This article will give you tips on how to analyze responses from an ex-cult member survey about financial stability using the right AI-powered methods for survey response analysis.
Choosing the right tools for analyzing ex-cult member survey responses
Your approach to analyzing survey data really hinges on the structure of what you’ve collected. For quantitative data—things like, “How many people agreed with statement X?”—tools like Excel or Google Sheets let you quickly count, filter, and visualize results.
Quantitative data: If your survey includes yes/no answers, ratings, or other select-all-that-apply questions, you’ll get straightforward numbers. Excel or Google Sheets make quick work of tabulating these responses, finding percentages, and building basic charts.
Qualitative data: It’s a different story when you have open-ended questions or detailed follow-ups—especially with personal and sensitive topics like financial transitions after leaving a cult. Reading dozens (or hundreds!) of responses isn’t realistic, and this is where AI tools come into play for pulling out patterns, themes, and insights you wouldn’t spot manually.
There are two main approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If your data is already exported (say, as a CSV), you can copy and paste responses into ChatGPT or another GPT-powered chat tool and get instant insights. This approach works for small data sets and quick explorations, but you’ll hit friction pretty quickly:
Data size limitations and messy exports can make it inconvenient. Copying and pasting isn’t ideal for a rigorous analysis. You’ll also need to clean your data manually and keep track of prompts and answers somewhere else. For deep dives—especially around life-altering topics like financial stability post-cult—this gets tedious fast.
All-in-one tool like Specific
Platforms like Specific are designed for exactly this job—collecting open-ended responses (with smart, automated follow-ups) and analyzing everything through built-in, AI-powered discovery. When you collect survey data with Specific, the platform instantly summarizes all responses and finds recurring themes. Its AI-driven approach means:
Automated followup questions during the survey improve data quality, surfacing context you’d never get from rigid forms. (Learn more about automatic AI follow-ups)
Immediate, actionable insights from qualitative text—discover challenges around financial independence, fears, and new opportunities, all without sifting through text one by one.
Chat directly with AI to ask your own questions. You can focus on specific subgroups, filter by answers, or dig into quotes—similar to ChatGPT, but deeply integrated with your survey data and with features for managing what’s sent to AI.
You can compare Specific to other established solutions as well. Tools like NVivo, MAXQDA, and Atlas.ti are common in research settings—and increasingly add AI features, like automated coding and sentiment analysis. NVivo, for instance, is known for AI-driven coding suggestions, supporting thematic deep dives with less manual grunt work [1]. Looppanel and Thematic take similar approaches, using AI to pull out core themes, automate sentiment, and assist in surfacing patterns in large-scale qualitative data [2][3].
Useful prompts that you can use for ex-cult member financial stability survey analysis
When you have open-ended responses—experiences, worries, or financial strategies—it’s all about asking the right questions to your AI tool. My favorite method is to use precise prompts that cut through the noise and structure the outputs in an actionable way. Here’s what works especially well for this audience and topic:
Prompt for core ideas: Use this in Specific, ChatGPT, or any GPT-powered tool to quickly surface the main topics ex-cult members mention when talking about financial stability:
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
Pro tip: AI always performs better if you give it more context about your survey—explain that it’s an ex-cult audience, that you’re researching struggles with personal finance, or that you want to prioritize actionable insights. For example:
You are analyzing responses from people who have recently left controlling communities and are adjusting to mainstream financial systems. Extract themes, ideas, and common worries, especially those relating to regaining independence or securing employment.
Prompt for follow-up exploration: After you’ve surfaced a core idea—say, “job insecurity”—try this:
“Tell me more about job insecurity (core idea)”
Prompt for specific topics: To check whether anyone discussed a particular pain or idea:
“Did anyone talk about dealing with debt?”
Tip: You can add “Include quotes.”
Prompt for pain points and challenges: Surveys on financial stability with this audience surface lots of challenges. Ask:
“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 and drivers: Understanding why ex-cult members make certain financial decisions is crucial for meaningful action. Try:
“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 broad sense of optimism or hesitance:
“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.”
Prompt for unmet needs and opportunities: If you want to learn where to support this audience most effectively:
“Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
How Specific analyzes qualitative data based on question type
The power of a tool like Specific isn’t just that it can analyze thousands of words at once—the platform instantly adapts its summaries based on the structure of your survey questions:
Open-ended questions (with or without follow-ups): You get a summary for all responses, as well as for follow-up answers attached to each main question. This gives rich, contextual summaries without the manual work.
Choices with follow-ups: Each choice (for example, “primary source of income”) gets its own summary of all the associated follow-up responses. It’s easy to compare how different approaches to income, saving, or coping are discussed in depth.
NPS: For surveys using Net Promoter Score to gauge satisfaction or likelihood to recommend, Specific pulls out reasons for each group (detractors, passives, promoters), putting the supporting comments into context.
You can also do all this in ChatGPT by grouping your data and summarizing responses for each question or answer—but it’s definitely more labor intensive and easy to lose track.
Managing AI context limits with larger survey data sets
One big technical challenge with AI survey analysis is the context window. Language models like GPT can only process so many words at once—if you have 500+ survey responses, they simply won’t fit. In Specific, you can handle this in two smart ways:
Filtering: Limit analysis to only those conversations where participants replied to selected questions or gave specific answers. For instance, focus only on ex-cult members who reported “job loss,” or who scored low on financial wellbeing.
Cropping: Decide which questions matter most, and crop out the rest before sending to the AI for summary. Maybe you only want thematic analysis for the main “financial challenges” question and its follow-ups.
Both filtering and cropping help you stay under the technical context limit, while still ensuring you get useful, actionable insights from a manageable data slice.
Collaborative features for analyzing ex-cult member survey responses
Collaboration is a real pain point when several people need to dig into sensitive survey topics—especially with tough, nuanced responses like those from ex-cult member financial stability studies. Keeping track of who analyzed what, consolidating notes, and sharing insights can spiral out of control in a spreadsheet or chat export.
In Specific, you interact directly with AI about your survey data inside chat-based threads, making analysis fast and structured. The real magic is that you can spin up multiple chats—each chat has its own focus and filters, like “income instability deep dive” or “first jobs after leaving.”
Track contributors with chat avatars. Each message in an AI chat is tagged with the sender, so if you invite colleagues to analyze or comment, you instantly see who asked each question or provided feedback. This is huge for transparency and organizing collective learning, especially when you’re gaming out interventions or policy recommendations together.
Share insights and findings directly within the platform, without copy-pasting results into docs or emails. You can compare thematic summaries, verify findings from ChatGPT, or create consensus before exporting highlights.
Create your ex-cult member survey about financial stability now
Start gathering honest, nuanced insights from ex-cult members on financial stability and let AI-powered analysis illuminate the patterns in minutes, not months.