This article will give you tips on how to analyze responses from an ex-cult member survey about support services satisfaction using the right combination of AI survey analysis tools and strategies.
Choosing the right tools for analysis
Your approach and tooling will depend on the structure and format of the survey data you collect. Here’s the breakdown:
Quantitative data: Total counts, like how many ex-cult members are satisfied or dissatisfied, work best with classic tools like Excel or Google Sheets. You simply tally, calculate, and visualize the numbers.
Qualitative data: It gets trickier with free-form responses—think open-ended answers or follow-up explanations. Reading every response by hand becomes impossible fast. AI tools step in here. They scan through huge blocks of text, pinpoint recurring themes, and help you see the bigger picture. This is especially crucial for support services satisfaction surveys, where nuance matters.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you use ChatGPT-style models, you can copy your exported survey data directly into the chat and start interacting with it. Want to spot themes, trends, and emotional cues among ex-cult member responses? Just ask.
But there’s a catch: Transferring big datasets is awkward. Context limits can force you to break responses up or lose track of the overall narrative. Nuances can fall through the cracks because the tool doesn’t "know" anything about your survey logic.
All-in-one tool like Specific
This is where a purpose-built AI survey analysis tool like Specific shines. Specific can both collect the data in a conversational format (with follow-up questions triggered by the AI in real time), and then analyze responses using powerful GPT-based models that instantly summarize, surface themes, and highlight actionable insights—no manual spreadsheets or busy work.
A big win: By using dynamic AI follow-up questions, surveys elicit richer, more detailed responses from ex-cult members, delivering more context for each answer.
Afterward, you can chat with the AI about the results—just like ChatGPT, but with far more context control and survey-specific intelligence. Filtering, segmenting, exporting, and managing which data gets sent for analysis are all built in. This means deeper, more reliable support services satisfaction insights and less friction for your research flow.
Useful prompts that you can use to analyze ex-cult member support services satisfaction data
The real magic in AI survey response analysis happens when you use well-crafted prompts. Here are battle-tested prompts designed for understanding ex-cult member experiences and the effectiveness of support services:
Prompt for core ideas: This is my go-to prompt for surfacing the major themes in a sea of 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
Tip: Always provide more context to the AI. For example, give details about how you recruited ex-cult members, the type of support services covered, or what outcomes you hope to improve with this analysis. Here’s a handy example:
Analyze these responses from ex-cult members who have used peer support groups, counseling, and emergency housing services in the last year. I want to understand what is working, what’s missing, and how these experiences compare to their needs post-exit.
Want to dive deeper on a specific issue? Just use this intuitive prompt:
Prompt to expand: "Tell me more about XYZ (core idea)"
Prompt for specific topic: Use to validate whether a certain issue comes up: "Did anyone talk about emotional safety?" (Feel free to add: "Include quotes.")
Prompt for personas: Extract the spectrum of voices present in your data: "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 is crucial for actionable insights: "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: Get to the why: "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 Suggestions & Ideas: Gather practical feedback: "Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant."
If you want even more ideas, check out this guide on best questions for ex-cult member support services surveys or experiment with different prompt styles in the tool.
How Specific analyzes qualitative data from different question types
Different question types produce different challenges when analyzing qualitative data. Here’s how Specific handles each automatically so you don’t have to tinker with raw text:
Open-ended questions (with or without follow-ups): You get a summary of all the answers, including explanations and stories from any automatic follow-up questions.
Choices with follow-ups: Each possible choice clusters and summarizes its relevant follow-up responses, helping you see, for example, how those who chose group therapy versus hotline support experienced services.
NPS questions: Specific categorizes promoters, passives, and detractors, and gives you a targeted summary for each group based on their follow-up responses. That lets you quickly spot what delights, what annoys, and what just... doesn’t move the needle for your audience.
You can replicate a version of this workflow with ChatGPT or other GPT tools—but it’s more labor-intensive, as you’ll need to prepare, filter, and submit data for each branch manually.
If you want to build a survey with these flows, the AI survey generator for ex-cult member support services satisfaction is a good starting point.
Working around AI context size limits in survey response analysis
A major limitation of using general-purpose AI tools for survey analysis is the context size—how much data the AI can truly "hold" and process at once. When you have a lot of ex-cult member responses, you’ll run into this wall sooner than you think.
There are two reliable strategies—both built into Specific—to overcome this:
Filtering: Narrow down which conversations or responses the AI looks at, such as only those mentioning a particular support service, or responses to selected open-ended questions.
Cropping: Select just the questions you want analyzed (e.g., only follow-up comments on satisfaction), reducing noise and fitting more high-value content into the context window.
Using these approaches, you’ll bypass the common pain point that holds back many teams working with complex survey datasets. According to qualitative research experts, up to 70% of manual analysis time is spent just sorting and filtering responses before any real insight emerges. [1]
Collaborative features for analyzing ex-cult member survey responses
Team analysis often hits friction—especially when researchers and advocates need to review support services feedback together. It’s easy to lose track of which findings came from whom, or which themes have been vetted by more than one stakeholder.
With Specific, you and your colleagues can chat with the AI about the ex-cult member survey results in real time—each analysis chat is a separate thread. You can apply filters in every thread to isolate comments about, for example, counseling or emergency housing, making deep dives and collaborative reviews easier to manage.
Multiple chats mean you don’t step on each other’s toes—every chat is clearly attributed with the creator’s name, so you always know who discovered each insight. Sender avatars appear on all chat messages, so group reviews feel clear and organized, not like a shared Google Doc gone wild. This is a huge upgrade versus the chaos of sharing files via email or sifting through endless spreadsheet comments.
All these tools are built with social impact leaders and researchers in mind, but accessible to anyone—no complex setup or "researcher credentials" required. For an in-depth, step-by-step guide to building surveys for this community, check out this post on creating ex-cult member surveys about support services satisfaction.
Create your ex-cult member survey about support services satisfaction now
Get actionable feedback and richer context from ex-cult members in minutes—Specific’s conversational AI surveys instantly elevate your support services satisfaction analysis with smarter follow-ups and instant insight generation. Create, launch, and analyze in one place—your next step toward real impact starts today.