This article will give you tips on how to analyze responses from a conference participants survey about mobile app usability. I’ll show you the best approaches for survey response analysis using AI and expert tools.
Choosing the right tools for analyzing survey responses
Your approach and tooling really depend on what kind of data you have. With survey analysis, there are two main types of data:
Quantitative data: Things like "how many people used feature X" or "how many users gave a score of 7 out of 10" are easy to count. You can handle this kind of structured, numerical data with simple tools—Excel or Google Sheets are totally fine for this.
Qualitative data: But when you have open-ended answers (“Describe your frustrations using the app”) or follow-up responses, things get messy. Reading through long responses one by one just isn’t practical, especially with dozens or hundreds of participants. This is where AI tools for survey analysis come in.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Export, paste, chat: You can export responses from your survey platform and copy them into ChatGPT or another AI chatbot. From there, you can discuss the data with the AI, ask for summaries, or look for key patterns.
Not the most convenient: But let’s be honest—copying and pasting survey exports isn’t fun. If you have a lot of responses, you’ll run into limits with how much data you can actually feed into ChatGPT at once. It’s possible, just not seamless, and tracking which chat covers which part of your data can get confusing fast. Still, 42.1% of surveyed feedback teams report using tools like ChatGPT for feedback categorization and analysis—it’s a proven method, if a bit manual. [1]
All-in-one tool like Specific
Purpose-built for survey analysis: Specific collects survey data in a conversational format and uses AI to analyze the results on the fly. You get quality data—because our conversational surveys ask follow-up questions automatically, the insights you gather are much deeper than traditional forms.
Instant results, zero spreadsheets: Our tool will instantly summarize all responses, find core ideas, and surface key themes. No more sifting through walls of text or figuring out formulas—just actionable insights delivered in everyday language. You can chat with AI about your data exactly like ChatGPT—but with extra features for managing what goes into each analysis. Want to see how that works? Check out this rundown of AI survey response analysis in Specific.
Smarter, more effective surveys: 85.2% of mobile app professionals gather feedback already, but those using multiple feedback methods (in-app, email, embedded widgets) see better data. Specific lets you combine collection and analysis, so you can act while the feedback is still fresh. [1]
Useful prompts that you can use for analyzing mobile app usability survey data
If you want results from an AI survey response analysis—whether you’re using Specific, ChatGPT, or something else—prompts are everything. The best prompts help you get the most actionable insights, fast.
Prompt for core ideas: This is the backbone if you want to surface top themes or pain points straight from participant responses. Here’s the exact prompt Specific uses (it will also work in ChatGPT or Claude):
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
More context = better analysis: AI performs stronger when you set the scene. Before running your analysis, provide context about the survey (who the audience is, why you ran it, what your primary goal is, even what trends you’re tracking). Example prompt that includes context:
We ran this survey with 200 conference participants, all of whom used our mobile app for event navigation and networking. Our goal is to understand what features worked, where people got stuck or frustrated, and why they did (or didn’t) use our in-app messaging feature. Please extract and summarize the main feedback themes, separating by functionality if possible.
"Tell me more about..." Once you spot an interesting core idea (for example, “navigation confusion”), follow up with:
Tell me more about navigation confusion.
Prompt for specific topic: If you want to check whether anyone brought up something specific, ask:
Did anyone talk about session reminders? Include quotes.
Prompt for pain points and challenges: Want to surface all the main friction points? Use:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for personas: To segment your audience into groups (think “networking power users” vs. “app skeptics”), use:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for sentiment analysis: If you want to see the overall emotional temperature, ask:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for motivations & drivers: If you care about what motivates different conference participants to engage with (or ignore) certain features, ask:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for unmet needs or opportunities: Looking to spot new feature ideas or things you’re missing? Use:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want a full guide to building your own survey prompts, there’s a deeper dive here: best questions for conference participant surveys about mobile app usability.
How Specific summarizes qualitative data based on question type
Specific structures its analysis around the kind of question you asked:
Open-ended questions (with or without follow-ups): You get a detailed summary for all responses to the primary question and any follow-up related to it.
Choices with follow-ups: Each choice gets its own summary of all associated follow-up responses, helping you compare across segments (for example, "iOS users" vs. "Android users").
NPS questions: Feedback gets bucketed by detractors, passives, and promoters—each with a separate summary of what that group said in their follow-ups.
You can recreate this in ChatGPT—it’s just more labor-intensive, involving tons of manual sorting and prompt engineering. If you’d rather skip all that, check out how automatic AI follow-up questions make richer data collection possible.
How to handle AI context limits on large surveys
Even AI-based tools have context size limits—meaning there’s only so much text ChatGPT or Claude can process at once. If your survey brought in hundreds of detailed responses, you need to “fit” your data into what the AI can handle. Specific builds workarounds right in:
Filtering: You can filter conversations to include just the segments you want (“Show only people who gave a negative usability score” or “Only participants who answered the messaging feature follow-up”). AI then analyzes only these filtered replies.
Cropping: You can select which questions to send to AI for analysis (“Analyze only the open-ended feedback”). This means you don’t max out the context size, but still get focused insight from the biggest priorities.
If you’re manually using ChatGPT for survey data, you’ll need to do similar filtering and cropping—either before or after pasting data into the chat window. Using an AI survey analysis tool built for this kind of work just makes life easier and helps you avoid unnecessary headaches.
Collaborative features for analyzing conference participants survey responses
Collaboration can be a real pain point when teams want to dig into usability survey results together—especially if everyone’s exporting CSVs, building their own summaries, and emailing analysis around.
Chat-driven teamwork: In Specific, you and your teammates can analyze survey data just by chatting with AI. You’re never locked into one big shared transcript—each person can have their own chat tied to a unique analysis, filtered however they want (“Let’s look at only iOS users who mentioned download pain points”).
Transparency and traceability: Each chat shows who created it, making it easy to track ownership across product managers, researchers, or UX teams. When collaborating, every message in AI Chat displays each sender’s avatar—you always know who asked which question, which insight or follow-up belongs to which colleague, and where to look for next steps.
Optimized for fast decision-making: These features help teams make sense of usability data together, faster. Whether you want to validate a hunch, deep-dive into pain points, or prepare a presentation of core themes, everything happens in one place—no back-and-forth in email threads or scattered Google Docs. If you want to learn how to design surveys for this use case, read our step-by-step guide here: how to create a conference participants survey about mobile app usability.
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