This article will give you tips on how to analyze responses from an inactive users survey about pricing concerns using AI-powered tools and strategies.
Choosing the right tools for analyzing survey data
The best way to approach survey analysis depends on whether you’re looking at quantitative or qualitative data. Here’s how I tackle each type:
Quantitative data: For things like “How many users selected concern X as the top pricing issue?”, basic counts and percentages work well. You can use Excel or Google Sheets to crunch the numbers fast.
Qualitative data: When your survey gathers open-ended responses—especially follow-ups—it’s simply impossible to read everything or spot every pattern yourself. This is where AI tools shine. They digest large volumes of text and surface what really matters.
When dealing with qualitative responses, you generally have two solid tooling approaches:
ChatGPT or similar GPT tool for AI analysis
Export and copy data: Many teams copy their text response exports straight into ChatGPT or a similar GPT-powered tool and “chat” through the data.
Manual setup and limitations: It’s direct, but often clunky. You paste a bunch of text, tweak your prompt, sometimes hit response length limits, and can struggle to keep context or manage follow-ups. Insights are valuable, but it can get messy with big data sets.
All-in-one tool like Specific
Baked-in survey & analysis workflow: Specific is made for this. It collects qualitative data with a conversational AI and then instantly turns those responses into summaries, themes, and actionable insights—all inside the platform. See how AI survey response analysis works in Specific.
Automated follow-up questions: While users answer your pricing survey, Specific’s AI asks smart follow-ups to dig deeper—giving you richer context and far better data than static forms. (Learn about automatic AI follow-up questions.)
No spreadsheets, just insights: Your analysis happens instantly and conversationally: chat with the results like you would with ChatGPT, but with survey-specific context, advanced filtering, and easy sharing.
Additional controls: In Specific, you can manage exactly which questions/responses get analyzed, keep track of themes over time, and segment by user type—all inside one place. This makes it far easier to focus on “inactive users” and their unique pricing friction points.
Backed by research: AI-powered survey tools can cut your data interpretation time in half, according to Forrester Research [1]. Gartner found they improve qualitative analysis accuracy by 30% [3].
Useful prompts that you can use to analyze inactive users' pricing concerns
Prompts supercharge your AI analysis, especially when you ask the right questions about pricing pain points and inactive users. Here are my go-to prompts for teasing out real insights from survey data.
Prompt for core ideas: If I want top-level themes from hundreds of responses, this prompt never fails—no matter which GPT-based tool I’m using:
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: Always include contextual info (like details about “inactive users,” your company's pricing plans, etc.) in your initial prompt. It makes a huge difference. For example:
Analyze these survey responses from inactive users who recently canceled due to pricing concerns. Our main goal is to understand their top objections, hidden pain points, and what would make them consider re-subscribing. Highlight any themes related to feature value, competitor comparisons, or suggested price points.
Once you’ve surfaced key ideas, you can go deeper: “Tell me more about [core idea]” will break down specific concerns—perfect if a trend like “lack of affordable plan” stands out.
Prompt for specific topic: To quickly check for a particular angle (“Did anyone mention price compared to competitors?”), just ask:
Did anyone talk about competitor pricing? Include quotes.
Prompt for personas: Understand if there are distinct groups among your inactive users who care about different issues.
"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: Zero in on what’s driving pricing complaints.
"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: Go beyond pain points to see what would re-engage these users.
"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: Assess if overall sentiment is negative, neutral, or maybe mixed about your pricing.
"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."
For more on question strategy, check this article on the best questions for inactive users surveys about pricing concerns.
How Specific analyzes qualitative survey data by question type
Open-ended questions (with/without follow-ups): You get a summary of all user responses—including any deep-dive follow-up answers. This creates a map of what topics really matter to users who gave longer feedback.
Choice questions with follow-ups: Each answer choice (“I found it too expensive,” etc.) gets its own analysis. The AI summarizes only the follow-up responses linked to that selection, which shows exactly why pricing is a barrier for each group of users.
NPS questions: The AI splits the data based on detractors, passives, and promoters. Each category is summarized with a focus on the pricing issues relevant to that group—perfect for zeroing in on those most at risk of churn.
You can do the same type of segmented analysis by hand in ChatGPT, but it’s much more manual: copying, filtering, and writing custom prompts for each scenario.
If you’re designing a survey and want to see these question types in action, try the AI survey generator for inactive users surveys about pricing concerns.
Addressing challenges with AI context limits
Analyzing large survey data sets with GPT-models comes with one major constraint: context window limits. If you have a mountain of inactive users’ responses—especially on pricing—your data may not fit in one go.
There are two effective workarounds (both built into Specific):
Filtering: Only send responses from conversations where users replied to the pricing question (or a specific follow-up) to the AI for analysis. This massively reduces context while keeping relevance high.
Cropping: Narrow down which survey questions are included in the AI analysis. For pricing concerns, you can crop just the related questions—this lets you pull out more data from a larger user set without losing context.
Not only does this keep things manageable, but it also helps you focus in on the core reasons inactive users dropped off, rather than drowning in unrelated feedback. Competitive teams using AI do this as a best practice. For a deeper dive, see this breakdown of filtering and cropping in Specific's response analysis.
Collaborative features for analyzing inactive users survey responses
Survey analysis is rarely a solo effort. If you’re running a pricing concerns survey for inactive users, odds are that colleagues in product, research, and customer success all want in on the action.
Real-time chat analysis: In Specific, you analyze your survey results by chatting directly with AI—no need for multiple exports or emailing files back and forth.
Multi-chat workflow: Want to look at churn, price elasticity, and competitor themes separately? Just create multiple chats, each with their own question filters or audience focus. Everyone sees who created each chat, making it easy to coordinate across roles and time zones.
True collaboration: Every AI chat shows the sender’s avatar and name—so when your head of growth spots a trend, you know where the insight is coming from. This eliminates the confusion that clutters traditional survey tools.
Optional segmentation: Filter chats to analyze just respondents who share specific price concerns or who fit a high-value persona. No need to wait on IT or write your own scripts.
To see how easy it is to set up this kind of shared, focused survey analysis, check out this how-to guide for creating inactive users surveys about pricing concerns.
Create your inactive users survey about pricing concerns now
Start capturing better insights in less time with AI-powered, conversational surveys—uncover why inactive users disengage around pricing and turn that feedback into real action.