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Fast Food Customer Experience & Loyalty Survey

Measures satisfaction with speed, accuracy, food quality, and value at a quick-service restaurant, plus what drives repeat visits and recommendations. An AI follow-up interview reconstructs exactly what happened on the customer's most recent visit — especially when something went wrong — instead of settling for a star rating.

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Sample questions

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

13 questions · ~7 min
Q01
Message

Thanks for stopping by! We'd love to hear about your recent experience at (Replace with restaurant name). This will take about 4-5 minutes and your honest feedback helps us fix what's not working.

Q02
Multiple ChoiceRequired

In the last 3 months, how often have you visited (Replace with restaurant name)?

  • This was my first visit
  • 1-2 times
  • 3-5 times
  • 6-10 times
  • More than 10 times
Q03
Multiple ChoiceRequired

On your most recent visit, how did you place your order?

  • Dine-in counter
  • Drive-thru
  • Mobile app or kiosk
  • Delivery app (e.g. Replace with DoorDash/Uber Eats)
  • Phone order
Q04
MatrixRequired

Thinking about your most recent visit, how would you rate each of the following?

6 rows × 5 columns
  • Food taste and quality
  • Order accuracy
  • Speed of service
  • Cleanliness of the location
  • Staff friendliness
  • +1 more
Columns: Poor · Fair · Good · Very good · Excellent
Q05
Opinion ScaleRequired

How likely are you to recommend (Replace with restaurant name) to a friend or family member?

Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q06
Multiple ChoiceRequired

On your most recent visit, was your order correct?

  • Yes, completely correct
  • Mostly correct, one small mistake
  • No, a major item was wrong or missing
  • I didn't check before leaving
Q07
AI Interview

Reconstruct exactly what happened on the respondent's most recent visit: what they ordered, how the process went from ordering to receiving food, and whether it matched expectations. If they reported an order accuracy problem or gave a low recommendation rating, dig into what specifically went wrong, how staff handled it (if at all), and whether it would change where they go next time. If everything went well, ask what one thing stood out as the highlight.

Q08
Best–Worst Trade-off (MaxDiff)

If (Replace with restaurant name) could only improve ONE of these, which would matter most to you — and which matters least?

  • Faster order and pickup times
  • Lower prices
  • More order accuracy
  • Friendlier, more attentive staff
  • Cleaner dining area and restrooms
  • Healthier menu options
  • More menu variety
  • A smoother app or ordering experience
Pick best & worst per setBest:Matters mostWorst:Matters least
Q09
Opinion ScaleRequired

How likely are you to visit (Replace with restaurant name) again in the next 30 days?

Scale: 1 – 5
Min:Very unlikelyMax:Very likely
Q10
Multiple Choice

What's the main reason you choose (Replace with restaurant name) over competitors like (Replace with Competitor A) or (Replace with Competitor B)?

  • Location or convenience
  • Price
  • Food taste or quality
  • Speed of service
  • Menu options for me or my family
  • Habit or loyalty program
  • Other
Q11
Multiple Choice

Which age range do you fall into?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65 or older
  • Prefer not to say
Q12
Multiple Choice

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q13
Message

That wraps things up — thank you for sharing your experience! Your feedback goes directly to our operations team to help us improve speed, accuracy, and quality at every visit.

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

  • Uses an AI follow-up interview to reconstruct exactly what happened on the respondent's most recent visit, especially when something went wrong, instead of stopping at a star rating
  • Pairs a satisfaction matrix and opinion-scale recommendation/repeat-visit questions with a MaxDiff exercise to identify the single highest-priority fix
  • Captures order accuracy and ordering channel as structured multiple-choice questions so follow-up context is quantifiable, not just anecdotal
  • Runs on a platform with free tier access, automated per-response quality scoring, and transparent, published prompts — no academic pricing tier exists or is claimed

QuestionPro

Food survey questions | Food-related survey questions & template

This is more of a curated question-and-guide page covering food-related survey topics than a single ready-to-field fast food CX template. It's useful as a reference library for question ideas, but a researcher would need to assemble and configure their own survey from it. No mention of adaptive interviewing or automated scoring.

What it does well

  • Broad library of food-related survey questions across multiple use cases
  • Backed by an established survey platform with standard question types
  • Good starting reference for drafting a fast food survey from scratch

Where it falls short

  • Static question bank rather than an adaptive interview that probes what actually happened on a visit
  • No automated per-response quality scoring
  • No published prompt-level methodology since there's no AI interviewing component

Jotform

Fast Food Survey Form Template

A dedicated, fielding-ready fast food survey form built on Jotform's drag-and-drop form builder. It likely covers basic satisfaction and visit-frequency questions but relies entirely on fixed fields rather than any conversational follow-up. Good for quick deployment, limited for depth on 'what went wrong' incidents.

What it does well

  • Purpose-built specifically for fast food feedback, not a generic template
  • Fast to deploy using Jotform's familiar drag-and-drop builder
  • Likely supports standard branding and embedding options

Where it falls short

  • Fixed-field form with no adaptive AI follow-up to reconstruct a specific visit
  • No voice AI interview option or guided screen-share task capability
  • No automated quality scoring of open-ended responses

SurveyMonkey

Customer Experience Survey Template & Questions

A general-purpose customer experience template, not specific to fast food or quick-service restaurants, so it would need heavy customization to capture order accuracy, speed, and value drivers. It's backed by a mature survey platform with strong distribution and reporting features. Depth on any single incident still depends on manual open-text questions.

What it does well

  • Well-established platform with broad distribution and analytics tooling
  • Generic CX template is flexible across many industries
  • Likely includes standard NPS/satisfaction question types out of the box

Where it falls short

  • Not tailored to fast food specifics like order accuracy or speed of service
  • No adaptive AI follow-up interview to reconstruct a specific recent visit
  • No transparent published prompts or automated per-response quality scoring

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