Collect customer information with AI

Static web forms frustrate customers and kill completion rates. Long forms with 8+ fields see 60-70% abandonment before submission. Customers hate filling out rigid forms that don't adapt to their situation, asking irrelevant questions or requiring information they don't have readily available. Form abandonment costs eCommerce businesses thousands in lost leads, incomplete refund requests, and missed survey responses. Support teams then spend hours following up via email asking for the missing information that customers didn't provide on the form.

Zipchat's Collect Customer Information ability replaces static forms with intelligent conversations. You configure the AI to ask a specific series of questions conversationally, adapting based on customer responses. Instead of a refund form with 10 fields, the AI asks "What's your order number?", then "What's the reason for your refund?", collecting answers through natural dialogue. The AI retrieves relevant information from the conversation context (like order details already discussed), so customers don't re-enter data they've already provided. Once complete, the entire conversation with structured answers gets forwarded to your team inbox or automation platform like Make.com for processing.

Unlike rigid forms that feel impersonal and intimidating, conversational information collection feels helpful and adaptive. The AI can skip irrelevant questions based on earlier answers, explain why information is needed, and handle unclear responses by asking follow-up clarifications. A customer saying "I'm not sure what my order number is" triggers the AI to search by email instead. Someone giving incomplete information gets a gentle prompt: "Just to confirm, you'd like a refund for the damaged item, correct?" This flexibility dramatically increases completion rates.

Merchants using conversational information collection see 45-60% higher completion rates compared to static forms and 20-30 hours monthly time savings from receiving complete, organized information upfront. For refund requests, customer service escalations, product customization orders, or post-purchase surveys, this eliminates the back-and-forth email chains that delay resolution and frustrate everyone involved.

Refund Request with Complete Information Collection

A customer messages at 4:32 PM wanting a refund: "I want to return the sweater I ordered. It doesn't fit right."

The AI recognizes this as a refund inquiry and begins collecting necessary information conversationally: "I can help you with that return. Let me get a few details. What's your order number?" Customer responds: "#7283." AI continues: "Got it, order #7283 for the cashmere sweater ($89). You mentioned it doesn't fit right, can you tell me more? Is it too small, too large, or something else?" Customer: "Too small, I ordered medium but should have gotten large."

The AI asks: "Would you prefer a refund or an exchange for size large?" Customer: "Refund please." AI confirms: "Perfect. Final question: do you still have the original packaging and tags?" Customer: "Yes, everything's still in the box." The AI concludes: "Great! I have everything I need. Here's a summary of your refund request: Order #7283, Cashmere Sweater (Medium), Reason: Size too small, Item condition: Unworn with tags. I'm sending this to our returns team now. You'll receive a return shipping label via email within 2 hours at the address on your order. Refund will process once we receive the item (typically 5-7 business days). Reference: RET-7283."

A static refund form would have required the customer to navigate to a returns page, enter their order number manually, select from dropdown menus, and submit without any confirmation or guidance. The conversational approach collected complete information in 90 seconds through natural back-and-forth, provided clear next steps, and created an organized case for the returns team. Completion rate: 95% versus typical 40-50% for web forms.

Product Customization Order with Complex Requirements

A customer browsing custom furniture messages on WhatsApp at 7:18 PM: "Can I order a custom desk? I have specific dimensions I need."

The AI begins the customization collection process: "Absolutely! I'll help you configure your custom desk. Let's start with dimensions. What length do you need?" Customer: "60 inches long." AI: "Perfect, 60 inches. How about depth?" Customer: "Maybe 28 inches? Is that standard?" AI: "28 inches works great, that's actually our most popular depth for home offices. Now, what height do you prefer? Standard is 29 inches, or we can adjust." Customer: "Standard is fine."

AI continues: "Great! Now for finish options. We offer walnut, oak, or white lacquer. Any preference?" Customer: "Walnut." AI: "Beautiful choice. Do you need any add-ons? Cable management cutout, keyboard tray, or drawer?" Customer: "Cable management would be good, no drawer needed." AI summarizes: "Excellent! Here's your custom desk: 60"L x 28"D x 29"H, Walnut finish, Cable management cutout included. Price: $875 (includes custom sizing). Lead time is 4-5 weeks for custom builds. Ready to place this order?" Customer: "Yes, let's do it." AI: "Perfect! I'm sending your specifications to our workshop team now with reference CUSTOM-8847. They'll send detailed drawings for approval within 48 hours before starting production. You'll receive an invoice via email shortly."

A custom furniture form with 15 fields and technical terminology would intimidate customers and cause abandonment. The conversational approach guided the customer through each decision, provided context (like "28 inches is most popular"), allowed questions mid-process, and created confidence in the purchase. The furniture workshop receives complete, organized specifications instead of incomplete form submissions missing critical details.

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• Navigate to Dashboard → "AI Training" → "Prompt & Tools" → "Additional Instructions"
• Define the specific information collection scenarios you want to automate (refund requests, custom orders, surveys, contact forms, etc.)
• Write prompts that specify: the trigger phrase or context that starts collection, the exact questions to ask in sequence, required versus optional information, and how to handle unclear or incomplete responses
• Configure where completed information should be sent
• Set up conditional logic if needed: "If customer says item is defective, also ask for photos. If they want exchange, ask for preferred size/color."
• Test the collection flow by going through the conversation as a customer to verify the AI asks questions in logical order, handles unclear answers appropriately, and delivers complete information to your destination

How is this different from just using a regular web form?

Conversational collection has 3 key advantages: 1) Adaptive questioning based on responses (skips irrelevant questions, asks clarifying follow-ups), 2) Natural language processing (customers can answer in their own words, not forced into dropdown options), 3) Context awareness (AI remembers information from earlier in conversation, doesn't ask redundant questions). A customer who already mentioned their order number won't be asked for it again. Someone giving a vague answer like "It's broken" gets asked "Can you describe what's broken specifically?" Forms can't do this, resulting in 40-60% lower completion rates than conversational collection.

Can the AI validate responses to ensure information quality?

Yes. You can configure validation rules in your prompts. For email collection: "Verify the email format is valid before proceeding." For order numbers: "Confirm the order number exists in Shopify before continuing." For quantity questions: "If customer provides a number outside reasonable range, ask for confirmation." The AI can also ask clarifying questions when answers seem unclear or incomplete: "You mentioned the item is damaged. Can you describe the damage so I can route this correctly?" This validation ensures your team receives complete, actionable information instead of garbage data.

Can I use this for post-purchase surveys or feedback collection?

Absolutely, especially via WhatsApp. This is highly effective for surveys because conversational format feels less intrusive than email surveys. Configure the AI to ask: "Quick question - how would you rate your shopping experience today? 1-5 stars." Then based on the response: "Thanks! What did you like most about your experience?" or "I'm sorry to hear that. What could we improve?" Conversational surveys get 3-4x higher response rates than email surveys because they happen in the moment during an already-active conversation. The AI collects feedback, thanks the customer, and forwards insights to your team.

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