This article will give you tips on how to analyze responses from inactive users surveys about competitor switching reasons. I'll show you the best options for breaking down your survey data, so you actually get insights—not just numbers.
Choosing the right tools for analyzing survey responses
Your approach depends on how your survey was structured and what kind of answers you got. Here’s what matters:
Quantitative data: If you ran a survey with multiple choice or rating questions (“How likely are you to stay with us?”), you can quickly analyze the counts in Excel or Google Sheets. This approach works well when you want to tally up reasons users give for leaving, like price or features.
Qualitative data: For open-ended responses (“Why did you switch to a competitor?”) or detailed follow-ups, you’re sitting on a goldmine of insights—but it’s impossible to read through everything by hand. AI tools really shine here, because they can identify themes, sentiments, and trends efficiently.
There are two main approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy-paste your exported survey data straight into ChatGPT or another general-purpose AI. Then, you just chat about the data and ask it to summarize or spot trends.
The advantage: It’s flexible and will work for most raw text.
The downside: Handling big lists of responses this way isn’t convenient. Formatting can get messy, and filtering responses by question or group is tricky. Plus, you'll bump into context length limits fast if your survey got decent traction.
All-in-one tool like Specific
Purpose-built solution: Specific is built exactly for these kinds of research scenarios—collecting and analyzing survey data in a single flow.
Smarter data collection: When you use Specific to collect survey responses, you unlock automatic AI follow-up questions, which capture richer responses right when your users answer (learn more about AI-powered survey followups).
Instant AI analysis: The platform instantly summarizes all responses, finds patterns, pulls out the top reasons users leave, and highlights actionable insights. There’s no need to mess with spreadsheets or copy-pasting anything. See more on AI survey response analysis.
Conversational analytics: You get a chat-like interface for digging into your results with AI. You can ask follow-up questions (“Did price come up much?”) and even filter which responses are analyzed. This method also avoids the context limitation issues that plague mainstream GPT tools.
Manageable data flows: Beyond just chatting, you have features that help you manage, filter, and segment which data gets sent to the AI at every step. If you want to generate a fresh survey—for the same inactive users and switching reasons—use the ready-made AI survey generator preset for inactive users or create a new one using the custom AI survey generator.
Useful prompts that you can use for analyzing inactive users survey responses
You’ll get the best results if you have good prompts for your analysis—whether you’re using Specific, ChatGPT, or any other GPT-based tool. Here are the key ones to try for inactive user and competitor switching reasons surveys:
Core ideas prompt: This works great for surfacing the top reasons users switched. Here’s a reliable prompt you can use:
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
Context boosts AI performance: If you give more info about your survey (e.g. “inactive users, competitor switching reasons, our goals, what context matters”), AI will produce far stronger insights. For example:
These responses are from inactive users who recently left our platform for competitors. We want to find actionable reasons why they switched, patterns by user type, and see how pricing or customer service is influencing decisions. Please extract the 5 most common core ideas and summarize each.
Drill deeper: Use “Tell me more about [core idea]” to unpack what drives a specific trend (like price sensitivity or feature gaps).
Spot mentions of a topic: If you want to quickly check whether people mention a particular issue, just ask:
Did anyone talk about [specific topic]? Include quotes.
Personas prompt: If you're aiming to segment your churned users by type, use:
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.
Pain points and challenges: Want to catalog frustrations?
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.
Motivations & drivers: To pinpoint what actually made inactive users switch:
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.
Sentiment analysis: If your audience is vocal, ask:
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 more in-depth prompt ideas? Check out this resource on the best questions for inactive users surveys about competitor switching reasons.
How Specific analyzes qualitative data by question type
The way responses are summarized depends on the question:
Open-ended questions (with or without followups): You get an AI summary for all responses to that question—including everything captured by automatic follow-ups. Insights tell you the common reasons for churn and rich explanations.
Multiple choice questions with followups: Each answer choice gets its own summary of the follow-up responses. For example, you can see all the extra reasons given by those who said “Price” vs. those who said “Features.”
NPS: Each NPS segment (detractors, passives, promoters) is summarized separately with all the feedback and reasons related to their score.
DIY with ChatGPT: You can do the same thing by manually pasting in response sets per question or segment, but it takes a lot more work—especially if you want to analyze by group or filter by choice.
Want to see how Specific does this in action? Explore the AI survey response analysis feature.
How to handle AI context limits when analyzing lots of survey responses
Context limits are real: most AI tools can only process a certain amount of text at once. If your inactive user survey had dozens or hundreds of responses, you’ll run into this quickly.
Here’s how to handle it (and what Specific does automatically):
Filtering: Only analyze conversations where users replied to certain questions (“Show me only users who complained about customer service”). This reduces data sent to AI and targets your analysis—key if you want to dig deep on price sensitivity (which, by the way, drives 41% of customer switching per Nielsen [1]).
Cropping: You can crop specific questions for AI analysis, rather than sending the entire conversation. That way, you focus on what matters—say, just the open-ended “why” after a user picks “features” as their main reason for switching.
Specific has these as built-in options, but you can always apply the same principles when chunking data for ChatGPT or other AI tools.
Collaborative features for analyzing inactive users survey responses
It’s hard to get meaningful analysis if you’re working alone, or if everybody’s reviewing a static spreadsheet. This is especially true for inactive users and competitor switching reasons surveys, where you want different teams—CX, growth, product, research—digging into the same set of data.
Chat-style collaboration: In Specific, you analyze survey data like you’re chatting with an AI. You can spin up multiple chats, each with different filters, so one teammate can focus on customer service complaints while another dives into feature requests.
Clear team visibility: Every AI chat displays who created it, making it easy to see which part of the team is working on what. If you're exploring inactive user feedback from different angles (say, pricing vs. UX pain points), you won't step on each other's toes.
Sender context in chats: With multi-user analysis, each chat message shows the sender's avatar. It's clear who asked which follow-up or requested a new data slice—hugely helpful for keeping track when collaborating.
Targeted insights: Because of advanced filtering, your team can analyze specific subgroups—like those who switched for price (41% globally) or for better product quality (26% globally) [1]. Want specialists on customer service? Remember, 56% of customers globally cite poor service as a reason for leaving [2]. This kind of targeted teamwork surfaces deeper insights.
Ready to create your own survey and see these collaborative features in action? Try the AI survey creator or launch a fresh NPS variant using the prebuilt NPS builder for inactive users.
Create your inactive users survey about competitor switching reasons now
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