This article will give you tips on how to analyze responses from a citizen survey about mobile app experience. If you want actionable insights, it's all about using the right approach for your audience, the survey type, and the data you collect.
Choosing the right tools for analyzing survey response data
The best approach—and the right tools—depend on the kind of data your citizen survey about mobile app experience collects.
Quantitative data: If your survey includes questions like, "How satisfied are you?", you can easily crunch the numbers in Excel or Google Sheets. Think of this as simple counting: how many citizens picked each option, or what’s the average score for mobile app features.
Qualitative data: Open-ended responses are far richer, but they’re a nightmare to process manually. Reading hundreds of citizen comments is not practical—instead, you need AI-powered tools that can summarize, categorize, and distill deeper insights automatically.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual copy-paste process: You can export your survey data as a spreadsheet and paste the qualitative responses into ChatGPT or a similar AI chatbot. From there, you can start chatting about results—asking for summaries, sentiment analysis, or suggestions based on the data.
Limited convenience: While this works, it's a bit clunky. You need to reformat data, watch for context window limits, and you don’t get features tailored for survey analysis. Still, it’s a low-barrier entry if you want to experiment with small datasets.
All-in-one tool like Specific
Purpose-built workflow: Specific is made for this. You can create conversational surveys that automatically ask the right follow-up questions, increasing the quality of your mobile app experience data. The platform combines collection and AI-powered analysis in one tool.
Instant AI-driven insights: AI in Specific summarizes responses in real-time, identifies the most common themes, and gives you actionable takeaways—without sifting through spreadsheets.
Conversational analysis: You can chat directly with the AI about your survey results (just like ChatGPT), but with features built for survey data—for example, managing which questions, responses, or segments you’re analyzing.
It's no surprise that 42.1% of mobile app creators already use AI tools for feedback analysis and prioritization[1]—the right tool can move you from data chaos to actionable clarity.
Useful prompts that you can use to analyze citizen survey responses about mobile app experience
If you're using AI tools like ChatGPT or Specific, prompts are your friends. The right prompt turns messy text into insights. Here are some of the most effective prompts for this kind of survey:
Prompt for core ideas: This is a go-to for summarizing large numbers of citizen responses about their mobile app experience—great for tracking recurring feedback, pain points, and must-fix issues.
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
Tip: Give context to AI for even better results. Always describe your survey, audience, and goal. For example:
"You’re analyzing responses from a citizen survey about mobile app experience. My main goal is to uncover top issues citizens face with our mobile app, including what makes the experience positive or frustrating."
Dive deeper into a theme: Once you have core ideas, ask follow-ups:
"Tell me more about XYZ (core idea)."
Prompt for specific topics: To check if citizens mentioned a specific feature or issue, ask:
"Did anyone talk about mobile notifications? Include quotes."
Prompt for personas: Build empathy with these insights:
"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: Uncover frustrations and recurring issues:
"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: Map out what citizens feel overall—and why:
"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."
There are lots of angles you can explore—see this guide to citizen survey questions about mobile app experience for inspiration on what to ask in your survey in the first place.
How Specific analyzes qualitative data from different question types
Specific's AI survey response analysis is tailored for the type of question in your survey:
Open-ended questions (with or without follow-ups): You’ll get a summary of all citizen responses, plus summaries for each related follow-up—helping you understand not just the "what", but the "why".
Multiple-choice with follow-ups: Each option is treated as a separate track. AI summarizes the follow-ups for citizens who selected, say, "I use the app daily", so you see specific experiences tied to usage patterns.
NPS (Net Promoter Score): You don't just get a single score—Specific generates separate summaries for detractors, passives, and promoters, each based only on the responses from those segments. That way, you see what really delights or frustrates each type of citizen in your app.
You can do this with ChatGPT too, but it's more manual work: you have to filter and reformat your data for each segment and paste it in piece by piece. With Specific, it's automatic.
Find more details on how this works in practice in our deep dive on AI-powered survey analysis.
Dealing with AI context size limits
AI chatbots can process only a limited amount of data at once—if your citizen survey about mobile app experience collects a flood of insightful responses, this can become a real bottleneck.
To work around this, there are two tactics:
Filtering: Filter your survey data so the AI looks only at relevant conversations. For example, focus on citizens who mentioned a specific issue or answered a critical question.
Cropping: Crop the dataset, sending only the most relevant questions or parts of conversations to the AI—helping you analyze more conversations in fewer passes.
These capabilities are built into Specific, so you can efficiently analyze even extensive feedback without running into system limits. If you're curious about how these features work, see an explanation in the AI survey response analysis feature overview.
Collaborative features for analyzing citizen survey responses
Anyone who’s worked on a citizen survey about mobile app experience knows that collaboration gets messy fast—teams argue over data, lose track of insights, or duplicate analysis.
Chat with AI as a team: In Specific, anyone on your team can analyze the survey results by simply chatting with the AI. This conversational workflow means no more long meetings to agree on segments—just start a new chat about any angle you want to explore.
Multiple chats, each with context: You can spin up multiple chats, each focused on a specific slice of data (say, citizens who gave negative feedback on mobile notifications). Each chat shows who started it, so it's easy to track ownership and follow different threads.
Real-time team collaboration: Chats show who said what as you collaborate. When colleagues join, their avatar appears by their messages, making it super clear who’s driving the analysis. This is much more structured and transparent than sharing files back and forth.
These collaborative AI features help citizens’ voices turn into action items, not just data points. See how the process works—from design to analysis—in the full citizen survey how-to guide.
Create your citizen survey about mobile app experience now
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