This article will give you tips on how to analyze responses/data from API Developers survey about Developer Onboarding Experience. If you want to turn survey results into real insights, you'll need a process that works for both numbers and rich, open-ended feedback.
Choosing the right tools for analyzing your API Developers survey data
Your analysis approach depends on what kind of data you’ve collected. Here’s what I look for:
Quantitative data: For questions with defined choices or ratings, like satisfaction or NPS, it’s simple—just count responses. I usually plug this data into Excel or Google Sheets to crunch the numbers and chart trends.
Qualitative data: For open-ended or follow-up responses, things get tricky. Manually reading 50+ developer comments? No thanks. To see patterns and get to insights, I use AI tools—nothing humanly scalable beats them for parsing dense blocks of feedback.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can always copy your survey export into ChatGPT (or other general-purpose GPT tools) and start a conversation about the results. This is a low-barrier entry if you’re just starting or your dataset is small.
But: This workflow isn’t ideal for repeated use. Exports need cleaning, pasting long survey data gets messy, and you’ll waste time prepping context for every prompt. Plus, you’ll quickly hit context window limits if your survey is even modestly sized.
All-in-one tool like Specific
If you want a tool built for this, I’d point to Specific. It lets you both collect data with conversational surveys and analyze results instantly with AI—all in one place.
Quality goes up: Surveys on Specific can automatically ask follow-up questions, dynamically probing to get to the “why”—key for onboarding research. That means what you’ll analyze is much deeper than in a static form. See how this works in detail in the guide to automatic AI followup questions.
AI-powered analysis: Once you have your results, Specific instantly summarizes open-ended answers. It extracts themes, clusters pain points, captures “aha!” quotes, and finds patterns—without any manual tagging. You can chat with the AI about the data, just like with ChatGPT, but with survey context built in and tools for filtering and segmenting responses.
If you want hands-on exploration, Specific also supports multiple analysis chats and allows you to manage which data/context gets sent to the AI. This is perfect for diving into different onboarding trends, bottlenecks, or developer cohorts. Find out more in the AI survey response analysis guide.
Useful prompts that you can use for API Developers survey response analysis
Great results depend on asking the AI smart questions. Here’s a prompt I use for almost any open-ended developer onboarding survey:
Prompt for core ideas: This gets you an instant summary of key topics developers mention (great for first-pass analysis!).
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
Want to make the summary even better? AI works best with more context. Briefly describe the background of your survey, goals, product, or team:
Here's the context: This survey was given to API Developers working in companies of 50+ engineers. The goal is to understand where onboarding breaks down and which resources accelerate productivity for new hires.
Prompt for drilling deeper: Once you see an interesting core idea, follow up with: "Tell me more about XYZ (core idea)" to explore all related feedback.
Prompt for specific topic: Use: "Did anyone talk about documentation quality?" For direct checks—add "Include quotes" to get verbatim feedback, which is great for sharing with your product or docs team.
Prompt for pain points and challenges: "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: "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."
Prompt for suggestions & ideas: "Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant."
Take a look at this guide for best survey question ideas or try the AI survey generator for API developers onboarding if you want help creating your own survey template.
How Specific analyzes qualitative data based on question type
Open-ended questions (with or without followups): You’ll get a summary showing both the main answers and the deeper reasons surfaced via follow-up questions.
Choices with followups: Each multiple choice option has its own AI-driven summary, so you can immediately see why developers chose “slower onboarding” or “missing documentation,” and what their actual experience was, including quotes from responses.
NPS questions: Promoters, Passives, and Detractors are split out—each group’s follow-up feedback is summarized separately. This is a game-changer for targeting the right action for each cohort.
If you’re using ChatGPT, you can replicate all of this, but it’s more work—you’ll need to filter and paste relevant answers manually to get targeted summaries on each group or answer type. For more on in-depth strategies, check out the how-to article for survey creation.
How to tackle challenges with AI’s context limits in survey analysis
Even powerful tools like GPT have context size limits—they can only process a certain amount of text at once. If your API Developers survey generates dozens or hundreds of onboarding stories, you can easily hit this wall.
There are two ways to stay efficient (Specific handles both out of the box):
Filtering: Before sending to AI, filter for conversations where users replied to relevant questions or selected certain answers. For example, only analyze developers who mentioned “API authentication headaches.”
Cropping: Select which questions should be included in the AI’s context. Got ten onboarding questions but only care about the open-ended “biggest challenge” item? Just crop to that—saves space and increases insight density.
This is also perfect for running parallel explorations: run the core prompt on all onboarding pain points, while separately analyzing only feedback about documentation or company size.
Collaborative features for analyzing API Developers survey responses
Collaboration can get messy fast when multiple people try to analyze and interpret survey results across product, onboarding, and developer relations teams. Tracking who found what—and how you reached your conclusions—often gets lost in endless spreadsheets or comment threads.
In Specific, you chat with AI on live data, so everyone can spin up their own inquiry: “Someone from onboarding wants pain points, developer relations wants docs feedback.” Each team member can create a separate analysis chat thread, apply their own filters, and instantly see who asked each question and who’s contributed which insights.
You always know who said what, because every message includes the sender’s avatar and metadata. That means when someone on the docs team, product, or engineering joins the conversation, you see their questions and findings in context. It’s all tracked, always up-to-date, and encourages transparent, collaborative insight hunting.
No more lost context: When someone hits on a breakthrough—like a recurring onboarding hurdle for new API consumers—it’s easy to share or export the summary with the right stakeholders. Everyone benefits, and discovering new patterns becomes a team effort. For a hands-on look, check the explanation of AI-powered survey analysis features.
Create your API Developers survey about Developer Onboarding Experience now
Unlock actionable onboarding insights in minutes with dynamic AI analysis, powerful collaboration, and automated followups—get the feedback your developer team actually needs. Create your API Developers survey about Developer Onboarding Experience and start leveling up your onboarding process today.