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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
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
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
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
When you're deciding what to eat, how important is each of the following?
- Taste and flavor
- Health / nutrition
- Price
- Convenience / prep time
- Sustainability or ethical sourcing
- +1 more
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
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
How willing are you to try an unfamiliar dish or ingredient you've never had before?
How satisfied are you with the food options currently available to you day-to-day (grocery, delivery, restaurants nearby)?
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.
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
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
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 TemplateA 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 TrendsA 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 & templateA 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 TemplateA 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
Frequently asked questions
What questions are in the “Everyday Food Preference & Decision Drivers Survey” template?
The template includes 13 ready-to-use questions, starting with: “Hi! We'd love to understand your everyday food choices — what you like, what you avoid, and what actually tips the scale…” · “Which of these best describes how you eat most of the time?” · “Which cuisines do you genuinely enjoy eating — not just tolerate? Select all that apply.”. The full set is previewed above, and every question is editable.
How long does this survey take to complete?
Respondents typically finish the 13 questions in about 7 minutes.
Can I customize this template?
Yes — every question, answer option, and the ordering is editable before you launch. You can add or remove questions, or ask the AI editor to rework the survey around your research goal.
Is this template free to use?
Yes. Open it in the editor and start customizing right away — no account required to try it, and the free plan covers launching your survey.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies on similar topics.
Client Project Satisfaction & Delivery Review
Measures how satisfied clients or stakeholders are with a completed or in-flight project — covering scope clarity, timeliness, budget adherence, communication, and deliverable quality — with an AI follow-up that digs into the real story behind the overall rating instead of a generic star score. Built for agencies, consultancies, and internal project teams running post-project or milestone reviews.
View templateOnline Purchasing Habits and Decision Drivers Survey
Measures how often people shop online, what actually drives a buy-or-abandon decision, and where friction shows up in checkout, delivery, and returns. An AI follow-up interview reconstructs a real recent purchase (or near-purchase) moment instead of relying on generic satisfaction ratings, making it useful for e-commerce and retail teams diagnosing drop-off.
View templateGeneral Shopping Behavior & Demographics Survey
Maps how, where, and why people shop — channel mix, category spend, decision drivers, and core demographics — with an AI follow-up interview that reconstructs a recent real purchase decision instead of relying on stated preferences. Built for retail, e-commerce, and market-sizing teams profiling a customer base.
View templateTypical Customer Profile and Decision Drivers Survey
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.
View templateFast Food Visit Experience & Ordering Satisfaction Survey
Captures how customers actually experience a fast food visit — speed, accuracy, taste, value, and channel choice — plus which improvements would matter most and how customers think about pricing. An AI follow-up interview reconstructs exactly what happened on the respondent's most recent visit instead of relying on vague satisfaction scores. Built for restaurant operators, franchise groups, and QSR marketing teams.
View templateSupermarket Shopping Attitudes and Habits Survey
Captures how shoppers choose, evaluate, and switch between grocery stores and channels — price sensitivity, loyalty behavior, and channel mix — for retail and CPG researchers. An AI follow-up interview digs into the real story behind a shopper's most recent store choice or channel switch, beyond what a closed question can capture.
View template