This article will give you tips on how to analyze responses from an ex-cult member survey about peer support group needs. I'll show you how to work with this unique data using AI, proven prompts, and the best tooling for the job.
Choosing the right tools for analyzing ex-cult member survey data
The right approach—and the right tools—depend on how your survey collected answers. If you’re just counting how many people selected an option, it’s straightforward. But if you’re dealing with long-form, open-ended replies, you’ll want AI help. Let’s break it down:
Quantitative data: If your results are structured (for example, multiple choice numbers), use Excel or Google Sheets. These handle counts, averages, and charts quickly.
Qualitative data: For answers to open-ended or follow-up questions (the “why” behind someone’s choice), reading responses one by one gets overwhelming fast. And manual coding falls short with nuanced emotional survey topics like peer support.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export your survey data (CSV or spreadsheet) and paste it into ChatGPT. From there, chat with the AI about themes or ask it to summarize patterns. This works if you only have a few dozen responses.
But here’s the pain: Large datasets often hit token/context limits. It’s hard to organize chats, track what was sent, or filter responses without outside tools. Managing analysis for ex-cult member peer support surveys, which often include deeply personal stories, gets unwieldy fast.
All-in-one tool like Specific
Purpose-built for surveys: Specific is designed from the ground up for these situations. It can collect conversational, AI-driven surveys—where follow-up questions raise the quality and detail of each response (see automatic AI follow-up questions).
Instant analysis: Its AI engine instantly summarizes all responses, organizes themes, and surfaces actionable insights—no spreadsheet wrangling, no manual copy-paste. Chat with AI about your survey results right inside the dashboard, just like talking to ChatGPT, but with features for managing what data gets analyzed.
Advantages with ex-cult member peer support surveys: By handling qualitative and follow-up-heavy data, Specific lets you get to the heart of peer support needs, boosting data quality and reducing abandonment rates. You can see these benefits in published research: AI-driven surveys achieve completion rates of 70-80% (with only 15-25% abandonment), compared to just 45-50% (and 40-55%) for traditional forms. [1]
Useful prompts that you can use for analyzing Ex-Cult Member Peer Support Group Needs survey data
The biggest boost you can give your AI analysis is a clear, context-driven prompt. Here are examples that work well for Ex-Cult Member surveys about peer support needs:
Prompt for core ideas: Use this to extract main themes from qualitative responses:
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
Always provide context: The more you tell the AI about your survey's background, the better it performs. If your goal is to understand what ex-cult members most want in peer support, say so. Here’s a simple example:
Here are responses from a survey for ex-cult members about their peer support group needs. Please focus on what support activities or group features they value most, and highlight recurring themes.
Dive deeper into themes: After identifying a core idea (“emotional safety”), follow up with:
Tell me more about emotional safety.
Prompt for specific topic: To check if any respondents touched on a certain idea, use:
Did anyone talk about professional facilitation? Include quotes.
Other prompts that fit Ex-Cult Member peer support topics:
Personas prompt: “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.”
Unmet needs & opportunities: “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
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.”
If you want more inspiration for what to ask, try these best questions for ex-cult member peer support surveys.
How Specific handles analysis by question type
Specific automatically adapts its analysis approach based on each question type:
Open-ended questions (with or without follow-ups): You get a summary for every open-text response—and separate summaries if follow-up responses are present, giving you detailed explanations behind each answer.
Choices with follow-ups: For each multiple-choice answer, Specific summarizes all related follow-up comments. This means you understand not just what was chosen, but why.
NPS questions (Net Promoter Score): Specific groups promoters, passives, and detractors, providing separate summaries for each group’s follow-up answers. You instantly see what drives promoters and what frustrates detractors.
You can recreate this with ChatGPT, yet it requires a manual approach: sorting results by hand, filtering each group, and repeatedly feeding them into the AI—an approach that quickly becomes a chore for larger data sets.
You can learn more about Specific’s deep-dive approach on its AI survey response analysis page.
AI context limits and practical solutions for large survey datasets
AI tools like GPT have context size limits—they can only “see” a certain amount of text per prompt. This matters when you have hundreds of open-ended survey responses, which is very common in ex-cult member peer support research (and a big reason why manual analysis gets so hard).
Specific solves this issue out of the box:
Filtering: Narrow down conversations so only those where respondents answered certain questions (or picked specific options) are analyzed by the AI. This keeps your prompt within context size while still focusing on your priority topics.
Cropping: Select only the questions you want to send to AI for analysis. By focusing just on, say, "How could this group better support you?", you maximize data density in your prompt and avoid hitting context limits.
If you’re using ChatGPT or another general-purpose GPT model, you’ll need to manually filter and curate your data first, which takes more time but follows the same core ideas.
Collaborative features for analyzing Ex-Cult Member survey responses
Collaborating on qualitative survey analysis is rarely simple. Ex-cult member peer support group data can be highly nuanced, requiring multiple perspectives to truly understand what people need. When you’re in a team—whether you’re researchers, facilitators, or peer supporters—sharing insights, building on each other’s questions, and seeing what’s been covered becomes a bottleneck in most tools.
Analyze by chatting: In Specific, everyone on your team can chat with AI directly about the survey results. No need to coordinate spreadsheet exports or worry about duplicate work.
Multiple chats & visibility: Each team member can spin up their own chat thread around a particular focus—like motivations, group dynamics, or common pain points—while seeing who started each. This is especially handy for surfacing diverse perspectives on tough topics.
Real-time collaboration: Every chat displays the sender’s avatar, so you always know whose question led to a given insight. As teams dive into different sub-sets of the data (filtered by question, demographic, or NPS group), everyone stays on the same page, building institutional knowledge and avoiding data silos.
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