Q01
Message
All templatesIndustry-Specific
Online Bookstore Customer Experience Survey
Tracks how readers discover, buy, and receive books from your online store — covering selection, pricing, delivery, and format preferences — with an AI follow-up that digs into the story behind their most recent order and their recommendation score. Built for bookstore teams optimizing catalog, fulfillment, and retention.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
12 questions · ~7 min
Q02
Multiple ChoiceRequired
In the last 3 months, how many books have you purchased from (Replace with store name)?
- None
- 1-2
- 3-5
- 6-10
- More than 10
Q03
Multiple Choice
What was your most recent purchase mainly for?
- My own reading
- A gift for someone else
- A book club or class
- Research or work
- Collecting (rare/special editions)
- Other
Q04
MatrixRequired
Thinking about your recent orders, how would you rate each of the following?
6 rows × 5 columns
- Book selection and variety
- Pricing
- Website or app ease of use
- Delivery speed
- Condition of books on arrival
- +1 more
Columns: Poor · Fair · Good · Very good · Excellent
Q05
Opinion ScaleRequired
How likely are you to recommend (Replace with store name) to a friend or fellow reader?
Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q06
AI Interview
Anchor on the respondent's recommendation score and their most recent order: reconstruct what they ordered, how discovery/checkout/delivery actually went, and what specifically drove the score. If the score is low (0-6), probe the single worst moment in that order and what would have fixed it. If high (9-10), probe what specifically earned the loyalty so it can be reinforced elsewhere.
Q07
Best–Worst Trade-off (MaxDiff)Required
Which of these improvements would make the biggest difference to your experience? Pick your most and least important each round.
- Faster delivery
- Lower prices
- Wider book selection
- Better personalized recommendations
- Easier returns/exchanges
- More formats (ebook, audiobook)
- A loyalty/rewards program
- Sturdier packaging
- Live chat support
Pick best & worst per setBest:Most important to meWorst:Least important to me
Q08
Point Allocation
Imagine you had 100 points to spend across formats based on how you actually buy books today. How would you split them?
- Physical/print books
- E-books
- Audiobooks
- Rare or collectible editions
Allocate 100 points
Q09
Multiple Choice
What device do you most often use to browse or buy books from us?
- Desktop or laptop computer
- Phone browser
- Phone app
- Tablet
- Other
Q10
Long Text
What's one thing we could change to make your next order better?
Q11
Multiple Choice
What is your age range?
- Under 18
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65+
- Prefer not to say
Q12
Message
That's everything — thank you for the detailed feedback! We'll use your answers to improve our selection, pricing, and delivery experience for readers like you.
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 anchors specifically on the respondent's recommendation score and their most recent order, digging into the real story behind that purchase rather than stopping at rating scales
- Combines quantitative structure (multiple choice, matrix ratings, opinion scale, max-diff, and a constant-sum format-allocation exercise) with open-ended and conversational elements for a fuller picture of buying behavior
- Captures format preference trade-offs directly (constant-sum across formats) and device/channel usage alongside delivery and pricing perceptions, giving catalog and fulfillment teams actionable, cross-cut data
- Opens and closes with natural chat-style messages that make the survey feel like a conversation with the store rather than a static form, which can support completion and honesty on the open-ended feedback question
QuestionPro
University Bookstore Online Survey TemplateThis is a static, fielding-ready template built for a university bookstore context rather than a general online bookstore retailer, so questions likely emphasize course materials and campus buyers over general trade-book customers. It's a legitimate comparable in the bookstore survey space, but its scope is narrower and audience-specific compared to a general retail bookstore experience survey.
What it does well
- Purpose-built for a bookstore audience, so question wording and flow are likely tailored to book-buying behavior
- Comes from an established survey platform with broad template library support and standard question types
- Ready to deploy as-is for teams wanting a quick, no-setup bookstore survey
Where it falls short
- No adaptive AI follow-up interview — respondents can't be probed further on their most recent order or recommendation score beyond fixed questions
- No voice AI interview option or guided screen-share tasks for deeper qualitative insight
- As a static form, there's no per-response quality scoring or transparent prompt methodology to review
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
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