This article will give you tips on how to analyze responses from a SaaS customer survey about value for money using AI and modern survey analysis techniques.
Choosing the right tools for survey response analysis
The best approach and tooling for survey analysis depends on the structure of your data:
Quantitative data: Numbers and counts (for example, “How many users chose Option A?”) are straightforward—Excel or Google Sheets get the job done.
Qualitative data: Answers to open-ended or follow-up questions are a different beast. You can’t just read 300 long replies—AI tools, like modern GPT-based solutions, are your best bet for fast, thorough analysis.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export your survey data and paste it into ChatGPT or a similar GPT interface. Then, chat with the AI to dig for insights or have it summarize feedback.
Limitations: While it’s flexible, this approach isn’t ideal for large data sets or ongoing analysis. You’ll have to manage data formatting, keep an eye on prompt limits, and manually group or filter responses. It’s workable for a quick one-off, but quickly gets tedious for big surveys.
All-in-one tool like Specific
An AI-powered survey platform like Specific brings the whole workflow into one place. You can both create your survey (collecting rich, open-ended responses, thanks to automatic AI follow-up questions) and analyze results in a single dashboard.
Why it stands out: When collecting feedback, Specific’s AI can ask real-time follow-up questions that clarify initial answers—boosting the depth and clarity of the feedback you’ll analyze later. You don’t have to worry about missing context or shallow answers. (learn more about AI follow-up questions)
For analyzing qualitative data, Specific instantly summarizes and distills all responses, surfaces key themes, and lets you chat directly with the AI about anything in your data—just like talking to ChatGPT. You can also filter or crop responses before AI gets to work, making it efficient for any survey size. (see how AI survey response analysis works in Specific)
Studies show that leveraging AI and NLP in survey analysis greatly enhances the quality and utility of insights derived from open-ended responses [1]. Businesses save both time and receive higher-quality insights than manual analysis.
Useful prompts that you can use to analyze SaaS customer survey responses about value for money
Whether you’re using ChatGPT, Specific, or another AI-driven platform, the prompts you give the AI heavily influence the quality of the insights you get. Here are proven prompts for analyzing SaaS customer survey data about value for money:
Prompt for core ideas: Use this to instantly extract and rank the main themes. (This is the default in Specific, but you can use it elsewhere too):
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
Give AI context up front: The more you explain—survey background, goal, and business context—the better. For example:
You are analyzing survey data from current SaaS customers on how they perceive our pricing and product value. Our main goal is to identify concrete actions to improve value for money and reduce churn. Please extract and prioritize the main themes, giving extra weight to comments from high-value customers.
Explore individual themes: Use, “Tell me more about XYZ (core idea)” to dig deeper into any specific trend surfaced in your results.
Prompt for specific topic: Want to see if anyone mentioned a particular product, feature, or price point?
Did anyone talk about XYZ? Include quotes.
Prompt for personas: For customer segmentation and understanding who says what, try:
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: Great for surfacing friction—what’s costing you customers:
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 sentiment analysis: Uncover the emotional tone (e.g., how strong the "value for money" perception is):
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 & opportunities: Discover quick wins or big product gaps:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For more inspiration and ready-to-go survey templates, check out best questions for SaaS customer surveys about value for money, or try the AI-powered SaaS customer survey generator.
How Specific summarizes and analyzes different types of qualitative responses
With Specific (or a mix of GPT tools and manual sorting), here’s how qualitative feedback analysis works by question type:
Open-ended questions (with or without follow-ups): The platform provides a single summary that covers all user responses—including the detailed clarifications and extra insights that follow-ups bring.
Choices with follow-ups: Each selectable option receives its own summary, drawing from all related follow-up responses. This makes it clear why people chose what they did, at a glance.
NPS (Net Promoter Score): Responses are grouped and summarized for each NPS bucket (detractors, passives, promoters). You immediately see what each segment values or dislikes.
You can achieve a similar outcome with ChatGPT, but it requires a lot more copy-pasting, filtering, and manual prompting per question and answer type.
Tackling large data sets and AI context limits in survey analysis
AI context limits: All GPT-based tools have “context size” limits—the maximum amount of data (survey responses) you can feed into the AI at once. When you have hundreds or thousands of replies, not everything fits.
Specific’s solution: The platform offers built-in tools to avoid losing insights or running into context errors:
Filtering: Easily filter conversations based on user replies. For example, only analyze users who selected “Dissatisfied” or only those who answered a key follow-up. Only the most relevant conversations are sent to the AI for analysis.
Cropping: Choose specific survey questions to include in your analysis, so the AI focuses narrowly (e.g., only open-ended feedback about pricing instead of the whole survey). Both features keep your data within context size and mean you never have to throw out good feedback.
The UK government’s project using AI for survey analysis found that this kind of automated filtering and cropping led to rapid, cost-effective analysis even for thousands of responses—matching the results of expensive manual teams [2].
Collaborative features for analyzing SaaS customer survey responses
Collaborating on the analysis of SaaS customer value for money surveys can get messy fast. With lots of stakeholders, opinions, and data, you need tools that support teamwork—without bottlenecks or information getting lost.
Chat with AI, together: In Specific, you and your colleagues can analyze survey data by chatting directly with the AI in your dashboard. Discuss insights, ask new questions, and review the summaries instantly as a group—no downloads or exports required.
Multiple chats, multiple perspectives: You can run parallel AI chats, each with its own set of filters applied. For example, a product manager might look only at dissatisfied customers, while a marketing manager focuses on promoters. Each thread shows who created it—making it easy to compare findings and collaborate, even across departments.
Track who said what: In collaborative chats, every message is tagged with the sender’s avatar and name. This transparency is crucial when several people are analyzing and discussing the data—no lost context, no guessing who asked what.
Curious about building and customizing a survey for this very audience? Read how to create SaaS customer surveys about value for money or try the AI survey editor to see just how easy it is to fine-tune your questions.
Create your SaaS customer survey about value for money now
Don’t wait—get valuable, actionable insights from your SaaS customers on value for money in minutes with a conversational AI survey that does all the analysis for you.