This article will give you tips on how to analyze responses from an Inactive Users survey about Reactivation Incentives. Let’s dive right into smart ways to turn real responses into actionable insights with AI and the right analysis tools.
Choosing the right tools for analyzing your survey responses
The analysis process—and the tools I reach for—completely depends on whether your data is structured or open-ended. Here’s how I break it down:
Quantitative data: If we're dealing with counts, such as how many Inactive Users clicked a specific Reactivation Incentive, tools like Excel or Google Sheets are perfect. Just export and tally results: percentages, rankings, simple graphs—done in minutes.
Qualitative data: But when I get open-ended responses—like “what would encourage you to return?”—bulk reading isn’t practical. With dozens or hundreds chiming in, scanning for key themes or nuanced feedback is impossible without AI.
For qualitative survey analysis, there are two main approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
You can export your survey data and paste it straight into ChatGPT or another GPT tool. Now, you can talk to the AI about your responses: ask for key themes, summaries, or siphon ideas for new Reactivation Incentives.
The catch? This workflow gets clunky—fast. Large volumes of text often hit context size limits. You’ll find yourself splitting data into chunks, losing track of the survey’s structure, or manually tracking which answer belongs to which user. It works for quick, small projects, but isn’t scalable for a whole survey.
All-in-one tool like Specific
Specific is designed for survey feedback analysis, end-to-end. Here’s how it stands out:
Data collection and analysis are in one place: You build your conversational survey, and the AI automatically asks smart follow-up questions. This means every Reactivation Incentive idea gets a deeper, contextual response. More context = better insights.
AI-powered response analysis: After collecting your Inactive Users’ feedback, Specific instantly summarizes survey responses, spotlights main themes, and distills everything into bite-size findings. No spreadsheets, and you skip the manual heavy lifting.
Conversational interface for analysis: Want more detail? You chat directly with Specific’s AI about the results, just like using ChatGPT—but with built-in support for segmenting responses and applying filters so the AI gets the right context.
Easy to manage: You can fine-tune which parts of the survey go to AI, or combine responses across different groups—features that keep the analysis focused and inside context windows.
If you want to see what a tailored survey for this exact scenario looks like, check this preset AI survey generator for Inactive Users and Reactivation Incentives or learn more about best practices from this deep dive on survey questions.
If you start with an open-ended question about what would bring users back, AI will point out things like, “25 people mentioned more personalized incentives,” “40 responses named bigger discounts,” or “5 users requested dynamic rewards.” And those trends matter: data shows that tailored bonuses can increase deposit frequency by 25%, and dynamic rewards can bump retention by 40%—all translating into stronger ROI for your reactivation efforts. [1]
Useful prompts that you can use for analyzing Inactive Users Reactivation Incentives data
Getting actionable insights starts with asking your AI the right questions. Whether you’re working in ChatGPT, Specific, or anything GPT-powered, these prompts help you structure analysis—and mentor the AI to do the heavy lifting for you.
Prompt for core ideas: Perfect for surfacing key Reactivation Incentive themes from large batches of open-ended answers. This is the core prompt that Specific uses by default, but you can copy it anywhere. Paste your batch of responses and 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
Always provide context. Give AI more background for sharper answers—for instance, explain your survey’s goal (“surveying 200 Inactive Users to pinpoint which Reactivation Incentives will win them back for Q2 push”):
I conducted a survey with 200 inactive users to understand which reactivation incentives would motivate them most to return to our platform. Please summarize core ideas and trends in the feedback.
Dive deeper into core ideas. Once the AI has surfaced “Personalized incentives” or “Bigger discounts,” ask for detail: “Tell me more about why users want personalized incentives.” This keeps your exploration focused and efficient.
Validate topics quickly: “Did anyone talk about loyalty points or gamification?” Add “Include quotes” if you want direct feedback highlights. This is straightforward and helps you support arguments with real user voices.
Other prompts for digging into survey motivations:
Personas prompt: Group similar respondents—Are some price-motivated? Others loyal but waiting for new features? Run:
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 prompt: Especially useful if users say why they left or what incentives didn’t work before. Try:
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 prompt: Unpack what’s really nudging users. For example:
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.
For more prompt inspiration, or to generate a survey with AI from scratch, take a look at the AI survey generator or the step-by-step guide to building your own survey for Inactive Users.
How Specific analyzes feedback based on question types
Open-ended questions with or without follow-ups: Specific collects responses, including every AI-generated follow-up. The platform then gives you an instant summary—not just of initial answers, but of everything users revealed during the deeper conversation. The full picture, not a flat summary.
Choices with follow-ups: For surveys that offer selections (“What type of incentive would you value most?”) and prompt for more detail on each choice, Specific creates a summary for each possible answer. This way, you see not just what was picked, but why—because insights always hide in the “why.”
NPS (Net Promoter Score): If you include an NPS question, each group—detractors, passives, promoters—gets its own summary based on their open-ended follow-up responses. You see exactly what’s driving dissatisfaction versus loyalty.
You can replicate this structure in ChatGPT, but it requires more manual setup—splitting responses, filtering by answer type, and running each batch through your prompts.
If you want to know how AI automatic follow-up questions work, here’s a breakdown of the feature and its impact on qualitative depth.
Staying within AI context limits for large survey data sets
AI platforms aren’t limitless; context windows can’t always handle a giant block of survey responses at once. Here’s how I get around it (and how Specific handles it natively):
Filtering: Filter conversations by user reply, question, or answer before running your analysis. For instance, analyze only users who picked “Bonus Cash” as a Reactivation Incentive. This keeps each analysis session focused, actionable, and within the context size.
Cropping: You can choose to send only selected questions—or subsets of survey data—to the AI for summary. Skip what’s irrelevant and analyze 1000s of conversations in manageable chunks.
These two approaches work equally well with Specific or manually in ChatGPT, but Specific helps you orchestrate this by default so you never lose track or overload your AI prompt window.
Collaborative features for analyzing Inactive Users survey responses
Collaboration is often the bottleneck in survey analysis. Teams want to slice and dice data, share findings, and avoid siloed workstreams—especially important when dissecting Reactivation Incentives across roles or departments.
In Specific, survey data analysis is as collaborative as it gets. Instead of passing spreadsheets around, you just chat with the AI. Each team member can spin up their own analysis chat. Each chat can have unique filters (“Only show responses from users who unsubscribed after a price change”), so teams dig deeper together.
Every chat is transparent. You can instantly see who started a chat, and how each angle is evolving—reassuring when Product, Marketing, and Support want different insights.
Everyone gets credit for their work: Whenever someone sends a message, you see their avatar, so clear documentation (and fewer accidental overwrites).
Get inspired by seeing survey templates and examples of analysis in action: survey templates and real survey examples.
Create your Inactive Users survey about Reactivation Incentives now
Start capturing deeper insights, surface the Reactivation Incentives that move your users, and turn feedback into ROI—instantly, conversationally, and with analysis handled by AI in minutes. Create your survey and make your next reactivation campaign radically more effective today.