Q01
Message
All templates
Shipping Request Experience & Fulfillment Survey
Measures how smoothly a customer's shipping or freight request was handled — from initial quote through final delivery — for logistics, courier, and freight companies. An AI follow-up interview digs into the specific moment that caused the most friction, going beyond satisfaction scores to what actually happened.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
13 questions · ~7 min
Q02
Multiple ChoiceRequired
How did you submit your most recent shipping request?
- Online form/website
- Mobile app
- Phone call
- In person at a drop-off location
- Through a broker or third party
Q03
Opinion ScaleRequired
How easy was it to submit your shipping request and get a quote?
Scale: 1 – 7
Min:Very difficultMax:Very easy
Q04
Rating ScaleRequired
How would you rate the clarity of the pricing you were quoted (no surprise fees, clear breakdown)?
Range: 1 – 5
Min:Very unclearMax:Very clear
Q05
Multiple ChoiceRequired
Did your shipment arrive by the date or window originally promised?
- Yes, on time or early
- Slightly late (1-2 days)
- Significantly late (3+ days)
- Still hasn't arrived
- Not applicable / pickup only
Q06
Multiple ChoiceRequired
What was the condition of your shipment upon arrival?
- Arrived in perfect condition
- Minor damage to packaging only
- Damage to contents
- Item(s) missing
- Not yet delivered
Q07
MatrixRequired
How much do you agree with each statement about this shipping request?
4 rows × 5 columns
- Tracking updates were accurate and timely
- Customer support was easy to reach when needed
- I was told about delays or issues proactively, not after asking
- The final cost matched the original quote
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q08
Opinion ScaleRequired
How likely are you to use this carrier for a shipping request again?
Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q09
AI Interview
Reconstruct the single moment in this shipping request that caused the most friction or the most delight — whether that's the quote, pickup, tracking, a delay, or the delivery itself. Ask what exactly happened, what the respondent expected instead, and how they found out about any problem. If they reported damage or a missed date, probe how it was resolved and whether that resolution felt fair.
Q10
Multiple Choice
What was the main reason you chose this carrier for this shipment? (Replace with your own carrier/brand context if relevant)
- Price
- Speed of delivery
- Past positive experience
- Availability/coverage in my area
- Recommended by someone else
- It was the only option offered
Q11
Long Text
Is there anything else about your shipping request — good or bad — you'd like us to know?
Q12
Multiple Choice
Was this shipping request for personal or business use?
- Personal
- Business
- Prefer not to say
Q13
Message
That's everything — thank you for the detailed feedback! We use responses like yours to fix delivery bottlenecks and hold our carriers and support teams accountable.
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 a satisfaction score with an AI follow-up interview that reconstructs the single moment of friction in the shipping request, uncovering what actually happened rather than just how it felt
- Covers the full journey — quote clarity, promised vs. actual delivery date, shipment condition on arrival, and likelihood to reuse the carrier — in one structured flow
- Includes a matrix question for multi-statement agreement plus an open-ended long-text question so customers can add context the scaled questions miss
- Separates personal vs. business use and reason for carrier choice, giving logistics and freight teams segmentation data a static intake form doesn't collect
Jotform
Shipping Request Form TemplateThis is a static intake form for collecting shipping request details (sender/receiver info, package specs, etc.), not a post-shipment experience survey. It's built for operational data capture rather than measuring satisfaction or friction points. Useful as a request-submission form but doesn't overlap with a feedback/CX use case.
What it does well
- Drag-and-drop form builder with wide field-type and integration options
- Fast to deploy for capturing shipment/logistics details from customers
- Part of a large template library so it can be customized for adjacent logistics workflows
Where it falls short
- No adaptive AI follow-up questioning — it's a fixed form with no ability to probe deeper on a specific friction point
- No built-in post-delivery satisfaction, delivery-window, or condition-on-arrival questions since it's designed as a submission form, not a feedback survey
- No automated quality scoring or auto-generated insight reports
SurveySparrow
Shipping Request Form Template | Shipment Request FormAlso framed as a shipment request/intake form rather than a post-delivery experience survey, though SurveySparrow's conversational form style makes it feel more chat-like than a typical form. It can collect shipping details in a friendlier flow but isn't designed to measure delivery outcomes or friction after the fact.
What it does well
- Conversational, chat-style form UI that can feel more engaging than a traditional field-by-field form
- Templated and quick to launch for capturing shipment request details
- Mobile-friendly presentation typical of SurveySparrow's product
Where it falls short
- No adaptive AI interviewing — logic branching in SurveySparrow is rule-based, not a dynamic AI follow-up on a specific friction moment
- Template is oriented toward request intake, not measuring quote clarity, delivery-window accuracy, shipment condition, or reuse likelihood
- No transparent AI prompt visibility or automated per-response quality scoring
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.