Losing customers at checkout is one of the most frustrating pain points for any online business. Exit intent survey questions offer a direct way to uncover why shoppers leave items in their cart—information that otherwise stays hidden. With over 70% of online shopping carts abandoned on average [1], finding the right questions is crucial for revenue recovery and better customer experience.
This guide covers the best questions to ask when someone abandons their cart. We’ll dig into how AI-powered conversational surveys go far beyond traditional forms, and for every question, you’ll get AI follow-up prompts and branching logic to truly reveal the root cause.
Questions to reveal price sensitivity and value concerns
Price is usually the first objection shoppers mention—but it’s rarely the only factor at play. People’s decisions are shaped by perception of value, hidden fees, and competitive alternatives. Here’s how we use smart exit intent survey questions to dig deeper:
“Was there anything about the price or value that made you hesitate?”
This question gets past a simple “too expensive” answer and opens the door to qualitative feedback about affordability and perceived value.AI follow-up prompt:
“Can you share what price point you expected, or if you saw a better deal elsewhere?”
Branching: If the shopper focuses on price, prompt for their budget range or ask if a discount would change their mind.
“Did the total cost—including shipping and fees—match your expectations?”
Buyers often abandon not just because of product price, but because of surprise costs at checkout. This question surfaces those hidden objections.
AI follow-up prompt:
“Which part of the total cost felt too high—product price, shipping, or extra fees?”
Branching: If extra fees come up, the AI asks about acceptable delivery charges or payment methods.
“Were you considering a competitor at the same time?”
Directly addressing the possibility of comparison helps reveal if you’re losing shoppers to similar products with better perceived value.
AI follow-up prompt:
“What did you find different or more appealing with the competitor's offering?”
Branching: If price is mentioned, prompt about specific features or services that made the competitor stand out.
By combining open-ended questions with custom AI probing, you can unlock nuanced motivations that generic surveys overlook. According to Baymard Institute, 48% of cart abandoners cite extra costs as the primary reason—but follow-up reveals that many are referring to value mismatches, not just price [1].
Surface-level answer | AI-discovered insight |
|---|---|
“It’s too expensive.” | The customer expected free shipping on all orders over $50; a $4.99 shipping fee at checkout triggered abandonment. |
“Just browsing.” | The user wanted to use a student discount but found the process confusing and gave up. |
“Found better elsewhere.” | Competitor offered the same item but with a bonus accessory included, making their offer feel more valuable. |
Want to build tailored price and value questions? Try the AI survey generator from Specific to shape your survey prompts to your audience and product.
Shipping-focused questions that reduce abandonment
Unexpected shipping costs and vague delivery times destroy trust at checkout, causing shoppers to hesitate or ditch their carts entirely. In fact, shipping and delivery account for a whopping 22% of all cart abandonment incidents [1]. Here’s how to probe deeply in a way that feels conversational—not clinical:
“Did anything about our shipping options or costs surprise you?”
This question actively acknowledges that surprises happen, encouraging honest answers.
AI follow-up prompt:
“What about the shipping details felt unexpected—was it the cost, the timeline, or something else?”
Tone: Empathetic (“I know shipping can be frustrating—what would make it better for you?”)
“Was the estimated delivery time suitable for your needs?”
Creating urgency around “when” an order will arrive is a major reason for drop-off, especially for gifts or urgent needs.
AI follow-up prompt:
“How soon were you hoping to receive your order? Are there specific dates you needed it by?”
Tone: Empathetic and proactive—if they needed same-day, AI asks about local pickup or other alternatives.
“Have you had any past shipping issues with us?”
This question draws out emotion (“frustrated”, “let down”) and addresses lingering doubts, which can fester quietly.
AI follow-up prompt:
“Can you tell me more? If something went wrong, I’d love to know how we might fix it.”
Tone: Apologetic and action-oriented, inviting specific stories.
Generic shipping question | Conversational approach |
|---|---|
“Was shipping an issue?” | “Could you share your thoughts on our shipping options or any concerns about delivery times?” |
“Did you see the shipping info?” | “Was there anything about the shipping details or costs that felt off to you?” |
AI will quickly uncover if it’s the cost, speed, or reliability that’s the real blocker, thanks to adaptive follow-up logic set up in your in-product conversational survey widget.
Trust-building questions for hesitant shoppers
Trust barriers are rarely blurted out by shoppers—they’re often quiet and unspoken, yet they make or break purchases. Traditional surveys miss these, but conversational interviews can bring them to light:
“Did you have any concerns about the security of your payment?”
Payment hesitancy is a top-five abandonment cause, especially among new visitors.
AI follow-up prompt:
“What specifically made you hesitate—was it the payment process, the look of the checkout, or something missing?”
Tone: Professional and reassuring—remind them of certifications or trusted payment methods if appropriate.
“Were you clear about our return or refund policies?”
Easy returns boost conversion confidence—uncertainty saps it fast.
AI follow-up prompt:
“Was there anything about the policy you wish was easier to understand or more flexible?”
Branching: If concern is about speed of refund, dig into the details that would inspire confidence (timeline, process, etc).
“Did anything about our website or brand make you second-guess your purchase?”
Direct but non-accusatory, this question can prompt truth about site experience, reviews, or lack of social proof.
AI follow-up prompt:
“Can you think of something that would’ve made you feel more comfortable or trust us more?”
Tone: Reassuring and curious, never defensive.
Conversational surveys naturally invite honest, nuanced answers—shoppers feel less like they’re filling out a form and more like they’re talking to a real person. That’s why AI-powered automatic follow-up questions adapt in real time based on every response. Try this on your own with the automatic AI follow-up questions feature.
Product-related questions that prevent future abandonment
Not every cart abandoner is unhappy with your store—sometimes, the product just wasn’t the right fit, or key selection options were missing. A great exit intent survey can help address gaps before they scale:
“Did you find all the info you needed about this product?”
Open-ended to welcome any confusion about specs, legitimacy, warranty, or care instructions.
AI follow-up prompt:
“Tell me what details you were looking for and couldn’t find.”
Branching: AI drills down if size, ingredients, or instructions are mentioned.
“Were you satisfied with available options (size, color, features)?”
This addresses the silent frustration of not seeing the variant or configuration a customer wants.
AI follow-up prompt:
“Which option were you hoping for that we didn’t have?”
Branching: If a specific variant comes up, prompt about frequency of buying that kind or willingness to preorder/waitlist.
“Was there anything about the product description or images that made you unsure?”
Many shoppers leave when images, videos, or hands-on reviews are missing.
AI follow-up prompt:
“What would help build your confidence in making a purchase? More photos, real-life examples, or something else?”
Branching: If they want real reviews, the AI can prompt for which kind (star ratings, video testimonials, etc).
Missing variants often cause silent abandonment—someone wanted a different size or color, but you never find out unless you ask. AI-driven follow-ups can reveal patterns, such as frequent requests for the same new variant, so you can adjust future product offering (as demonstrated with AI survey response analysis).
Setting up your exit intent survey for success
Getting more—and better—answers starts with smart implementation. Here’s what I recommend based on what actually works in ecommerce:
Timing: Trigger surveys 2–3 seconds before typical exit behavior (mouse movement to the page edge, inactivity, or cart idle).
Placement: Use a subtle widget in the lower corner to reduce intrusion, or a center overlay for high-intent shoppers.
Frequency Controls: Limit each visitor to one survey per browsing session to prevent fatigue and annoyance.
First Question: Start short, simple, and conversational—avoid overwhelming shoppers with a form the moment they try to leave.
Language: Offer multilingual support so you catch international shoppers in their preferred language. This makes a measurable difference—40% of users are more likely to participate in their native language [2].
DO | DON'T |
|---|---|
Trigger before exit with a gentle widget | Interrupt instantly or after they fully leave |
Ask open-ended, friendly questions | Dive into a full survey form up front |
Use language and tone settings that match your brand | Default to robotic or impersonal text |
Leverage AI to adapt and personalize follow-ups | Stick to rigid, static forms for everyone |
If you’re ready to install a conversational widget that actually talks with your customers (not at them), check out the in-product conversational survey tool. The real value? AI analysis reveals not only what’s blocking one sale, but trends and patterns across all abandonments.
Turn abandonment insights into recovered revenue
You can only fix what you understand—get to the bottom of your specific customers' objections and stop losing revenue to invisible barriers. Every ecommerce brand has unique abandonment patterns, and the only
