This article will give you tips on how to analyze responses from an online course student survey about pricing and value. Whether you’re new to survey analysis or looking to up your game, here’s what you need to know.
Choosing the right tools for survey response analysis
The best tool for analyzing your survey depends on the structure of your data and the types of answers you collected. Let’s break it down:
Quantitative data: If you’re looking at numbers—like how many students selected a certain price range or rated value from 1 to 5—spreadsheets like Excel or Google Sheets are your go-to tools. You can easily create charts, count votes, and visualize the distribution.
Qualitative data: When you have open-ended responses, students’ explanations, or their personal stories about why a course’s price felt fair or not—those can’t be effectively counted or graphed by hand. Here, you’ll want to use an AI tool to process and summarize those insights. With responses piling up, reading them all yourself gets overwhelming fast, especially when dozens or hundreds of students elaborate in their own words.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste workflow: You can export your survey data and paste it into ChatGPT or another generic language model. Then, you can prompt the AI to summarize feedback, extract themes, or answer specific questions about your data.
Convenience limitations: This approach works but isn’t particularly convenient—especially for large data sets. You’ll need to format your data, manage prompt limits, and manually keep track of which pieces you’ve analyzed.
All-in-one tool like Specific
Purpose-built for surveys: With a survey platform like Specific, you get a tool that handles both survey distribution and AI-powered response analysis in one place.
Smarter data collection: Specific uses AI to ask follow-up questions in real time, so you’ll get richer details and quality data. That means you’re not just hearing that students think a course is “expensive”—you’ll know why, what they were expecting, or what value aspects weighed most in their decision.
Effortless AI analysis: Specific automatically summarizes open-ended feedback, finds patterns in what students are saying about pricing and value, and creates actionable insights—no spreadsheets or manual copy-paste needed. You can also interact with your data via chat, much like ChatGPT but focused entirely on your survey results and custom context. Additional features let you manage which data you’re chatting about, ensuring you always keep focus.
Useful prompts that you can use to analyze Online Course Student Pricing and Value feedback
Prompts are where the magic happens. Whether you use ChatGPT, Specific, or another AI tool, well-crafted prompts give you targeted, actionable results. Here’s how you can get better insights using these examples:
Prompt for core ideas: If you need a big-picture summary of what students really think about course pricing and value, this is my go-to prompt. It’s the backbone in Specific, but also works great in ChatGPT.
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
You’ll get back a list of bulletproof themes, like “Fair pricing for content quality,” with concise explanations and stats. That’s often the most actionable place to start.
Boosting AI performance with context: Always give AI more context—describe your survey’s purpose, share the background, or clarify your goal. Here’s how you can tell the AI what it should know up front:
Please remember: This survey was conducted among online course students to understand their opinions about the pricing and value of the courses they enrolled in. The goal is to uncover what factors influence perceived value and willingness to pay.
Prompt for digging deeper into an idea: Once you spot a theme, ask: “Tell me more about [core idea].” The AI will break down specifics, supporting details, or key quotes about that topic.
Prompt for validating a specific topic: To see if students cared about a certain point—maybe “payment plans” or “lifetime access”—use:
Did anyone talk about [topic]? Include quotes.
Prompt for personas: If you want to segment your audience, 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: To surface frustrations or challenges with course value or pricing, use:
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: Understand why students decide that a course is worth its price—or not:
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: To get a sense of general mood around pricing and value:
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.
Want to dive deeper into the best questions to ask in this type of survey? Or if you’re still adjusting your surveys on the fly, check out our AI survey editor for modifying questions in plain language.
How Specific analyzes qualitative data based on question type
In Specific, how the AI summarizes and analyzes your survey responses depends on the question type. Here’s how it’s structured:
Open-ended questions: You’ll get a unified summary of student responses—including any follow-ups the AI conducted—providing you with a crisp overview of the entire conversation related to that topic.
Choices with follow-ups: Each choice (e.g., “I chose this course because of price”) is summarized separately. That way, you can see what drove specific groups of respondents—whether they value affordability, extra features, or instructor credentials.
NPS (Net Promoter Score): Responses from promoters, passives, and detractors are automatically split into their own summaries. Each group’s feedback is analyzed in context, making it easy to understand why certain students are satisfied, neutral, or dissatisfied with pricing and value.
If you use ChatGPT, you can get similar breakdowns, but it takes more effort—ideally by segmenting and pasting the relevant responses and prompting the AI per group or question.
Tips for tackling context limits in AI tools
AI tools—like ChatGPT or even advanced survey platforms—have context or character limits. When you’re working with dozens or hundreds of detailed student responses, you may hit those limits fast.
To solve this, there are two common strategies. Both are built into Specific, but you can apply them yourself too if you’re DIY-ing:
Filtering: Only include conversations (responses) where students answered certain questions or selected specific choices. That way, your analysis zooms in on what you care about most, and you stay under the context cap.
Cropping questions: Send only selected questions to the AI rather than entire conversations. For example, ask the AI to analyze just the question about price sensitivity, leaving out unrelated topics. This approach lets you cover more data, more accurately.
Both filtering and cropping help you maximize insights from a large qualitative survey, without losing depth or detail.
Collaborative features for analyzing online course student survey responses
Collaborating on survey analysis is a perennial challenge. With pricing and value research, you often need to share findings or discuss interpretations across your research team, program staff, or course creators.
Effortless collaboration within Specific: You can simply chat with AI to analyze the data—no need for everyone to have separate logins, downloads, or messy spreadsheets. Multiple AI chats can run in parallel, each focusing on a different subset (like a specific cohort or topic). Each chat shows who started it and what filters are applied, so you’re never confused about which analysis you’re looking at.
Clear ownership and discussion threading: When you’re collaborating, seeing who asked what question or who contributed which insight is vital. Every chat message in Specific displays the sender’s avatar, making it a breeze to track conversation context and co-analyze efficiently.
Perfect for team reviews or stakeholder sharing: These collaborative features let you build up insight over time, revisit analyses, and onboard newcomers without losing continuity or context. That’s especially handy when validating pricing strategies or value messaging for your next course cohort. If you want to get started, you can try generating a new survey for online course students about pricing and value or explore options for building your own AI survey from scratch.
Create your online course student survey about pricing and value now
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