This article will give you tips on how to analyze responses from an ex-cult member survey about identity reconstruction using AI-powered survey analysis. Whether you’re dealing with multiple choice results or paragraphs of personal testimony, I’ll cut through the noise and help you get to actionable insights fast.
Choosing the right tools for analysis
Your approach to survey analysis always depends on the form and structure of the responses. If you’re looking at straightforward counts or ratings—such as “how many ex-cult members experienced a loss of social network”—tools like Excel or Google Sheets are more than enough for basic quantitative analysis. For these questions, you just run the sums and chart the results.
Quantitative data: If you have checkboxes, scales, or NPS ratings, they’re easy to analyze conventionally. Input your data into Google Sheets or Excel, count occurrences, calculate averages, and plot graphs. Most basic survey tools handle this out of the box.
Qualitative data: Here’s where it gets real. Open-ended responses—long-form answers about how members rebuilt their identities—are priceless, but reading through dozens or hundreds is impossible manually. This is where AI analysis steps in, finding patterns and extracting themes quickly.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you export your survey results as a CSV or plain text, you can paste chunks of the data into ChatGPT and prompt it for a summary or thematic extraction. This clearly works—and it will give you a flavor of the group’s core issues—but let’s be honest: handling long lists of personal stories this way is tedious. You’re stuck managing context limits, chunking your data, and constantly copying and pasting. For surveys with substantial open-text responses, the process slows down and you’ll risk missing connections across the data. If you go this route, always keep in mind that ChatGPT and similar tools can only process a specific amount of text at once, and the more manual labor involved, the more frustrating it gets if you want to do deep analysis.
All-in-one tool like Specific
Specific was designed for exactly this challenge. With it, you can both collect responses from ex-cult members and analyze those responses with AI, all in one place.
When data is collected, the platform uses AI-powered followup questions to dig even deeper—meaning the context you get is richer from the start. Once you have a batch of responses, the AI automatically summarizes everything, highlights underlying themes, pulls out actionable findings, and lets you “chat” with the AI to answer your questions about results—without needing to wrangle spreadsheets or cut and paste answers for the algorithm. You can set filters, drill down to specific subgroups, and even control what the AI sees in context. It’s all built for collaborative, evidence-based survey analysis.
If you want a hands-on example, try out the survey generator built for ex-cult member identity reconstruction surveys or explore the AI survey generator for niche survey topics.
Even reputable research tools acknowledge that AI-driven platforms streamline the process of extracting meaningful insights from complex qualitative datasets, vastly increasing productivity when compared to manual analysis. [1]
Useful prompts that you can use to analyze ex-cult member survey response data about identity reconstruction
The way you prompt the AI or GPT model makes a huge difference in the quality of survey analysis you get—especially with open responses. Here are some proven suggestions:
Prompt for core ideas: This generic prompt is a workhorse, and it’s baked into Specific's own analysis flow. It reliably pulls out major themes from stacks of ex-cult member testimony, showing you what’s really moving respondents on the topic of identity reconstruction.
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
Give context for better results: Remember, AI always works better with context! Tell the AI what kind of survey you ran, who answered, your research goals, or specific things you’re curious about. Here’s an example:
I ran a survey with ex-cult members about how they rebuilt their identities after leaving. I want to understand major challenges people face and what helped them most during the transition. Group similar answers, and highlight real, actionable insights.
Prompt for drilldown: Just found an interesting idea in the top themes above? Immediately ask:
Tell me more about loss of community support (core idea)
Prompt for specific topic: If you want to confirm whether anyone brought up a certain theme—like religious trauma, family struggles, or online support groups—use:
Did anyone talk about rebuilding self-esteem? Include quotes.
Prompt for personas: Sometimes, you’ll want the AI to identify user personas or archetypes based on response patterns. Perfect if you’re trying to map out typical journeys or distinct needs among ex-cult members.
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: This helps identify where ex-cult members most commonly struggle with rebuilding their identities and what’s blocking their progress.
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: Good for understanding why ex-cult members embark on certain recovery paths or what gives them hope.
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: This tells you if the group has an overall positive, neutral, or negative outlook—and why.
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.
Use these prompts directly in AI chat, or copy them into your own workflow—no matter which tool you’re using.
If you want more tips on what to ask, check out this practical guide on choosing the best questions for ex-cult member identity reconstruction surveys.
How Specific analyzes qualitative survey responses by question type
Specific’s AI survey response analysis is designed to make sense of different types of questions, without you having to think about it. Here’s how it handles varying structures:
Open-ended questions with or without followups: Produces an instant summary for all initial responses and any further followup conversations. You get both the bird’s-eye view and detailed insights for every angle respondents shared on rebuilding identity.
Choices with followups: For every multiple-choice answer, you get a separate summary of the open-text or follow-up replies linked to each choice. For example, if the survey asks what helped the most (“support from friends,” “therapy,” “reading books”), Specific will show unique insights for each group.
NPS (Net Promoter Score): For NPS questions about identity rebuilding experiences, Specific categorizes respondents as detractors, passives, or promoters. Each group receives its own summary, so you can see what supporters vs. skeptics are saying.
You can do all this using ChatGPT too—it just takes a lot more copy-paste and organizational grunt work. If you want to try an NPS approach for this topic, get started in one click using the identity reconstruction NPS survey builder.
Rich, AI-powered analysis is so much more accurate when you’re capturing conversational, followup-driven survey responses. According to peer-reviewed research, leveraging such AI platforms results in dramatically improved insight extraction speed and accuracy, compared to traditional hand-coding methods. [2]
How to tackle challenges with AI context limits
No matter which AI or GPT model you use, you’re always limited by context size (basically, the “memory” of the AI per chat). If you paste too many survey responses, the AI can’t see everything at once. There are two smart solutions—both standard in Specific’s workflow, but you can adapt them elsewhere:
Filtering: Narrow down which conversations or responses the AI analyzes. For example, you might only want to look at survey participants who specifically addressed “loss of personal identity” or “struggles with family integration.” In Specific, this filter is point-and-click. Elsewhere, you’ll need to manually curate.
Cropping: Select only the questions you want the AI to analyze. If your survey has ten questions, maybe start with just one or two that matter most. This keeps the AI focused and avoids context overflow.
This approach keeps your insights relevant and organized. Academic studies confirm that applying sampling, filtering, and question-specific cropping preserves data quality while enabling meaningful AI analysis—especially when participant volumes are high. [3]
Collaborative features for analyzing ex-cult member survey responses
Collaboration is often the main pain point when working on sensitive identity reconstruction surveys with expert teams or support communities. Passing around Google Sheets or text files just creates confusion, version control headaches, and lost context.
Specific solves this by letting you analyze survey results just by chatting with AI. You and your colleagues all see the same dataset, but you can have multiple conversations at once—each with its own filters, supporting queries, and topic focus.
Each chat has its own context and history, and you always see who created or contributed. This makes it easy for a therapist, peer support leader, and researcher to explore different angles, compare findings, and build on one another’s work.
In group analysis, avatars and visible sender names in the AI chat show who’s contributing what question or perspective, so feedback stays organized, and it’s never a mystery who asked or wondered about which insight.
You benefit from real-time, in-platform collaboration instead of endless back-and-forth. This breaks down silos—especially important when rebuilding understanding is so core to the ex-cult member audience. For more collaborative tips, read this practical guide to building ex-cult member identity reconstruction surveys.
Create your ex-cult member survey about identity reconstruction now
Start your survey today and let AI handle the analysis—actionable insights, richer stories, and deeper healing journeys, all unlocked in minutes with collaborative features designed for real teams working on sensitive topics.