AI follow-up that uncovers the concrete moment behind each score rather than a vague comment.

Tracks how satisfied customers are with a local or small business — overall experience, specific touchpoints like staff and value, and likelihood to return or recommend — with an AI follow-up that digs into the real story behind each customer's score. Built for owners and managers who want concrete, actionable feedback in under 5 minutes.

Measures policyholder satisfaction with their insurance agency across service, claims handling, pricing clarity, and communication, plus renewal intent. An AI follow-up interview digs into the real story behind a recent claim or service interaction so agencies know exactly what to fix or protect.

Captures how customers actually reach you for help, how easy and effective that contact was, and which channels they'd pick again — with an AI follow-up that reconstructs their most recent support interaction in detail instead of relying on vague satisfaction ratings.

Captures how a recent support request went — channel used, effort required, resolution status, and satisfaction — paired with an AI follow-up that digs into what actually happened during the interaction, not just the rating given. Built for support and CX teams tracking ticket quality beyond CSAT scores.

Captures a customer's satisfaction, the specific result they achieved, and their willingness to be featured — with an AI follow-up interview that reconstructs the concrete before/after story behind their experience instead of a generic compliment. Built for marketing, customer success, and case study teams sourcing usable testimonials.

Captures how people applying for or using U.S. government support programs (unemployment, SNAP, Medicaid, housing assistance, etc.) actually experience the process — from applying to getting help when something goes wrong. An AI follow-up interview digs into the specific moment that made the process feel easy or frustrating, giving program teams concrete friction points instead of vague satisfaction scores.

Measures the concrete business or life outcomes customers have achieved since adopting your product or service — not just satisfaction, but what actually changed. Includes an AI follow-up interview that reconstructs a specific before-and-after story behind the numbers, giving you case-study-ready evidence of real impact.

Builds a clear picture of your typical customer — how they found you, why they chose you over alternatives, what they value most, and how they actually use your product day to day. An AI follow-up interview digs into the real story behind their top decision driver, giving teams building ideal customer profiles or refining positioning something more concrete than survey averages.

Benchmarks your Net Promoter Score against named competitors among customers who use both, then uses an AI follow-up to uncover the specific moments that widen or close the gap. Built for CX and product teams who need more than a single number — they need to know why customers would (or wouldn't) switch.

Identifies which customers have results worth turning into a case study, testimonial, or video story, and gauges their willingness to be featured. The AI follow-up interview goes past generic praise to reconstruct the specific before-and-after — the numbers, the turning point, the proof — that makes a customer story credible to a stranger.

Measures how satisfied business customers are with your company as a vendor — account management, reliability, support, and pricing value — plus renewal intent. An AI follow-up interview digs into the real reasons behind their satisfaction and renewal score instead of leaving you with a bare number.

Measures how satisfied repeat customers are with their most recent purchase or interaction, what keeps them coming back, and early signs of switching risk. An AI follow-up interview digs into the real story behind their satisfaction rating and any hesitation to stay loyal, surfacing specifics a scale alone can't capture. Built for retention and CX teams tracking existing customers rather than first-time buyers.

Captures satisfaction, effort, and resolution quality right after a support interaction, plus what matters most in a good service experience. An AI follow-up interview digs into the specific moment things went well or fell apart, surfacing details a rating alone can't explain. Built for support, CX, and operations teams.

Captures how customers rate your product, support, and value versus what actually drives their loyalty or churn risk. Combines a satisfaction and recommendation benchmark with a best-worst prioritization of improvement areas, plus an AI follow-up interview that digs into the story behind each score so you know exactly what to fix first.

Uncovers what customers are actually trying to accomplish, which needs matter most, and where your product or service currently falls short — with an AI follow-up that digs into the single biggest unmet need instead of settling for a generic complaint. Built for product, CX, and research teams planning roadmaps around real customer priorities.

Maps how customers rate your product, support, and value over time, then pinpoints what actually drives loyalty versus churn risk. An AI follow-up interview digs into the specific moment behind each customer's satisfaction score, surfacing concrete stories your dashboard numbers can't. Built for CX, product, and account teams running quarterly or post-milestone customer reviews.

Measures how well your brand's support holds up on social channels — response speed, resolution, agent tone — for customers who reached out via Twitter/X, Facebook, Instagram, or DM. An AI follow-up reconstructs the actual conversation thread to surface what closed loops fast and what left customers hanging.

Captures how easy it was to file a complaint online, how the resolution was handled, and whether the outcome felt fair — for CX and support teams auditing digital complaint channels. An AI follow-up interview reconstructs exactly what happened during resolution, not just a satisfaction score.

A general-purpose feedback survey that tracks recommendation likelihood, satisfaction, and which service aspects matter most to fix first — paired with an AI follow-up interview that digs into the specific moment behind each customer's score instead of a generic comment box.

Understand why a customer actually left, what nearly kept them, and what could bring them back — combining structured ratings on price, product, and support with an AI voice follow-up that reconstructs the real trigger behind the decision instead of the generic reason picked from a list. Built for CS, retention, and product teams running win-back research.