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How to use AI to analyze responses from ex-cult member survey about exit experience

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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 exit experience. If you want real insights, you need to approach survey response analysis with the right tools and techniques.

Choosing the right tools for analyzing survey responses

The right approach—and tool—for survey analysis depends mostly on the form and structure of the data you collected. Here’s the split:

  • Quantitative data: If your survey just asks for ratings or for users to pick from multiple choices (e.g., “rate your exit experience on a scale of 1–10”), these answers are easy to count and analyze in a spreadsheet like Excel or Google Sheets. You can quickly spot trends, average scores, and frequencies.

  • Qualitative data: When people answer open-ended questions (“tell me about your exit experience”), things get complicated. If you have 20 or 200 responses, reading through each by hand is overwhelming. You need to use AI tools to efficiently summarize and uncover patterns in these narrative responses.

There are really two practical approaches to handling qualitative responses:

ChatGPT or similar GPT tool for AI analysis

Copy-paste analysis: You can export your survey data and paste it into ChatGPT or another large language model. Then, you can prompt the AI to summarize, extract themes, or highlight patterns based on your questions.

Limitations: This method is workable for small amounts of data. But, it quickly gets tedious—you have to wrangle spreadsheet exports, handle response formatting, and you risk hitting token/context limits. Plus, you miss the efficiency of tool-integrated features like filtering, tagging, or quick exporting.

All-in-one tool like Specific

Purpose-built for survey analysis: A dedicated tool like Specific makes life easier. First, it’s designed for this exact use case: you can both collect survey responses and analyze them in the same place, without switching between platforms.

AI-powered follow-up questions: When you use Specific to collect your data, the AI automatically asks clarifying and probing follow-up questions in real time. This leads to richer, more useful responses. Check out how this works in practice here.

Instant analysis & live chat: Specific instantly summarizes all your survey responses, detects core themes, and transforms mountains of open text into actionable insights—zero spreadsheets or manual coding required. You can use a chat interface (like ChatGPT, but built directly into the analysis workflow) to discuss results, dig into specific findings, and manage which parts of your data are included in AI context for better focus.

Integrations with broader toolsets: If you need something more specialized, tools such as NVivo, MAXQDA, Atlas.ti, Delve, and Looppanel also offer robust AI-powered qualitative data analysis capabilities, including sentiment analysis and thematic coding. Many professional researchers in cult recovery studies rely on these for deep dives into exit experience narratives [1][2][3].

Useful prompts that you can use for ex-cult member exit experience survey analysis

The power of your analysis really comes down to the prompts you give the AI. Here are a few I know work well, especially if you’re dealing with nuanced, open-ended answer sets from ex-cult members describing their exit experiences.

Prompt for core ideas: Use this to quickly get a list of dominant themes, exactly how tools like Specific do it. This prompt is designed to work both in Specific and pasted straight into ChatGPT or similar tools:

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

The more context you provide to the AI, the better the analysis. Always add background, e.g.:

“This survey was conducted among ex-cult members to explore their exit experience. My goal is to identify the main factors that helped or hindered their exit, as well as broader themes that could be useful for support organizations.”

Once you have your themes, you might want to dig deeper into one. Ask:

Tell me more about “loss of community” (core idea)

Prompt for specific topic: If you have a hunch or need to check if something is mentioned:

Did anyone talk about family relationships? Include quotes.

Prompt for personas: Find recurring “types” of ex-cult member experiences:

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: Uncover collective struggles after leaving a cult:

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: Take the emotional temperature:

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.

Need more ideas? Check out the best practices for questions to ask in ex-cult member surveys.

How Specific analyzes different question types in qualitative surveys

The structure of your questions has a big impact on how you (or any AI tool) should analyze responses:

  • Open-ended questions (with or without follow-ups): Specific summarizes all main responses alongside the answers to follow-up questions, giving you a top-level and detailed view at the same time.

  • Choice questions with follow-ups: The tool analyzes each choice separately, then provides you with summaries of the follow-up responses tied to each—so you can see exactly why people picked a specific option.

  • NPS (Net Promoter Score): Specific splits respondents into detractors, passives, or promoters, and creates a distinct summary for responses within each group. You can unpack “why” behind each rating easily.

You can do the same type of analysis using ChatGPT—just expect a bit more manual data sorting to group the right answers together, especially as response numbers grow.

Handling AI context limits in large ex-cult member exit experience surveys

AI models can only analyze a limited amount of text at one time—referred to as the “context limit.” If you collect a substantial number of in-depth responses from ex-cult members, your data might not fit in all at once.

There are two ways to handle this, and Specific bakes them in:

  • Filtering: You can filter by user replies—say, only look at conversations where someone answered a specific sensitive topic or picked a particular option. This reduces the data the AI ingests, keeping analysis targeted.

  • Cropping: You can focus on specific questions—only send selected answer sets to the AI for analysis. This slices your data set to size, letting you unpack responses one angle at a time.

For a deeper dive into these workflows, check how AI survey response analysis works in Specific and discover more in the AI survey generator for ex-cult member exit experience surveys.

Collaborative features for analyzing ex-cult member survey responses

Analyzing survey data about exit experience is rarely a solo job—especially when you’re uncovering sensitive, multi-layered stories from ex-cult members. Collaboration is key for trustworthy interpretation and support.

Chat-driven analysis: With Specific, you can analyze your data just by chatting with AI. This not only makes your insights more accessible but empowers anybody on your team to ask follow-up questions, explore patterns, or validate assumptions in real time.

Multiple analysis threads: You can spin up multiple chats—each focused on a particular theme (like “support systems” or “trauma recovery”), apply custom filters, and keep analysis organized. Every chat clearly shows who started it, making collaboration seamless.

Team context and attribution: Threaded conversations come with clear attribution—you see avatars and author data for each message. This helps ensure shared understanding, maintains a transparent audit trail on who asked what, and lets you follow the flow of reasoning for every insight.

To set up your survey workflow from scratch, check our guide on how to create an ex-cult member survey about exit experience.

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Sources

  1. jeantwizeyimana.com. AI Tools for Survey Data Analysis: NVivo and MAXQDA

  2. looppanel.com. Guide on AI for open-ended survey response analysis (Atlas.ti, Looppanel)

  3. insight7.io. 5 Best AI Tools for Qualitative Research in 2024 (Delve, others)

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.