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How to use AI to analyze responses from ex-cult member survey about skill gaps

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Adam Sabla

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Aug 22, 2025

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This article will give you tips on how to analyze responses from an ex-cult member survey about skill gaps. If you want to find actionable insights fast, understanding your data is the first step.

Choose the right tools for analyzing survey data

The right approach—and the best tools—depend on what kind of data you’ve collected. If your survey asks both structured questions (like multiple choice) and open-ended follow-ups, you’ll need different methods:

  • Quantitative data: This is anything you can count—how many people picked a specific skill gap, for example. Tools like Excel or Google Sheets are perfect here. Just export your survey results and run your stats or build simple charts.

  • Qualitative data: Open-ended questions (or long follow-up replies) need a different approach. Reading hundreds of stories by hand isn’t just tiring—it’s impossible to spot patterns at scale. That’s where AI tools come in, analyzing large volumes of text without bias or fatigue.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

Copy-paste workflow. You can export your survey data, drop chunks of text into ChatGPT (or Claude, Gemini, etc.), and chat with the AI about your findings. This works, but:

It’s not super convenient. You lose data structure, which makes it tough to segment by question or filter specific answer types. Managing context—especially with long surveys—gets messy fast. You’ll also need to write and iterate on prompts yourself, or risk missing key patterns.

All-in-one tool like Specific

Purpose-built survey platform. Specific is an AI tool built for exactly this use case: It handles the entire workflow—creating the survey, collecting responses, and then analyzing those responses using AI. When people fill out your survey, Specific asks natural follow-up questions to dig for honest, actionable feedback.

Instant, AI-powered analysis. As soon as your survey starts collecting replies, Specific’s AI summarizes the key themes, extracts core ideas, and surfaces strengths, challenges, and details you’d easily miss with manual analysis. Spreadsheets and endless highlighting are gone—you get sharp, actionable summaries and can even have a conversation with the AI about your results, just as you would in ChatGPT, but with all your data context instantly available.

Data privacy, repeatable workflows. You control what data is shared into each AI analysis, and features like filtering and cropping help you handle larger sets of feedback without ever leaving the platform. Learn more about analyzing survey responses with AI in Specific.

Many professional tools like NVivo, MAXQDA, and ATLAS.ti also offer robust AI-driven capabilities for coding, theme detection, and visualization—helpful if you handle a massive volume of interviews or focus groups. For example, NVivo can automatically code qualitative data and suggest themes or sentiments in your ex-cult member responses, saving huge amounts of manual effort. [1]

Useful prompts that you can use to analyze ex-cult member skill gap survey responses

AI acts like an assistant—as smart as your prompt makes it. Here are tried-and-tested prompts to unlock insights from your ex-cult member skill gap responses:

Prompt for core ideas. This uncovers main themes at a glance. Specific uses a tuned version of this by default, but you can use this with GPT tools 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

If your survey has unique context or a specific goal, always give AI that background. Here’s how to set that up:

You are analyzing replies to a skill gap survey from adults who recently left a high-control group. My research goal is to identify practical, teachable skills they struggle with most, and what support or resources would make a difference. Highlight themes connected to employment readiness, emotional intelligence, and social adaptation.

Dive deeper into a theme. Use: "Tell me more about [core idea]" once you spot a topic in your summary—this explores that theme’s depth and detail.

Prompt for specific topics. If you’re validating a hypothesis (e.g., “Are digital literacy skills a challenge?”), try:

Did anyone talk about digital literacy challenges? Include quotes.

Personas prompt. If you want to segment by types of leavers (e.g., by age, background, or employment experience):

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.

Pain points and challenges. This gets straight to the core of the struggles that come up most often:

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. Use when you want to know what’s driving skill development:

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.

Sentiment analysis. For understanding tone and overall mood:

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 & ideas.

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

Unmet needs & opportunities.

Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.

If you want to use a proven survey flow, check out these best-practice Ex-cult member skill gaps survey questions or try the AI survey generator for skill gap research—it’ll set up the right structure for strong analysis.

How Specific summarizes qualitative survey data based on question type

Specific treats different question types with tailored AI. Here’s how it works:

  • Open-ended questions with or without follow-ups. For each question, Specific generates a summary of all responses—plus an extra layer summarizing responses to the related follow-ups. This way, you get both a high-level view and details on why people answered as they did.

  • Choices with follow-ups. Each time someone picks from multiple choices (e.g., “Which skill do you struggle with most?”), every choice gets its own summary of the follow-up answers related to it. This makes it super clear which choice needs action or deserves more attention.

  • NPS (Net Promoter Score). Detractors, passives, and promoters each get a theme summary spotlighting issues or opportunities unique to each group. Follow-up responses help you know why people fall into each category and what could move the needle.

You can mimic this structure using ChatGPT, but expect extra manual steps copying, pasting, and sorting responses by question—still manageable if your dataset isn’t huge.

How to tackle AI context limits when analyzing survey responses

Every AI model (including ChatGPT and others) has a “context size limit”—a hard cap on how much data you can send for a single analysis. If you have lots of responses, that limit hits fast. Here’s how to handle it effectively (both methods are built into Specific):

  • Filtering. Focus only on conversations where users replied to selected questions or picked specific skill gap choices. Filter out non-responses or tangential answers before sending to AI. This keeps the analysis relevant and within safe bounds.

  • Cropping questions for analysis. Instead of analyzing every question for every response, crop your data down to just the most important questions (or dimensions). This ensures you don’t lose the big picture and the AI won’t choke on oversized batches.

For more on how these techniques work, see the AI-powered response analysis guide.

Collaborative features for analyzing ex-cult member survey responses

Working alone on ex-cult member skill gaps responses can get overwhelming, especially when your team needs to dive in together or analyze different hypotheses.

Chat with AI—and with your teammates. With Specific, you don’t just view collected answers: you analyze them by chatting with AI—right in the app, not in a separate tab. Every chat is a focused workspace for exploring one angle of your data set.

Multiple investigations at once. You can spin up several chats—each using a different set of filters (say, only female leavers, or only people struggling with job interviews). Each chat is named and tracked so everyone knows who’s exploring what questions or segments.

Built-in attribution. In every collaborative conversation, messages show who asked which questions. Avatars and chat history make teamwork smoother, preventing confusion (and endless Slack threads about “who ran what analysis last week?”).

With this workflow, you can quickly split the workload and reach consensus on findings. If you’re building your survey from scratch, learn about collaborative AI survey editing or how to easily create ex-cult member skill gap surveys from a blank canvas.

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Sources

  1. NVivo. NVivo—Qualitative data analysis software with AI features, automated coding, and theme identification.

  2. MAXQDA. MAXQDA—Professional software for qualitative and mixed methods data analysis.

  3. ATLAS.ti. ATLAS.ti—AI-driven qualitative data analysis tool for coding and visualization support.

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.