Create your survey

Create your survey

Create your survey

Customer analysis example: how to use an AI survey template for feedback

Adam Sabla - Image Avatar

Adam Sabla

·

Sep 10, 2025

Create your survey

This customer analysis example walks you through collecting and analyzing customer feedback using AI-powered conversational surveys. By following this step-by-step guide, you'll see the complete process—from crafting an initial prompt to revealing actionable insights through customer feedback analysis. Experience how AI-driven conversations unlock deeper customer understanding.

Building your customer feedback survey with AI

Traditional survey creation is time-consuming and often feels like a chore. With the AI survey generator from Specific, you can build a research-grade survey just by describing your goal in plain language.

For a customer feedback initiative, you might start with an example prompt like:

Create a customer satisfaction survey to understand how recent users feel about our product. Include overall satisfaction, Net Promoter Score, reasons for their rating, and ask for suggestions to improve their experience.

The AI turns this direction into a structured survey template with a natural flow: satisfaction metrics, NPS, key drivers, and open-ended improvement suggestions. It also designs dynamic follow-up questions, branching logic, and response choices—making it easy for you to launch a conversational survey in minutes.

The efficiency boost is real: while email surveys average a 15%–25% response rate, conversational AI surveys like these regularly see 70%–90% completion rates, giving you richer, more representative data. [1][2]

Customer feedback survey template with dynamic follow-ups

Building on your prompt, here’s how Specific’s conversational survey logic works:

  • Overall satisfaction rating (scale 1–5): AI asks why they chose their rating, probing for context.

  • Net Promoter Score (NPS) question: “How likely are you to recommend us to a friend?”

  • NPS follow-up:

    • For low scores (0–6), AI gently uncovers pain points: “What made you choose this score? Was something missing?”

    • For high scores (9–10), AI digs into delight factors: “What did you love most?” and “What would make it even better?”

  • Open-ended improvement prompt: “What’s one thing we could do to improve your experience?”—plus AI clarifying questions to draw out specifics.

Each answer triggers tailored, real-time follow-ups (see the automatic AI follow-up questions explainer for details). This turns the static survey into an interactive, clarifying conversation—just like a sharp human interviewer.

Traditional Survey

Conversational Survey

Static form, few clarifying questions

Dynamic follow-ups uncover motivations

Higher abandonment (40%–55%) [3]

Much lower abandonment (15%–25%) [3]

Respondent gives minimal detail

AI prompts for real stories and specifics

With AI-powered logic, your survey isn’t just a questionnaire—it’s a genuine conversation that uncovers richer feedback.

Landing page vs in-product widget delivery

Delivery method affects both response rates and the quality of feedback. Specific lets you distribute surveys in two optimal ways:

Landing Page

In-Product Widget

Best for one-off requests
Share via email, chat, or direct link
Great for onboarding surveys or post-event

Collects continuous, contextual feedback
Appears natively inside your product
Triggers based on user behavior or events

Conversational survey page for broad distribution

In-product conversational survey for targeted, timely insight

Great for targeted outreach campaigns

Supports identity-based, action-based, and time-based targeting

Use a landing page to reach customers after a big release or major event. For teams wanting ongoing improvement, the in-product widget excels—show it after users complete a purchase, try a new feature, reach milestones, or at regular intervals. You can customize appearance with CSS so your widget always feels on-brand.

Targeting is precise: only survey users after they access a new feature, or trigger an interview for frequent visitors at risk of churning. This makes user feedback feel relevant and drives higher, more honest participation.

AI-powered analysis of customer feedback

Manual review of hundreds of responses is slow and error-prone—even expert analysts struggle to catch every theme. That’s why Specific’s AI survey response analysis instantly distills both quantitative and qualitative input across all customer conversations.

On an individual level, AI summarizes key takeaways. For instance:

Customer gave a 5/10 on satisfaction, mentioning "slow onboarding." AI summary: "User found product setup unclear and wants better startup guides."

The AI extracts dominant themes across all responses, like:

  • “Pricing concerns” (noted by 35% of respondents)

  • “Feature requests” for calendar integration

  • “Support issues” during account setup

Want to dig deeper? Just ask the AI:

What’s the top reason for detractors among power users? Compare to new users.

Which features get mentioned most by promoters in June?

You can create multiple analysis chats to explore retention drivers, churn signals, or new ideas—no spreadsheet wrangling required. This conversational data exploration turns piles of feedback into real organizational intelligence.

Customer analysis example: from data to decisions

Real insights from analyzing 200 customer responses:

  • Segmented findings: Power users (using product 6+ months) mainly requested advanced analytics and APIs, while new users wanted clearer onboarding and active welcome support.

  • NPS correlation: Passives and detractors frequently mentioned “unexpected pricing changes,” while promoters highlighted “responsive support” and “time savings.”

  • Emerging patterns: 27% of new users who asked for onboarding guides also gave lower NPS, suggesting immediate onboarding investment could lift advocacy.

This level of insight drives confident product decisions: Prioritize onboarding improvements for new users, focus feature development on advanced analytics, and fine-tune pricing communication. Insights from regular, conversational customer feedback surveys get product and CX teams aligned—miss this, and opportunities for growth slip away.

Start your customer feedback analysis today

  • Describe goal in prompt

  • Generate and customize survey

  • Launch by link or in-product widget

  • Analyze with AI—get instant recommendations

Results start coming in immediately. Create your own survey and unlock high-quality insights today.

Create your survey

Try it out. It's fun!

Sources

  1. SurveySparrow. Survey Response Rate Benchmarks for 2025: Industry-by-Industry Comparison and Best Practices.

  2. SuperAGI. AI vs Traditional Surveys: A Comparative Analysis of Automation, Accuracy, and User Engagement in 2025.

  3. MetaForms.ai. AI-Powered Surveys vs Traditional Online Surveys: Survey Data Collection Metrics

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.