This article will give you tips on how to analyze responses from a prospect survey about decision criteria using AI-driven tools and proven strategies to get actionable insights fast.
Choosing the right tools for survey response analysis
The approach you take—and the tools you use—depend largely on the structure of your survey data.
Quantitative data: If you asked prospects to pick from listed choices, it’s easy to tally up responses in a spreadsheet like Excel or Google Sheets. These tools handle scoring, counts, and percentages with ease.
Qualitative data: Open-ended responses (like “What was most important in your decision?”) or follow-up answers can’t be handled efficiently with spreadsheets. You need AI-powered tools that can read, summarize, and spot patterns at scale—nobody wants to slog through 300+ chat logs.
There are two main approaches for handling qualitative survey data:
ChatGPT or similar GPT tool for AI analysis
You can copy and paste exported survey responses into ChatGPT and have a conversation with the AI. This lets you prompt the model to extract themes or answer questions ("What reasons do people mention most often?").
But it comes with challenges: Managing big blocks of messy data is inconvenient. Maintaining context, separating responses, and following-up across hundreds of lines can become overwhelming. You’ll spend time dealing with copy/paste limits and organizing input before getting to the good stuff.
All-in-one tool like Specific
Specific is designed for this use-case. It can collect and analyze survey data, all powered by AI and built for real-world feedback. When paired with conversational surveys, it uses AI to ask intelligent follow-ups on-the-fly—which can boost both the quantity and depth of insights you get from prospects.
Analysis is fully automated: After collecting responses, Specific instantly summarizes feedback, highlights main decision criteria, and finds themes—no spreadsheets or manual work required. You can chat directly with AI about your results, just like in ChatGPT, but with added superpowers for searching, segmenting, and managing data sent to the model.
This “built-for-feedback” workflow contributes to much higher completion rates and lower abandonment. In fact, AI-powered conversational surveys now see completion rates of 70-80%, with abandonment as low as 15-25%, compared to 45-50% and 40-55% for traditional methods [1], dramatically increasing the number and quality of responses you can analyze.
Learn more about how AI survey response analysis works with Specific: AI survey response analysis.
Useful prompts that you can use to analyze prospect decision criteria survey responses
Qualitative survey analysis with AI gets way more productive when you use clear, targeted prompts. Here are several effective ones for decision criteria research with prospects:
Core ideas extraction: This is the best place to start if you just want the main themes your prospects care about most (what drove their decisions, in their own words). This is a power prompt we use in Specific, but it works in ChatGPT—or any other GPT tool too:
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
AI always does better with context: The more background you give in your prompt, the sharper the insights. Tell it about your goals, who your audience is, or why you’re running this decision criteria survey—for example:
Analyze these responses from B2B software prospects about how they chose between solutions. I'm interested in what criteria really matter to them—especially any details about competitor comparisons, evaluation processes, or sticking points. Output main themes and count how often each is mentioned.
Dive deeper into a theme: Once the AI pulls out major ideas, follow up with:
"Tell me more about XYZ (core idea)"
Find specific feedback: If you want to check if any prospects shared feedback on a particular area, use:
“Did anyone talk about XYZ?”
You can add: “Include quotes.”
Persona identification: To spot different types of buyers in your data:
"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."
Common pain points or objections:
"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."
Motivations & drivers:
"From the survey conversations, extract the primary motivations, desires, or reasons participants express for their choices. Group similar motivations and provide supporting evidence from the data."
Sentiment analysis:
"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."
Suggestions or feature requests:
"Identify and list all suggestions, ideas, or requests provided by survey participants. Organize by topic or frequency, and include direct quotes where relevant."
Need to build your own custom Prospect survey? Try this Prospect survey generator for decision criteria research or read a detailed walkthrough on how to create a Prospect survey about decision criteria.
How Specific analyzes qualitative responses by question type
How you analyze survey data depends a lot on the structure of the questions and the depth of the follow-ups. Here’s how Specific breaks it down for prospect decision criteria surveys:
Open-ended questions with or without follow-ups: Specific summarizes all responses to each question and includes the context of follow-up replies. This way, you see the big picture and all the nuance, whether someone offered a one-liner or gave detailed context in follow-ups.
Multiple choices with follow-ups: For each possible choice, you get a separate summary of what people who selected that option said as follow-up. This makes it much clearer how different segments think and why.
NPS questions: Each NPS category—detractors, passives, promoters—gets its own theme analysis and summary of supporting answers. This pinpoints the motivations and blockers behind referral or churn behavior.
You can do all of this in ChatGPT too with the right prompts, but it’s more manual and takes a lot more copying, filtering, and organizing before you get the same level of clarity.
Want to understand the best questions to ask in a prospect survey about decision criteria? Check out these tips for high-impact survey questions or try using the AI survey editor to refine your questionnaire just by chatting.
How to solve challenges with AI context limits in survey analysis
Context size issues: The more responses you feed into the AI for analysis, the more likely you are to bump up against context size limits—meaning the AI can’t “see” all the data at once. For large prospect decision criteria surveys, you have two practical workarounds (both built into Specific):
Filtering: Only send responses to specific questions, or from people who chose a particular answer, into the AI for analysis. This hones the analysis to what matters and ensures better accuracy.
Cropping: Limit input to only the questions you want to analyze right now. That way, you can analyze larger data sets piece by piece, without overwhelming the AI.
This kind of slicing keeps your insights focused and ensures nothing important gets missed because of model context constraints.
For more on how Specific manages large qualitative feedback datasets, see our deep-dive on AI-powered analysis.
Collaborative features for analyzing prospect survey responses
When you’re analyzing decision criteria feedback from prospects, team collaboration is often a sticking point—traditional tools make it hard to share context and build on each other’s findings.
Chat-driven analysis makes teamwork easy: With Specific, you can analyze survey data just by chatting with the AI, and everyone on your research or sales team can spin up their own chat, apply their own filters (e.g., only look at prospects in a particular industry), and see who created each chat thread. This keeps analysis threads focused and transparent.
Accountability and visibility: In collaborative chats, you’ll see avatars for each contributor, so you always know who said what. This is a huge help across sales, research, and product teams when you want to draw consensus or dig deeper into a particular insight.
Parallel explorations: You’re not limited to one line of questioning. If you need to understand both “top decision drivers” and “reasons for hesitation,” just set up two analysis chats and compare outputs. Specific’s workflow makes it easy for everyone—product managers, SDRs, researchers—to work in parallel, all while tracking exactly who contributed what to the final findings.
To try collaborative survey analysis on real data, launch an AI survey with a conversational interface or use this NPS survey builder for prospect decision criteria—the team-based insights flow naturally from there.
Create your prospect survey about decision criteria now
Uncover what drives your prospects’ decisions and analyze responses with AI-powered insights in minutes—no spreadsheets, instant collaboration, and higher-quality data every time.