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How to use AI to analyze responses from conference participants survey about price and value

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Adam Sabla

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Aug 21, 2025

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This article will give you tips on how to analyze responses from a conference participants survey about price and value. I’ll focus on practical ways to turn those survey responses into actionable insights using the right combination of tools and AI analysis.

Choosing the right tools for analyzing survey responses

Choosing the right approach and tooling really depends on the form and structure of your survey data. Here’s how I break it down:

  • Quantitative data: If you’re working with structured data where you’re counting the number of people who chose a specific price range or rated value on a scale, good old Excel or Google Sheets will do the trick. These tools are perfect for quick calculations, charting, and analysis of closed-ended questions or rating scales.

  • Qualitative data: Now, if your survey captures open-ended responses—like “Describe what you found valuable about the conference”—it’s a different ball game. Manually reviewing these long-form answers is overwhelming, especially with dozens or hundreds of participants. That’s where AI tools shine: they extract themes, summarize opinions, and save hours of scrolling.

When it comes to qualitative responses, there are two main approaches you can take for analysis tools:

ChatGPT or similar GPT tool for AI analysis

Copy-paste & converse: One common method is to export your survey data and paste it directly into ChatGPT (or a similar large language model). You can then ask your questions, like “What are the top reasons conference attendees mentioned price as a concern?”

It works, but it’s clunky: For short lists or small batches of data, this approach is fine. But formatting and sending larger datasets gets messy. You’ll spend extra time cleaning up data and managing limitations around context length (how much text you can fit into the chat).

All-in-one tool like Specific

Simplify the whole workflow: Tools built specifically for conversational surveys and AI analysis (like Specific) streamline both collection and analysis. You launch your AI-powered survey, which asks smarter follow-up questions in real time, producing higher-quality, more complete responses.

Instant AI summaries and key themes: As soon as responses are in, the AI generates summaries, detects core topics, and points out actionable insights. You can chat directly with the AI to dig in further, but you also get features for managing which data to analyze—no more juggling spreadsheets or copy-pasting into multiple chats.

Deeper dives, faster: You can instantly see which topics got the most mentions, spot trends between pricing and perceived value, and compare data across segments with just a few clicks. Learn more about how AI survey response analysis works in this guide.

Useful prompts that you can use to analyze Conference Participants survey data on price and value

Half of analysis success is in the prompts you use for AI. Here are a few tried-and-tested options for surfacing core insights from price and value survey data:

Prompt for core ideas: This is great for extracting themes across all open-ended responses.

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

Always provide context: AI works better when it knows more. If your survey targeted senior conference attendees worried about workshop fees, mention that—prompt like this:

My survey was sent to 200 senior conference attendees who attended a hybrid event. Our goal is to understand how pricing for in-person versus virtual tickets shaped their experience and perceived value. Please extract the key themes about price sensitivity in the responses below.

Prompt for deeper dives: After listing themes, use follow-up prompts like:

Tell me more about XYZ (core idea).

Prompt for specific topics: Validate patterns or test hypotheses with a direct prompt like:

Did anyone talk about the value of networking opportunities? Include quotes.

Prompt for personas: Use this to segment your audience by attitude or need:

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 pain points and challenges: Perfect to surface dealbreakers:

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 sentiment analysis: Useful for gauging overall satisfaction:

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.

Many experienced teams use a combination of these prompts to quickly identify perceived value, reasons for dissatisfaction, and the real drivers behind attendee perceptions. Remember that, in one recent study of AI use in education, the average perceived value was 3.61 out of 5, while perceived costs stayed low at 2.58, suggesting people do see strong overall utility in intelligent tools like these for uncovering actionable insights. [1]

If you want more hands-on guidance, here’s an in-depth look at the best questions to ask conference participants about price and value.

How analysis in Specific adapts to different question types

In Specific, the AI tailors its summaries and key findings to the structure of each question. Here’s how it typically works for common question formats:

  • Open-ended questions (with or without follow-ups): You get a comprehensive summary that combines all responses, plus insights from any follow-up queries linked to that question. This is ideal for exploring what conference attendees found valuable or disappointing—AI quickly bubbles up main arguments and unique viewpoints.

  • Multiple choice with follow-ups: Each choice gets its own focused summary, built only from the follow-up responses tied to that answer. If participants pick “conference too expensive,” you can instantly see what specific issues they cite in follow-ups about price versus value.

  • NPS (Net Promoter Score): The summary is divided by segment—detractors, passives, and promoters. For each group, the AI highlights common feedback and the reasoning behind scoring choices, especially from those vital follow-up responses.

If you’re using ChatGPT for analysis, you can get similar results by explicitly filtering and grouping your data before running prompts. It’s just more manual labor—Sorting and prepping the data for each scenario is extra work compared to the one-click summaries you get in tools designed for AI survey analysis like Specific.

If you want to see how this works live, try this survey generator with price and value preset.

How to handle AI context limits when analyzing large datasets

AI models like ChatGPT and GPT-4 can only process a certain amount of text (“context”) at once. If your price and value survey got hundreds of responses, you’ll quickly hit that limit and the AI won’t be able to “see” all your data. Here’s how I tackle it (and how Specific handles this automatically):

  • Filtering: I filter conversations based on specific user replies—so only conversations where people answered a target question, or where they picked a specific price/value option, are sent to AI for analysis. This keeps things focused and within scope.

  • Cropping: You can “crop” which survey questions go to AI at once. If you only care about feedback on networking events versus workshop fees, just analyze those. This allows the AI to process a bigger sample by working with less text per request.

For a deeper dive, see this detailed guide on AI-powered survey response analysis.

Collaborative features for analyzing conference participants survey responses

Collaboration friction is real: Anyone who’s worked on analyzing price and value survey responses with a team knows it’s easy to lose context, duplicate effort, or misinterpret feedback if you can’t easily see what others are doing.

Straightforward teamwork: In Specific, you analyze survey data by chatting with AI, but you don’t have to do it alone. You can set up multiple chat sessions, each tailored to a team member or specific research question—every chat shows who created it, so accountability and context are never lost.

Avatars & accountability: When collaborating, you see the sender’s avatar next to every message in the chat, making it clear who asked each question or requested each summary. This visual context matters especially when reviewing nuanced price and value sentiment data together.

Segment-focused collaboration: Different teams can focus on different aspects—one group analyzing high-spend attendees, another digging into NPS feedback, all within the same set of survey data. This reduces confusion and turbocharges interpretation, helping everyone get on the same page faster.

Experienced survey teams find that approaches like these not only save time but dramatically increase the reliability and actionability of insights. If you want to see what the survey creation process looks like in action, check out the AI survey generator or play with example question flows using the AI survey editor.

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Sources

  1. Springer. "Perceptions and Use of ChatGPT: Investigating Benefits, Drawbacks, and Implications in Education"

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.