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Market Segmentation & Customer Needs Discovery Survey

Groups your customers into meaningful segments by combining behavioral usage patterns, need-based attitudes, purchase-driver trade-offs, and demographics. Built for marketers and researchers building or refreshing a segmentation model. The AI follow-up interview digs into the 'why' behind each respondent's top purchase driver so segments are grounded in real reasoning, not just survey scores.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

15 questions · ~8 min
Q01
Message

Hi! We're trying to better understand the different types of customers we serve so we can serve each of you better. This will take about 6-8 minutes, and there are no right or wrong answers — just tell us how you actually think and shop.

Q02
Multiple ChoiceRequired

In the last 30 days, how often have you purchased or used (Replace with your product/category, e.g., 'meal-kit delivery services')? (Template note: swap in the category you're segmenting customers within.)

  • Not at all
  • Once
  • 2-3 times
  • 4-6 times
  • More than 6 times
Q03
MatrixRequired

How much do you agree with each statement about how you typically shop in this category?

6 rows × 5 columns
  • I actively look for the lowest price before buying.
  • I want products or services tailored to my specific needs.
  • I trust recommendations from people I know more than advertising.
  • I usually stick with brands I already know and trust.
  • I enjoy discovering and trying new brands or products.
  • +1 more
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q04
Best–Worst Trade-off (MaxDiff)Required

For each set, pick which factor matters most and which matters least when you decide what to buy in this category.

  • Price
  • Product quality
  • Convenience or ease of use
  • Brand reputation
  • Customer service
  • Sustainability or ethical practices
  • Availability where I shop
Pick best & worst per setBest:Matters mostWorst:Matters least
Q05
Point AllocationRequired

You have 100 points to allocate across the ways you typically discover new products or brands in this category. Give more points to the sources that influence you more.

  • Social media
  • Search engines
  • Word of mouth from friends or family
  • In-store discovery
  • Email or newsletters
  • TV or streaming ads
Allocate 100 points
Q06
Multiple ChoiceRequired

Where did you make your most recent purchase in this category? (Replace with your brand/category as needed.)

  • Company website
  • Retail store
  • Third-party online marketplace (e.g., Amazon)
  • Mobile app
  • Other
Q07
Opinion ScaleRequired

How likely are you to recommend (Replace with your brand/product) to a friend or colleague?

Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q08
AI Interview

Probe the reasoning behind the respondent's top-ranked purchase factor from the trade-off exercise: ask for a specific, recent purchase where that factor decided the outcome, and what would have changed their mind. Then connect it to their recommendation score — if low, uncover the unmet need behind it; if high, uncover exactly what need is being met so well. Listen for language that reveals which underlying segment (price-driven, convenience-driven, quality-driven, novelty-seeking, loyalist) they naturally fall into, and note any tension between their stated attitudes and their actual behavior.

Q09
Message

Almost done — just a few quick background questions to help us compare groups. All of these are optional.

Q10
Multiple Choice

Which age range do you fall into?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65+
  • Prefer not to say
Q11
Multiple Choice

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q12
Multiple Choice

What is your total annual household income?

  • Under $30,000
  • $30,000-$59,999
  • $60,000-$99,999
  • $100,000-$149,999
  • $150,000 or more
  • Prefer not to say
Q13
Multiple Choice

Which best describes your current employment status?

  • Employed full-time
  • Employed part-time
  • Self-employed
  • Unemployed
  • Student
  • Retired
  • Prefer not to say
Q14
Short Text

What city or region do you live in? (Optional)

Q15
Message

Thank you for sharing all of this! Your answers will be combined with other respondents' to build customer segments that shape our products, messaging, and offers — nothing here is tied to your individual identity.

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

  • Combines behavioral frequency, need-based attitude statements, MaxDiff-style trade-offs, and a constant-sum discovery-channel exercise in one flow, rather than a single question type
  • Includes an AI follow-up interview that probes the reasoning behind each respondent's top-ranked purchase driver, so segments are grounded in stated reasoning, not just ranked scores
  • Pairs quantitative segmentation inputs (usage, attitudes, trade-offs, demographics) with a conversational open-ended layer, then rolls everything into an auto-generated report for comparing groups
  • Uses plain chat-style intro/outro messages to keep respondents oriented, alongside standard demographic classification questions for segment profiling

SurveySparrow

Customer Needs Survey Template

A fielding-ready template focused on capturing customer needs and satisfaction rather than full segmentation. It's built on SurveySparrow's conversational form format, which is friendlier than a plain grid but still relies on fixed question sets. Good for a narrower needs-assessment use case than a full segmentation model.

What it does well

  • Conversational, chat-style UI likely improves completion rates versus a static grid
  • Purpose-built around customer needs rather than a generic template repurposed for segmentation
  • Ready to field with minimal setup

Where it falls short

  • No adaptive AI follow-up to probe why a respondent gave a particular answer — all questions are pre-scripted
  • No built-in behavioral usage + trade-off + demographic segmentation structure in one instrument
  • No automated per-response quality scoring or transparent prompt methodology disclosed

Typeform

Market Segmentation Survey Template

The most directly comparable template, aimed at the same market segmentation use case with a clean, conversational one-question-at-a-time format. It likely covers demographics and usage patterns but, like other static builders, cannot dynamically dig into a respondent's reasoning. Best suited to teams who want a quick, polished segmentation survey without custom logic.

What it does well

  • Purpose-built for market segmentation, so question flow is likely aligned to that goal
  • Polished, distraction-minimizing one-question-at-a-time interface
  • Easy to customize and deploy quickly

Where it falls short

  • No adaptive AI interview to explore the 'why' behind a respondent's top purchase driver — responses stay at the level of fixed answer choices
  • No automated quality scoring of individual responses
  • No transparent, published prompt/methodology layer since it's a static question template

QuestionPro

Supermarket Shopping Attitudes Survey Template

A category-specific attitudinal template focused on supermarket shopping behavior, useful as a reference for needs-based attitude questions but narrower in scope than a general-purpose segmentation model. It's a static template requiring adaptation to other categories rather than a flexible, cross-category segmentation instrument. No interview or follow-up component is present.

What it does well

  • Detailed, category-specific attitude statements tailored to supermarket shopping
  • Backed by QuestionPro's established survey logic and question library
  • Ready to field for grocery/retail-specific research

Where it falls short

  • Locked to a single retail category rather than a general, replaceable segmentation framework
  • No adaptive AI follow-up or voice interview option to explore reasoning behind trade-off choices
  • No automated per-response quality scoring or transparent prompt disclosure

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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