This article will give you tips on how to analyze responses from ex-cult member surveys about reasons for leaving—using practical AI approaches and tools.
Choosing the right tools for response analysis
The approach and tooling you use depends on the form and structure of the survey data you collect. Here’s how I break it down:
Quantitative data: Countable facts—like “What percentage of ex-cult members left for family reasons?”—are straightforward to analyze. You can easily use Excel or Google Sheets for filters, basic statistics, and graphs.
Qualitative data: Rich responses from open-ended or follow-up questions tell deeper stories—but it’s impossible to read and code each one by hand, especially at scale. Here, you need AI-powered tools to surface themes, hidden patterns, and insights without drowning in information.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you export your survey data, you can copy responses directly into ChatGPT or similar tools. You can then chat about your data, ask for themes or summaries, and use prompts to guide the analysis.
The upside: It’s accessible and flexible. For quick-and-dirty explorations or if you already use GPT-based chatbots, this can work.
The downside: Handling large data sets isn’t convenient. You’ll hit context limits (the amount of text you can paste), you have to clean and format the data yourself, and every step requires manual copy-pasting. Maintaining structure for follow-up questions or grouping answers by type is cumbersome.
All-in-one tool like Specific
Platforms like Specific are built precisely for this workflow. You design and launch conversational surveys—in chat format—that collect both structured and unstructured feedback. The AI instantly summarizes responses, identifies key themes, and delivers actionable insights right in the dashboard.
Key benefits:
You can chat with the AI about your data (like in ChatGPT) but with your structured survey context and filters. You get specialized prompts and analysis options tailored to survey data, not just generic chat.
When collecting data, Specific’s conversational surveys ask follow-up questions automatically—meaning you get more depth and clarity, not just surface-level answers. Learn more about automatic AI follow-up questions and why they matter for qualitative research.
No more spreadsheets or manual coding—the platform summarizes, tags, and organizes themes for you. Plus, you can export, share with your team, and manage analysis threads without friction.
Other reputable qualitative data analysis tools in the market—like NVivo, MAXQDA, ATLAS.ti, Delve, and Looppanel—offer similar AI features for coding, sentiment analysis, and theme detection, but don’t offer the conversational, chat-based experience built for survey workflows like Specific does. [1]
If you want to create a new ex-cult member survey about reasons for leaving, Specific gives you a focused conversational survey generator tailored to this audience and topic. Want more customization? Try the open-ended AI survey generator for any subject.
For in-depth guidance on survey questions, check out best questions for ex-cult member reasons for leaving surveys.
Useful prompts that you can use when analyzing ex-cult member reasons for leaving survey responses
Prompts are the real superpower when you’re digging into qualitative response data. Here’s my favorite approach and a few sample prompts:
Prompt for core ideas: This one works wonders for extracting main themes from extensive text.
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
Tip: Always give the AI more context—tell it what your survey is, who answered, and what outcome you’re after. It’s the difference between “meh” and spot-on summaries.
I conducted a survey among ex-cult members about their reasons for leaving, using open-ended questions and follow-ups. Extract and summarize the main themes, list supporting evidence, and note frequency if possible.
Prompt for more detail on any theme: Once you identify a core idea, just ask,
Tell me more about [core idea]
This slices right into supporting details, direct quotes, or additional context.
Prompt for specific topic validation: If you’re testing a hunch, try:
Did anyone talk about [specific topic]? Include quotes.
Prompt for personas: To segment your audience and bring out patterns, 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 pain points and challenges: Dig into what’s holding people back or driving dissatisfaction:
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 motivations & drivers: Identify what’s moving people to act:
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 sentiment analysis: Get a quick read on how respondents feel overall:
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.
For more tips, see how to create ex-cult member survey about reasons for leaving and advanced survey editing using AI survey editor.
How Specific handles qualitative analysis based on question type
Specific brings structure to qualitative data—even when questions get messy or answers sprawl. Here’s how it works, depending on question type:
Open-ended questions with or without followups: The AI delivers an overall summary for all responses, as well as grouped summaries for any follow-up answers attached to that question.
Choices with followups: For each choice (e.g., “I left for family,” “I left for belief changes”), each group of follow-up answers is summarized separately. You’ll see patterns by choice, not just a single wall of text.
NPS questions: Detractors, passives, and promoters each get their own summaries for follow-up responses. This way, you can compare what frustrated (or delighted) different segments, with detailed evidence for each.
You can do this by hand with ChatGPT—but in Specific, it’s built in and saves you countless hours. For a behind-the-scenes look, see AI-powered survey response analysis and our comparison to manual AI coding using export-and-paste tools like NVivo, MAXQDA, or ATLAS.ti. [1] [2] [3]
Addressing the challenge of context size limits in AI-based survey analysis
AI tools are powerful, but they’re constrained by how much text they can process at a time (context limit). If your ex-cult member survey has dozens—or hundreds—of passionate responses, not everything will fit. Here’s how I approach the problem:
Filtering: Only analyze conversations where people responded to the questions you care about, or only those who made certain selections. This makes your AI’s focus sharper, while allowing you to zoom in on specific audiences or topics.
Cropping: Select the key questions (or responses) you want the AI to analyze—reducing context size while maximizing the insight. Best for big surveys where only a few questions matter.
Specific includes these options out of the box, making it painless to analyze large qualitative datasets while working within AI system limits. For a hands-on experience, try spinning up an NPS survey for ex-cult members instantly.
Collaborative features for analyzing ex-cult member survey responses
Collaboration is a challenge—especially when you’re dealing with nuanced answers and emotionally-loaded reasons for leaving a cult. Having a system that allows your team, supporters, or researchers to analyze and build on each other’s findings makes all the difference.
Multiple chats, different focus: In Specific, you can spin up multiple AI-driven analysis threads—each with its own filters, themes, or subgroups. Every chat shows who started the thread and has its own set of follow-up questions or goals, so teams can divide and conquer topics like family, belief change, or trauma support.
Always know who’s contributing: In collaborative chats, every message displays the sender’s avatar—so it’s clear who asked what, and whose perspective shaped the insight. This is ideal for breaking down silos between researchers, supporters, and stakeholders when exploring complex, deeply personal motivations.
Chat to analyze, not just to code: You don’t need to export, script codebooks, or merge spreadsheets—just chat with the AI and uncover the stories, themes, and evidence your group needs to make sense of ex-cult member experiences.
Create your ex-cult member survey about reasons for leaving now
Start uncovering actionable insights and deeper motivations—use an AI-powered conversational survey built for empathy, nuance, and collaborative analysis to get better answers, faster.