Q01
Message
All templates
Online Retailer Shopping Experience Evaluation Survey
Measures how customers rate an online retailer on price, selection, site experience, shipping, and service, plus what drives loyalty versus defection to a competitor. An AI follow-up interview reconstructs the specifics of a recent order to explain the ratings instead of leaving them as abstract scores.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
14 questions · ~7 min
Q02
Multiple ChoiceRequired
In the last 6 months, how often have you made a purchase from this retailer?
- This was my first purchase
- 1-2 times
- 3-5 times
- 6 or more times
Q03
Multiple Choice
How did you make your most recent purchase?
- Website on a computer
- Mobile website
- Mobile app
- In-store, but ordered online for pickup
- Phone or chat with a representative
Q04
Opinion ScaleRequired
Overall, how satisfied were you with your most recent order?
Scale: 1 – 7
Min:Very dissatisfiedMax:Very satisfied
Q05
MatrixRequired
How would you rate this retailer on each of the following?
6 rows × 5 columns
- Price competitiveness
- Product selection and variety
- Website or app ease of use
- Shipping speed
- Return or exchange process
- +1 more
Columns: Poor · Fair · Good · Very Good · Excellent
Q06
Best–Worst Trade-off (MaxDiff)Required
When deciding where to shop online, which of these factors matter most and least to you?
- Price
- Shipping speed and cost
- Product selection and variety
- Return and exchange policy
- Website or app ease of use
- Customer reviews and ratings
- Customer service quality
- Loyalty or rewards program
Pick best & worst per setBest:Most importantWorst:Least important
Q07
Opinion ScaleRequired
How likely are you to recommend this retailer to a friend or colleague?
Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q08
AI Interview
Reconstruct the respondent's most recent order in concrete detail — what they bought, what went right or wrong at each step (browsing, checkout, shipping, delivery, returns), and how that experience shaped their satisfaction and recommendation scores. If they gave a low score, pin down the single moment that most hurt the experience and what specifically would need to change to raise it. If they gave a high score, probe whether that's typical or a standout exception.
Q09
Multiple ChoiceRequired
Thinking about your next purchase of this type, what's most likely?
- Definitely will keep shopping here
- Probably will keep shopping here
- Not sure — might compare with (Replace with competitor A)
- Probably will switch to a competitor
- Definitely will switch to a competitor
Q10
Long Text
What's the one thing this retailer could do to improve your experience?
Q11
Multiple Choice
What is your age range?
- Under 18
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65 or older
- Prefer not to say
Q12
Multiple Choice
What is your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Q13
Dropdown
What is your annual household income?
- Under $25,000
- $25,000-$49,999
- $50,000-$74,999
- $75,000-$99,999
- $100,000-$149,999
- $150,000 or more
- Prefer not to say
Q14
Message
That's everything — thank you for the detailed feedback! Your responses will feed directly into a report the retailer's team uses to fix friction points and improve the shopping experience.
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 satisfaction and NPS scores by using an AI follow-up interview to reconstruct the specifics of the respondent's most recent order — what they bought, how the process went, and why they rated it the way they did.
- Combines standard quantitative measures (purchase frequency, channel used, satisfaction, a ratings matrix across price/selection/site/shipping/service, and a MaxDiff on shopping priorities) with open-ended context that explains the numbers instead of leaving them abstract.
- Captures forward-looking loyalty signals (likelihood to repurchase, likelihood to recommend) alongside a direct improvement question, giving both a diagnosis and a lever for action.
- Closes with demographic questions (age, gender, income) for segmentation, framed by transparent chat messages that set expectations at the start and close of the survey.
QuestionPro
Online Retailer Evaluation Survey TemplateA fielding-ready template covering standard retailer evaluation metrics like pricing, selection, and service. It's a conventional static survey builder template rather than an interview-style tool, so all questions are fixed in advance. Good for quick deployment but doesn't adapt based on individual responses.
What it does well
- Ready-to-field template with established survey-platform infrastructure
- Likely covers core retailer evaluation dimensions (price, selection, service)
- Backed by a mature survey platform with reporting tools
Where it falls short
- No adaptive AI follow-up to probe into a specific recent order — ratings stay abstract
- No per-response quality scoring
- No published methodology or transparent prompt logic
Jotform
Online Shopping Evaluation Form TemplateThis is a form-builder template, closer to a customizable data-collection form than a research-grade survey instrument. It's easy to edit and deploy but is built for simple field capture, not structured behavioral or attitudinal research. No interview or conversational follow-up capability is present.
What it does well
- Highly customizable drag-and-drop form fields
- Easy to embed or share as a standalone form
- Simple setup for basic feedback collection
Where it falls short
- Static form fields only — no adaptive follow-up questioning
- No automated quality scoring of responses
- No mechanism to reconstruct order-level detail behind a rating
SurveyMonkey
Online Shopping Survey: Questions & TemplatePresented partly as a question bank/guide alongside a template, focused on shopping attitudes broadly rather than a single retailer's performance specifically. Useful as a starting question list, but it is a static, pre-set survey rather than a dynamic interview experience.
What it does well
- Established survey platform with broad question-bank guidance
- Covers general online shopping attitudes and behavior
- Backed by strong reporting and analytics tooling
Where it falls short
- No adaptive AI interview to dig into a specific recent order
- No voice-based or guided-task response options
- No transparent prompt-level methodology published
SurveySparrow
Online Shopping Survey TemplateA conversational-style survey template that presents questions in a chat-like UI, which improves completion experience over plain forms. However, the conversational format is scripted rather than adaptive — it doesn't generate follow-up questions based on what a respondent actually says.
What it does well
- Conversational chat-style UI for a friendlier respondent experience
- Ready-made template for online shopping feedback
- Mobile-friendly presentation
Where it falls short
- Conversational UI is not the same as adaptive AI follow-up questioning — flow is pre-scripted
- No automated per-response quality scoring
- No reconstruction of order-level specifics behind satisfaction scores
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.