QuestionPunkQuestionPunk
Start free
All templates

Everyday Food Preference & Decision Drivers Survey

Maps what people actually eat, which cuisines and attributes they favor, and what really drives a food choice in the moment — price, health, convenience, or taste. Built for food brands, restaurants, and meal-kit teams shaping menus or products. An AI follow-up interview reconstructs the last real meal decision instead of relying on stated preferences alone.

Use this templateBrowse more templates

Sample questions

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

13 questions · ~7 min
Q01
Message

Hi! We'd love to understand your everyday food choices — what you like, what you avoid, and what actually tips the scale when deciding what to eat. This takes about 6 minutes and there are no wrong answers.

Q02
Multiple ChoiceRequired

Which of these best describes how you eat most of the time?

  • Eat everything (omnivore)
  • Vegetarian
  • Vegan
  • Pescatarian
  • Flexitarian (mostly plant-based)
  • Low-carb / keto
  • Other
Q03
Multiple ChoiceRequired

Which cuisines do you genuinely enjoy eating — not just tolerate? Select all that apply.

  • Italian
  • Mexican
  • Chinese
  • Indian
  • Japanese
  • Middle Eastern
  • American / comfort food
  • Thai / Southeast Asian
  • Mediterranean
  • French
Q04
Multiple ChoiceRequired

In the last 30 days, how often did you try a dish or cuisine that was new to you?

  • Never
  • Once
  • A few times
  • Weekly or more
Q05
MatrixRequired

When you're deciding what to eat, how important is each of the following?

6 rows × 5 columns
  • Taste and flavor
  • Health / nutrition
  • Price
  • Convenience / prep time
  • Sustainability or ethical sourcing
  • +1 more
Columns: Not at all important · Slightly important · Moderately important · Very important · Extremely important
Q06
Best–Worst Trade-off (MaxDiff)Required

Thinking about picking a meal on a typical day, which of these matters most, and which matters least?

  • Great taste
  • Low price
  • Health benefits
  • Fast / minimal effort
  • Familiar and comforting
  • Large portion size
  • Locally sourced or sustainable
  • Visually appealing
Pick best & worst per setBest:Matters mostWorst:Matters least
Q07
Point AllocationRequired

You have 100 points to distribute across the factors below based on how much weight each one gets in your typical food decisions. Split them however feels honest — they don't need to be equal.

  • Price
  • Taste
  • Health / nutrition
  • Convenience
  • Brand or reputation
  • Packaging or presentation
Allocate 100 points
Q08
Opinion ScaleRequired

How willing are you to try an unfamiliar dish or ingredient you've never had before?

Scale: 1 – 7
Min:Very hesitant, I stick to what I knowMax:Very willing, I actively seek novelty
Q09
Rating Scale

How satisfied are you with the food options currently available to you day-to-day (grocery, delivery, restaurants nearby)?

Range: 1 – 5
Min:Very dissatisfiedMax:Very satisfied
Q10
AI Interview

Ask the respondent to walk you through the last meal or snack they chose for themselves, step by step: what options they considered, what ultimately tipped the decision (price, mood, health, convenience, habit), and whether they were happy with the choice afterward. If their stated top priority from the point-allocation question doesn't match what actually drove that real decision, gently probe the gap. If they say they 'just grabbed whatever,' dig into what 'whatever' usually is and why that's the default.

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 many people, including yourself, do you typically cook or shop for?

  • Just myself
  • 2 people
  • 3-4 people
  • 5 or more people
  • Prefer not to say
Q13
Message

Thank you for sharing your food preferences! Your answers will feed directly into how we shape menus and products to match what people actually want to eat, not just what they say they should eat.

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

  • Goes beyond stated preferences with an AI follow-up interview that reconstructs the respondent's actual last meal decision, surfacing real behavior instead of self-reported ideals
  • Combines a matrix, constant-sum, and max-diff exercise so we can triangulate what really drives choice — price, health, convenience, or taste — rather than relying on a single ranking question
  • Every AI-driven follow-up runs on transparent, reviewable prompts and produces automated per-response quality scoring plus an auto-generated report, unlike static form builders
  • Includes context questions (household cooking size, novelty-seeking, cuisine breadth) so food brands and meal-kit teams can segment findings by real usage patterns, not just demographics

Jotform

Picnic Food Preference Form Template

A fielding-ready static form built for a specific event use case (picnic planning) rather than general everyday food-decision research. It's easy to deploy and customize visually but is scoped narrowly and not designed to probe the reasoning behind choices. Useful as a lightweight intake form, not a decision-driver study.

What it does well

  • Ready-to-use, easily customizable form builder
  • Simple, low-friction respondent experience for casual event planning

Where it falls short

  • No adaptive follow-up questioning — collects only fixed, self-reported answers
  • No mechanism to weigh or trade off decision drivers like price, health, or convenience
  • No automated scoring or synthesized reporting of responses

SurveySparrow

Sample Food Preference Questionnaire Template | Uncover Dietary Trends

A fielding-ready conversational-style survey template aimed at general dietary trend discovery. It covers preference and habit questions in a friendly chat-like format but is still a fixed-question instrument. Good for broad trend snapshots rather than reconstructing specific real decisions.

What it does well

  • Conversational, chat-style question flow that feels approachable
  • Positioned specifically around food/dietary trend topics

Where it falls short

  • No adaptive AI interviewing — all questions are pre-set with no follow-up probing
  • No structured trade-off tools (e.g., points allocation or max-diff) to quantify what matters most
  • No transparent prompt methodology or automated report generation

QuestionPro

Food survey questions | Food-related survey questions & template

A category page/template collection of food and fast-food restaurant survey questions rather than a single polished, ready-to-field instrument. Useful as a question bank for restaurant-style feedback, but it reads more like a reference library than a cohesive decision-driver survey. Good for teams wanting to build their own from parts.

What it does well

  • Broad library of food and restaurant-specific question examples
  • Backed by an established survey platform with reporting and distribution tools

Where it falls short

  • Presented as a question bank/category page, not a single cohesive fielding-ready template
  • No adaptive AI follow-up to reconstruct an actual recent meal decision
  • No built-in trade-off measurement (constant-sum/max-diff) for ranking decision drivers

Typeform

Consumer Preference Survey Template

A general-purpose consumer preference template that could be adapted for food brands, but it is not food-specific and covers preference topics broadly rather than everyday meal decisions. Its polished conversational UI is a strength, though the content isn't purpose-built for food, cuisine, or meal-choice research.

What it does well

  • Clean, on-brand conversational interface known for high completion rates
  • Flexible template that can be adapted across product categories

Where it falls short

  • Not food-specific — no built-in cuisine, meal-timing, or food-attribute questions
  • Static question flow with no adaptive AI interview to probe an actual recent decision
  • No transparent prompt logic or automated per-response quality scoring

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

Use this templateStart free