This article will give you tips on how to analyze responses from an ex-cult member survey about rebuilding trust, focusing on the best ways to approach survey analysis with AI and modern tools.
Choosing the right tools for survey response analysis
The way you analyze your ex-cult member survey on rebuilding trust depends on whether your responses are structured or open-ended. Here’s how I think about choosing the right approach:
Quantitative data: If your survey asked straightforward questions with limited answers (like “How comfortable do you feel: 1-5?”), Excel or Google Sheets work perfectly. You can count up responses, make simple charts, and see trends quickly.
Qualitative data: For open-ended questions (“What helps you rebuild trust?” or follow-up stories), reading every answer is overwhelming—and impractical. That’s when you need AI tools to make sense of the nuance and varied responses.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste exported survey data into ChatGPT or a similar AI model.
This method is accessible—just drop your CSV or text export into the chat, and ask for themes or insights.
It’s OK for basic analysis, but it can quickly get unwieldy if you have many responses. Large files often don’t fit into the AI’s “context window,” so you’ll find yourself dissecting the data into chunks, which takes extra time and care to keep things organized. You’re also missing features for follow-up filtering and tracking qualitative themes over time.
All-in-one tool like Specific
Purpose-built AI survey analysis platforms like Specific remove a lot of manual work from the process.
Specific collects responses with conversational AI surveys. As people respond, the AI asks real follow-up questions—making sure you get deeper, higher quality feedback, not just surface-level answers. This is especially powerful for sensitive topics, like rebuilding trust after cult experiences, when context matters.
AI-powered analysis in Specific instantly summarizes and surfaces key insights. You don’t have to cut and paste anything: just click “analyze,” and you’ll get a digest of major themes, frequencies, and sample quotes—making it easy to spot what matters most to your audience.
You can chat directly with the AI about your survey results, just like you would in ChatGPT. Plus, you can manage what data and which questions the AI should focus on. This flexibility is huge when your survey covers a complex, emotional topic like trust-building after leaving a cult.
Other well-known tools like NVivo, MAXQDA, ATLAS.ti, Delve, and Looppanel also offer AI-powered features for qualitative survey analysis, such as automatic coding, theme identification, and sentiment analysis. These platforms are especially popular in academic and research settings, and can help you go deeper into pattern recognition and emotional analysis in your responses. [1][2][3]
If you want to build your own survey from scratch, check out this AI survey generator or learn more with these best practices: how to create ex-cult member survey about rebuilding trust and best questions for ex-cult member survey about rebuilding trust.
Useful prompts that you can use for analyzing ex-cult member rebuilding trust survey data
Once you’ve chosen your AI tool, the next step is asking good questions (prompts) to get quality insights. Here are some proven prompts and strategies:
Core ideas prompt: To distill the main insights and recurring themes through all your qualitative answers, try this:
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 performs better if you give it more context: Before the data, add a line like:
This survey was conducted among people who are former members of high-control groups. The purpose was to understand barriers and enablers to rebuilding trust with others in their lives after leaving such groups. Please consider this when summarizing the data below.
Dive deeper into themes: Use direct prompts like “Tell me more about [core idea]” to explore specifics around a trend the AI spotted.
Spot specific topics: Ask, “Did anyone talk about [topic]?” (e.g., ‘Did anyone mention therapy or group support?’) For richer insight, say “Include quotes.”
Personas prompt: If you want to understand different types of respondents:
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: “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 behaviors or choices. Group similar motivations together 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.” Sentiment analysis is especially useful when working with emotionally charged feedback, which is common in ex-cult member contexts.
You can also use prompts for “Suggestions & Ideas” or “Unmet Needs & Opportunities” to expand your analysis, making sure you’re not missing any actionable feedback or openings for future support programs.
For even more inspiration, you can explore best practices for question design and AI-powered follow-ups at automatic AI follow-up questions.
How Specific analyzes qualitative data by question type
Specific is smart about how it breaks down and summarizes your survey data, depending on the question type:
Open-ended questions (with or without follow-ups): The platform instantly generates a clear summary for all responses. If you’ve used follow-up probing (which is recommended), it includes summaries for each follow-up thread too.
Choices with follow-ups: For single- or multi-choice questions, Specific doesn’t lump all responses together. Instead, it gives you a summary of all the follow-up answers tied to each choice, so you can see—for example—how different trust-building methods resonate with distinct sub-groups.
NPS questions: If you use Net Promoter Score to measure willingness to recommend support groups, each segment (detractors, passives, promoters) gets its own qualitative summary. You can then compare not just scores, but also the "why" behind them.
You can absolutely recreate this approach in ChatGPT or similar tools. It just requires more manual effort—sorting data, segmenting by question or response type, and pasting step by step.
Avoiding AI context limits with filtering and cropping
One big challenge with AI survey analysis is the “context window” (how much data you can paste in at once). When survey responses are long or you have a high volume, your data won’t fit in one go.
In Specific, there are two main ways to solve this problem automatically:
Filtering. You can filter conversations by specific responses—so the AI only analyzes the ones where users replied to selected questions, or picked a certain answer. This helps focus on what matters and reduces context bloat.
Cropping. Crop questions for analysis—send only the selected questions to the AI, skipping the rest. That way, you can analyze more conversations in a single pass without running up against the AI’s size limit. This is a game changer if you’re working with sprawling qualitative data from ex-cult member surveys.
Tools like NVivo, MAXQDA, and ATLAS.ti also offer filtering and coding features to manage large-scale qualitative data efficiently. [1][2][3]
Collaborative features for analyzing ex-cult member survey responses
Analyzing qualitative responses about rebuilding trust after cult experiences isn’t something you want to do alone—interpretation benefits from multiple perspectives, and results need to be shared across advocates, therapists, and researchers.
Chat-based collaboration: In Specific, you can chat with AI about your data—just like ChatGPT, but focused on your unique responses. You’re not stuck in a giant spreadsheet, which makes collaborative discovery more engaging and less error-prone for sensitive, nuanced data sets.
Multiple analysis chats: You can run parallel chats about your survey—each with its own focus (like "barriers" vs. "success stories") and its own filters. This allows teams to explore different aspects of the rebuilding trust process simultaneously.
See who’s who: When your team contributes to analysis, every chat message is tagged with the sender’s avatar. It’s easier to keep track of ideas, feedback, and who’s asking what—especially in multi-stakeholder efforts (therapists, support group leaders, ex-members).
For a hands-on deep dive, or to create your own ex-cult member survey, you can check out the survey generator with rebuilding trust preset and the AI survey editor for real-time, collaborative editing and feedback.
Create your ex-cult member survey about rebuilding trust now
A truly effective survey process combines deep qualitative insight with the ability to act fast. Use modern AI-powered analysis to unlock the real stories behind rebuilding trust, and surface patterns you can actually use.