Retail Website Shopping Experience Evaluation
Evaluates how easily customers can find products, complete purchases, and navigate your retail website — covering search, checkout, speed, and design. An AI follow-up interview digs into what happened during the visit itself, especially for shoppers who hesitated or abandoned their purchase, for teams optimizing conversion and usability.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
What was the main reason for your visit to our website today?
- Browse products with no specific plan to buy
- Look for a specific product
- Complete a purchase I'd already decided on
- Check an order or account details
- Compare prices or options before buying elsewhere
How easy was it to find what you were looking for on our website?
How would you rate the overall visual design and layout of our website?
Please rate each part of your experience on our website today.
- Page load speed
- Search and filtering tools
- Checkout process
- Mobile browsing experience
How likely are you to recommend our website to a friend or colleague?
Did you complete the purchase you intended to make today?
- Yes, I completed my purchase
- No, I'm still deciding
- No, I gave up partway through
- I wasn't trying to make a purchase
Reconstruct what actually happened during this website visit, anchored on the respondent's stated purpose and purchase outcome. If they gave up or are still deciding, probe exactly where in the journey (search, product page, cart, checkout, shipping cost, payment) things broke down and what would have changed their mind. If they completed a purchase but gave a low recommendation score, dig into what almost stopped them. If everything went smoothly, ask what one thing would make the site even better.
Which device did you primarily use for this visit?
- Smartphone
- Tablet
- Desktop or laptop computer
What one change would most improve your experience on our website?
Which age range best describes you?
- Under 18
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65 or older
How do you describe your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
That's everything — thank you for sharing your experience! Your answers go directly to our web team to guide upcoming improvements to search, checkout, and site speed.
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 static ratings with an AI follow-up interview that reconstructs what actually happened during the visit, digging deeper with shoppers who hesitated or abandoned their purchase
- Combines quantitative measures (opinion scale for findability, rating for visual design, matrix for overall experience) with open-ended and interview-based qualitative insight in one flow
- Captures purchase completion, device type, and visit intent as structured data points that can be used to segment and trigger the deeper AI conversation
- Produces an automated report from the responses, so teams get synthesized findings rather than just raw answer counts
QuestionPro
Retail website customer evaluation survey questions + Sample questionnaire templateThis is a sample questionnaire/template page covering standard retail website evaluation topics like navigation, checkout, and satisfaction. It reads as a static question set to copy or adapt within QuestionPro's survey tool rather than an adaptive interview experience. Useful as a question-bank reference but not a fielding-ready conversational instrument.
What it does well
- Directly focused on retail website evaluation, so questions map closely to this use case
- Backed by an established survey platform with broad question-type support
- Likely includes example questions across multiple experience dimensions
Where it falls short
- No adaptive AI follow-up to probe why a shopper hesitated or abandoned checkout
- No voice AI interview option
- No published methodology on how questions were validated or scored
Jotform
Website Evaluation Form TemplateA general-purpose website evaluation form built on Jotform's drag-and-drop builder, not specific to retail or purchase behavior. It's a ready-to-use static form template, easy to customize visually, but lacks any retail-checkout or conversion-specific framing. Good for basic site feedback collection rather than deep usability diagnosis.
What it does well
- Easy visual customization via Jotform's drag-and-drop builder
- Quick to deploy as a generic feedback form
- Flexible field types for general website feedback
Where it falls short
- Not tailored to retail purchase journeys or checkout abandonment
- Purely static questions with no adaptive follow-up logic
- No mechanism to automatically score response quality or generate an interview-based report
Typeform
Website Evaluation FormTypeform's conversational-style form format makes for a pleasant one-question-at-a-time UI, but the underlying content is still a fixed sequence of general website feedback questions. It isn't retail-specific and doesn't adapt based on answers. Best suited for lightweight, broad site feedback rather than conversion-focused diagnosis.
What it does well
- Polished, conversational one-question-at-a-time interface
- Simple to launch for general website feedback
- Visually appealing, brand-friendly design
Where it falls short
- No AI-driven follow-up questioning based on individual answers
- Not specifically built for retail checkout/abandonment scenarios
- No automated quality scoring or generated analysis report
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.
Mailchimp Customer Check-In & Satisfaction Survey
A periodic pulse check for Mailchimp customers covering feature usage, satisfaction with core tools, support experience, and likelihood to recommend. An AI follow-up interview digs into the story behind each customer's recommendation score, surfacing the specific moment or friction point that drove it instead of a flat number.
View templateOpen Customer Comments & Feedback Deep-Dive Survey
Turns a single piece of customer feedback into a structured signal — capturing what prompted the comment, rating the specific aspects of the experience behind it, and using an AI follow-up interview to reconstruct the exact moment, cause, and fix. Built for support, CX, and product teams triaging open-ended comments.
View templateCustomer Product & Service Review Collection Survey
Gathers star ratings, structured feedback on quality, service, and value, and a publishable written review from recent customers. An AI follow-up interview digs into the specific moment behind their score, turning vague ratings into quotable, credible reviews for your site, app store, or marketplace listings.
View templateSmall Business Customer Satisfaction & Loyalty Survey
Tracks how satisfied customers are with a local or small business — overall experience, specific touchpoints like staff and value, and likelihood to return or recommend — with an AI follow-up that digs into the real story behind each customer's score. Built for owners and managers who want concrete, actionable feedback in under 5 minutes.
View templateCustomer Support Request Experience Survey
Captures how a recent support request went — channel used, effort required, resolution status, and satisfaction — paired with an AI follow-up that digs into what actually happened during the interaction, not just the rating given. Built for support and CX teams tracking ticket quality beyond CSAT scores.
View templateCompetitive Net Promoter Benchmark Survey
Benchmarks your Net Promoter Score against named competitors among customers who use both, then uses an AI follow-up to uncover the specific moments that widen or close the gap. Built for CX and product teams who need more than a single number — they need to know why customers would (or wouldn't) switch.
View template