This article will give you tips on how to analyze responses from a SaaS customer survey about training. If you’re trying to make sense of detailed customer feedback, you’ll want an efficient process for turning data into real insights.
Picking the right tools for analyzing SaaS customer feedback
The right approach—and tools—for analyzing survey data depend on the kind of answers you’ve collected. Let’s split it:
Quantitative data: These are answers like rating scales or checkboxes, where you just want to count how many people chose each option. Tools like Excel or Google Sheets are perfect for this job—they let you crunch the numbers and identify trends quickly.
Qualitative data: Think open-ended questions or free-text follow-ups. Reading all the answers manually is painful (if not impossible for large surveys), and traditional tools don’t help much. That’s where AI comes in, letting you sift through lots of text to find what actually matters—and fast.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy exported survey responses into ChatGPT (or similar) and chat directly about the data. You can ask questions, get summaries, and drill down into specific themes.
The downside: It’s not super convenient. You’ll need to format your data into text, watch out for context size limits (more on that later), and lose out on dedicated features for surveys, like segmenting by question type or answer.
All-in-one tool like Specific
Tailored for surveys: Specific was built for this exact scenario—it both collects data via conversational surveys and uses AI to analyze responses. When someone gives a brief answer or the AI senses more detail is needed, it asks smart follow-up questions automatically, leading to much richer data. (See how automatic follow-ups work.)
Instant analysis: Once you have responses, Specific’s AI-powered analysis summarizes them, identifies key topics, and surfaces actionable themes—immediately and without sorting through spreadsheets or exporting CSVs.
Interactive results: You can chat directly with the AI about your results (similar to ChatGPT, but with context-specific tools for handling different survey questions and segments). This lets you filter, compare, and dive deep into the nuances—no extra exports needed.
Want to quickly create a survey like this? There’s a one-click NPS survey builder tailored for SaaS customer training.
Useful prompts that you can use to analyze SaaS customer training survey data
Asking the right questions makes all the difference when analyzing qualitative responses—whether via an AI tool like Specific or with ChatGPT. Here are prompts and techniques you can use to dig deeper into your SaaS customer training data:
Core ideas prompt: The go-to for extracting key topics from dozens (or thousands) of survey entries. This works great in both ChatGPT and Specific, and is even built-in as a default in many AI survey tools:
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 tell it more. If you give context about your survey’s background, who took it, or the specific business goal, you get much sharper insights. For example:
Here’s the background: We’re a B2B SaaS product serving HR professionals. This is our annual training feedback survey aimed at understanding onboarding effectiveness, self-serve adoption, and where customers feel blocked. Please keep this in mind when summarizing responses.
Drill-down prompt: When you spot a topic or issue, follow up with: “Tell me more about XYZ (core idea)” to get a deep dive into any pattern.
Spot check prompt: “Did anyone talk about onboarding challenges?”—or whatever theme you want to check for. Add “Include quotes” for verbatim support.
Persona building prompt: Want to group users by their needs? Try: “Based on the survey responses, identify and describe a list of distinct personas—summarize their key characteristics, motivations, goals, and any relevant quotes.”
Pain points prompt: “Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each and note any frequency or patterns.”
Motivations prompt: “From the survey conversations, extract the primary motivations for participating in or skipping training. Group them, and support with representative quotes.”
Sentiment analysis prompt: “Assess the overall sentiment (positive, negative, neutral) expressed in the survey responses. Highlight phrases that best represent each category.”
Suggestions prompt: “List all suggestions, ideas, or requests around training—organize by frequency and topic.”
Unmet needs prompt: “Highlight any unmet needs, gaps, or opportunities for improving our training program found in the responses.”
Want better questions in your next survey? Check this guide on writing the best questions for SaaS customer training surveys.
How Specific tackles different types of survey questions
Not all survey questions are alike, so AI analysis should adapt to the question type. Here’s how Specific nails it:
Open-ended questions (with or without follow-ups): You get a summary of all respondent answers, grouped with any follow-up responses tied to that main question. This means you don’t lose important context or nuance.
Choices with follow-ups: If a multiple-choice question has a follow-up for each option, specific analyzes responses by choice—so each option gets its own breakdown, complete with supporting comments and extra detail.
NPS: Specific automatically groups feedback by detractors, passives, and promoters, summarizing follow-up responses for each group.
You can do the same breakdown manually in ChatGPT, but it’s a lot more laborious—copying, filtering, and chunking input each time.
Want a crash course in building these surveys? See how to create a great SaaS customer survey about training.
How to handle context limits when using AI for analysis
Running into context size limits? Large surveys with hundreds of responses often exceed what AI tools can process at once. You don’t want your analysis to miss valuable insights because not all data fit in a single prompt.
There are two tried-and-tested ways to deal with this (and Specific handles both automatically):
Filtering: Analyze only the responses that matter—filter conversations by those who replied to certain questions or chose specific answers. This keeps things focused and ensures AI crunches relevant data.
Cropping: Limit the analysis to selected questions—send just those items to the AI. This keeps you within size limits and still covers the key areas you care about.
For broader context, read our in-depth piece on using AI to analyze survey responses.
Collaborative features for analyzing SaaS customer survey responses
Team collaboration is a common pain point when analyzing survey data: everybody wants the same insights, but filtered through their own lens—CX, product, training, and support may all want different views.
Chat with AI together: With Specific, anyone on your team can simply open a chat and ask the AI about the survey results—no extra training, data exports, or meetings needed.
Multiple conversations, zero confusion: Every analysis chat can have its own filters (e.g., only power users, only detractors). Each team member sees who started a chat, what’s been discussed, and can jump straight into the threads that matter to them.
Transparent collaboration: In each chat, avatars indicate who contributed which insights. This keeps handoffs and reviews clear, and boosts confidence in shared decisions.
Curious how it works? Try the collaborative AI analysis feature here, or check out the AI survey editor for easy survey editing across the team.
Create your SaaS customer survey about training now
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