This article will give you tips on how to analyze responses from a Workspace Admins survey about onboarding experience using AI-powered survey response analysis and conversational survey tools.
Choosing the right tools for analyzing survey responses
When it comes to analyzing Workspace Admins survey data about onboarding experience, your approach depends on the type and structure of the responses you’ve collected.
Quantitative data: If you’re dealing with structured responses (e.g., how many admins selected “very satisfied” or “training was comprehensive”), you can quickly tally up results using classic tools like Excel or Google Sheets. These tools are great for turning simple multiple-choice data into easy-to-understand charts.
Qualitative data: For open-ended feedback, like admins sharing detailed onboarding stories or explaining their challenges, things get tougher. Reading each response manually just doesn’t scale—you need AI tools to summarize themes, pain points, and ideas woven throughout the responses.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export your survey responses and paste them into ChatGPT (or another GPT-based tool) for analysis. This method lets you use prompts to extract themes, pain points, or summarize the sentiment right away.
Limitations: Handling the data this way isn’t very convenient. You might run into problems with AI’s context limits if you have a lot of responses, and you’ll need to do a good bit of prep and manual work to keep things organized.
On the plus side, ChatGPT is both accessible and powerful for running one-off analyses—especially for short surveys or quick qualitative checks. In fact, ChatGPT is now widely used for analyzing qualitative survey data, letting you perform thematic analysis, sentiment detection, and more with just a few natural language prompts. [1]
All-in-one tool like Specific
Purpose-built for survey work: Specific is designed for just this sort of challenge. You can collect survey data, including real-time follow-ups that dig deeper (thanks to automated probing questions), then instantly analyze everything with AI—all in one place.
AI-powered analysis at your fingertips: Specific automatically summarizes open-ended and follow-up responses, highlights main themes, and surfaces insights without any manual coding or spreadsheet chaos. This AI survey response analysis is quick, actionable, and interactive: you can chat about your data just like with ChatGPT, but with extra survey-specific context. See more at how it works.
Fine-tuned for survey data: You get filters, chat threads for each analysis angle, and extra features to manage how your data is sent to AI—making analysis of complex onboarding experiences a breeze compared to generic AI tools.
If you want to start from scratch, Specific provides an AI survey builder you can use for any audience or topic.
If your focus is specifically Workspace Admins and onboarding, use the Workspace Admins onboarding survey generator for a tailored approach.
For those comparing broader options, AI-powered tools like NVivo, MAXQDA, and others can also auto-code and visualize themes in large datasets, so you’re not locked in to just spreadsheets. [1]
Useful prompts that you can use for analyzing Workspace Admins onboarding experience survey responses
One of the biggest wins with AI survey response analysis is being able to use prompts to dig out insights from your Workspace Admins onboarding survey. Here are a few powerful ones:
Prompt for core ideas: Use this to generate a high-level summary of key themes or topics mentioned in your open-ended responses. Here’s a prompt I recommend (adapted from Specific’s default):
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: AI always performs better if you give it more context. Briefly describe your survey’s goal, target audience, time, or particular concerns before sending your data for analysis. For example:
Analyze these responses from a Workspace Admins onboarding experience survey. Focus on identifying repeat pain points in the first 3 months, what admins found most difficult, and any surprising comments about our documentation. The survey was run in Q1 2024 with admins from companies over 100 employees.
Prompt for detailed exploration: After surfacing a core idea, simply prompt: "Tell me more about XYZ (core idea)" to explore deeper insights or segment results by subtheme.
Prompt for specific topics: Want to check if anyone mentioned a particular onboarding technology or process? Just ask:
Did anyone talk about XYZ? Include quotes.
Prompt for personas: If you want to segment opinions based on different roles or backgrounds:
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:
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 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.
If you're designing your survey, you'll get richer data (and more insightful AI analysis) if you take inspiration from these best questions for Workspace Admins onboarding experience during survey design.
How Specific analyzes qualitative data from different question types
Specific automatically adapts how it summarizes different types of questions, making it easy to connect insights across the onboarding experience:
Open-ended questions (with or without follow-ups): You get an instant summary for all responses—plus a breakdown of what admins said in their detailed follow-up exchanges.
Multiple choice with follow-ups: Each option gets its own summary. If, for example, someone chooses “onboarding process unclear” and then explains more, you see a concise report of every explanation tied to that specific choice.
NPS questions: Specific automatically groups follow-up responses by promoters, passives, or detractors—summarizing what each group actually said about onboarding strengths and gaps.
You could manually replicate this method in ChatGPT, but it’s a lot more laborious—filtering, grouping, and keeping track of categories quickly becomes unwieldy without a purpose-built workflow.
Want more hands-on tips? Check out our guide to creating and analyzing Workspace Admins onboarding surveys.
How to tackle AI’s context limit when analyzing large survey data sets
AI models (ChatGPT included) have context limits—a maximum number of tokens or words they can process at once. If your Workspace Admins onboarding survey is popular and you get lots of lengthy responses, you might hit this ceiling.
There are two main tactics (offered out of the box in Specific) to keep things manageable:
Filtering: Narrow down the dataset. Select only those admins who answered specific questions or chose particular options—letting the AI analyze just the responses that matter most.
Cropping: Chop down the content sent to the AI by selecting a single question or subset of questions for focused analysis. This ensures more, richer conversations fit within the model’s context window.
Most generic AI tools don’t give you control over what data makes it into the analysis session, so you end up hand-editing spreadsheets or data files. In Specific, it’s just a few clicks.
Collaborative features for analyzing Workspace Admins survey responses
Collaboration is often the pain point for teams working on onboarding experience surveys. Emailing spreadsheets back and forth, conflicting edits, and losing track of which admin said what can really slow things down.
Analyze together—like a real chat thread: With Specific, you simply start a chat with AI about your survey data—no data wrangling. Each topic, question, or angle you want to explore becomes its own chat, which you can name and share with colleagues.
Multiple analysis threads, clear authorship: Everyone on your team can kick off chats with different filters (for example “What did new hires from large companies say about onboarding training?”). It’s always clear who started each discussion—adding transparency and coordination to your collaborative analysis.
See who said what: When more than one person is collaborating inside AI chats, Specific clearly shows avatars with each message, so you can keep tabs on who contributed what—and make joint decisions quickly. No more guessing, fewer misunderstandings, and real teamwork unlocked.
If you want to get started, you might want to try editing your survey collaboratively with AI or use the NPS survey builder for Workspace Admins onboarding surveys for instant drafts and analysis-ready data structure.
Create your Workspace Admins survey about onboarding experience now
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