Automobile Buyer Journey & Satisfaction Survey
Captures the full vehicle-purchase experience — what drove the decision, how the dealership or online process performed, and how likely buyers are to return or recommend — for auto manufacturers and dealer groups. An AI follow-up interview reconstructs the actual deciding moment, including near-misses and hesitations that closed-ended questions miss.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
What make and model did you purchase (e.g., 'Toyota RAV4')?
Was this vehicle new or used?
- New
- Used (certified pre-owned)
- Used (non-certified)
How did you complete this purchase?
- In-person at a dealership
- Mostly online, picked up in person
- Fully online with home delivery
- Private sale (not a dealership)
From this list, which factor mattered most and which mattered least when you chose this vehicle?
- Price or overall value
- Safety ratings
- Fuel efficiency or range
- Brand reputation
- Styling or design
- Technology and features
- Financing or lease terms
- Expected resale value
Overall, how satisfied were you with the vehicle-buying process (not the vehicle itself)?
Please rate the following parts of your purchase experience.
- Sales staff knowledge
- Negotiation process
- Financing or paperwork speed
- Test drive experience
- Facility or website cleanliness/usability
How likely are you to recommend this dealership (or seller) to a friend or colleague?
Reconstruct the moment the respondent decided to buy this specific vehicle: what almost made them walk away, which other vehicles or dealers they seriously compared it to, and what tipped the decision. If their rating of the buying process was mediocre or low, probe the exact point in the process (negotiation, financing, wait time) that caused friction and what would have fixed it.
How likely are you to consider this same brand for your next vehicle?
- Definitely will
- Probably will
- Not sure
- Probably won't
- Definitely won't
Which age range do you fall into?
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65+
- Prefer not to say
How do you describe your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
That's everything — thank you! Your responses will be combined with other buyers' feedback to help improve the purchase and financing process.
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
- Includes an AI follow-up interview that reconstructs the actual moment of decision, surfacing near-misses and hesitations that fixed-response questions can't capture
- Combines quantitative measures (satisfaction rating, matrix ratings of the purchase experience, recommendation likelihood) with open-ended AI probing for a fuller picture
- Uses a MaxDiff exercise to force-rank the factors that truly mattered most and least in the purchase decision, rather than relying on vague importance scales
- Captures purchase channel, vehicle condition, and brand loyalty intent alongside experience data, all wrapped in a short, respondent-friendly flow with clear opening/closing messages
SurveyMonkey
Automobile Buyer Feedback Survey TemplateThis is a fielding-ready static template covering vehicle purchase feedback, similar in topic to ours. It relies on SurveyMonkey's standard closed-ended question types (ratings, multiple choice) rather than any adaptive interviewing. It's a solid quantitative baseline but won't dig into the qualitative 'why' behind a purchase decision.
What it does well
- Purpose-built template specifically for automobile buyer feedback
- Backed by SurveyMonkey's mature survey infrastructure and distribution tools
- Easy to deploy quickly with pre-built question sets
Where it falls short
- No adaptive AI follow-up questioning — respondents get the same static questions regardless of answers
- No voice AI interview or guided screen-share task option
- No automated per-response quality scoring or transparent prompt methodology
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 from the same category.
Brand & Category Awareness Tracker
Measures unaided and aided brand awareness, familiarity, and consideration within a product category — including where awareness comes from. An AI follow-up interview digs into why certain brands come to mind first and what's blocking awareness of brands that don't, going beyond simple recall percentages.
View templateProduct Concept Appeal & Purchase Intent Test
Tests how a new product or service concept lands with your target audience — appeal, differentiation, believability, and purchase intent — plus a best-worst trade-off on concept features and a price sensitivity check. An AI follow-up interview digs into the reasoning behind purchase intent scores so you know what to fix before you build it.
View templateMarket Sizing & Category Spend Survey
Estimates the total addressable spend, current usage, and switching potential for a product or service category — combining budget allocation, price-sensitivity, and prioritization questions with an AI follow-up that reconstructs the real trigger events and decision-makers behind category spend, not just stated intent.
View templateIn-Home Product Testing Feedback Survey
For consumer packaged goods and product teams running in-home or trial-use tests. Captures actual usage behavior, attribute-level ratings, purchase intent, and what matters most to improve — with an AI follow-up interview that reconstructs a specific moment of use to explain the ratings behind it.
View templateFiber, Protein & Gut Health Claims Credibility Study
Measures how much shoppers trust functional food and beverage claims around fiber, protein, and gut health/probiotics, what evidence makes a claim believable, and where skepticism creeps in. An AI follow-up interview digs into a specific claim the respondent doubted or trusted and reconstructs what actually changed their mind. Built for brand, insights, and regulatory teams evaluating label and marketing language.
View templateGLP-1 Weight Loss Wardrobe Resizing & Shopping Habits Survey
Tracks how GLP-1 medication-driven weight change is reshaping clothing size, shopping frequency, spend allocation, and what people do with ill-fitting garments. Built for apparel and resale researchers, with an AI follow-up interview that digs into the emotional and financial decisions behind buying new versus altering or holding onto old clothes.
View template