If you’ve ever wondered how to analyze open-ended survey responses excel style—especially after an NPS survey—you’re not alone. Open-ended NPS follow-ups are packed with juicy context, but they get messy fast when responses pile up.
Picking the right NPS follow-up questions makes it much easier to structure, code, and find actionable insights in Excel or your favorite spreadsheet. I want you to get the most from your feedback without drowning in manual recoding.
Best NPS follow-up questions that code well in Excel
Not all follow-ups are equally friendly to Excel analysis. The secret? Ask questions that are focused, clear, and prompt respondents to be specific. That way, when you export them, you'll thank yourself—responses naturally cluster into themes you can summarize, count, and act on (instead of wrestling with vague rambles).
Structured prompts (best for codeable data)
Specific, action-oriented requests (makes it easier to tag and bucket in Excel)
Single-focus questions (avoid the “anything else?” trap that yields unbound responses)
Let’s break down winning open-ended prompts for each NPS group, so you can fill your spreadsheet with useful, cleanly-codable insights.
Promoters (Score 9-10)
“What do you love most about our product/service?” – Surfaces key strengths; positive answers neatly group by feature or experience. [2]
“What did you enjoy most about your experience with us?” – Pinpoints the top delights, which you can categorize by value delivered. [7]
“Would you be willing to provide a testimonial or review?” – Quickly signals which fans you can follow up with, which segments easily in a spreadsheet. [10]
These questions prompt responses that usually cite a specific feature, benefit, or interaction—a breeze to categorize in Excel.
Passives (Score 7-8)
“What could we do to improve your experience?” – Explores concrete suggestions, so you can bucket recurring asks or fixable issues. [3]
“What’s one thing we could change to make your experience better?” – Direct, focused; forces one main pain point, making categorization sharper. [8]
“What feature or service would make you more likely to recommend us?” – Maps to product roadmap items; easy to sort by request or priority. [6]
Passive responses home in on improvement opportunities instead of generic praise or gripes; Excel tagging gets much simpler.
Detractors (Score 0-6)
“What was missing or disappointing in your experience with us?” – Reveals specific letdowns; responses typically mention one primary theme. [4]
“How can we make things right for you?” – Turns feedback into specific, remediable asks, clear for action in Excel. [5]
“What specific issues led to your rating, and how can we address them?” – Targets root causes and possible fixes, both easy to bucket. [9]
These Detractor follow-ups yield focused, actionable complaints—you won’t spend hours decoding rants.
Question Type | Excel Analysis Difficulty |
|---|---|
Highly Structured ("What did you enjoy most?") | Easy (clear, codeable) |
Open but Focused ("How can we improve?") | Moderate (some variation) |
Unstructured ("Anything you want to add?") | Hard (noisy, fragmented) |
Personally, I recommend dynamic AI-powered follow-ups that adapt to each response and make coding in Excel even smoother—see how these work and why they’re so impactful in automatic AI follow-up questions.
Traditional Excel methods for analyzing open-ended NPS feedback
If you’re manually analyzing NPS follow-ups in Excel, you already get the grind. The classic workflow looks like this:
Read each open-ended response… one by one
Come up with codes or tags (themes, sentiment, problem type)
Paste them into new columns alongside the original answer
Count, filter, or pivot table to summarize “what’s most common”
It’s tedious. For 30 responses, fine—but for 300 or 3,000? Your time is toast. Plus, coding is open to interpretation, so themes aren’t always consistent or comprehensive even after hours of labor. Manual tagging misses hidden insights and subtle context because we get tired or cut corners.
Sentiment analysis limitations: Sure, you can hack together basic sentiment analysis formulas in Excel using keywords (“great," “bad"), but nuance is lost. You’ll misclassify quirky or sarcastic feedback, and context gets ignored in column logic.
Theme extraction challenges: Real insights depend on context—Excel isn’t built to find multiword concepts or to group related ideas without tons of manual entry. If a respondent says, “Pricing was a little high, but support was excellent,” good luck cleanly splitting themes by formula or quick search. You’ll spend forever normalizing and correcting codes.
That’s why so many organizations struggle to scale open-ended feedback analysis, even though these responses deliver gold. Manual Excel processes are error-prone and painfully slow compared to modern approaches.
How AI transforms NPS follow-up analysis while keeping Excel compatibility
With Specific, we use AI to do the heavy lifting—every NPS follow-up is auto-categorized, sentiment scored, and summarized, ready for spreadsheet power moves.
Auto-categorization: Your open-ends are tagged with themes (like “Missing Feature” or “Great Support”) automatically, so you can spot patterns at a glance
Sentiment scoring: Each response gets a sentiment label (positive, neutral, negative), and this data exports seamlessly to CSV or Excel for instant filtering and sorting
Theme pre-extraction: AI identifies and labels the big recurring reasons, so when you drop your CSV into Excel, the pivot tables basically build themselves—no manual recoding round after round
Dive deeper on how AI-powered analysis integrates with your existing tools and makes life easier in AI survey response analysis.
CSV export structure: When you pull your results out of Specific, you’ll get a CSV where every response is neatly structured into columns: respondent ID, raw answer, auto-tagged category, sentiment score, and extracted key themes. No mess, no fuss—drop into Excel and start pivoting right away.
Manual Categorization | AI-Powered Analysis |
|---|---|
Hours spent coding, normalizing, and checking responses | Instant, automated tagging with clear categories |
Subjective and inconsistent themes | Consistent, model-driven categories and sentiment |
Risk of missing trends and outliers | Surface every common theme—including ones you wouldn’t spot yourself |
Basic excel formulas for sentiment, often wrong | Accurate sentiment and theme labeling at scale |
Responses from your NPS survey are neatly pre-tagged, sentiment-scored, and themed—so your analysis in Excel is fast and frustration-free.
Setting up your conversational survey for Excel-friendly analysis
You want open-ended NPS feedback that’s rich but still fits neatly into Excel analysis? Start by designing your survey to probe for specifics while staying structured. Conversational surveys hit the sweet spot—they draw people in (so you get detailed context), but keep follow-ups consistent and codable for easy export.
Great conversational follow-ups get the respondent to explain their answer in actionable terms. The AI can dig deeper when someone gives a vague or high-level answer, nudging them for detail—but always focused on questions like the ones above, which are easy to filter and summarize. With Specific, you combine thoughtful prompts and best-in-class conversational UX to keep respondents engaged and deliver answers you can actually use.
When you’re ready to build, the AI survey generator makes it simple—just describe what you want and get a tailored, codable NPS survey in seconds.
Remember: follow-ups aren’t just “support” for your survey—they make the entire thing a conversation. That’s why this is truly a conversational survey, not just a form asking for reasons.
Try prompts like these to create NPS surveys with Excel-friendly follow-ups:
NPS survey with follow-ups: For each score, ask (1) why they gave that score and (2) a specific, Excel-codable improvement, like “What’s one thing you’d change?” or “What feature would make you recommend us?”
Create an NPS survey where promoters are asked “What do you love most?” and “Anything we should keep doing?”, while detractors are asked “What disappointed you?” and “What could we do to make it right?” Make sure the follow-ups are easy to group in Excel.
Draft an NPS survey that uses dynamic AI follow-ups to clarify any vague answers, and ensures all responses can be auto-tagged for themes and sentiment in a CSV export.
Turn NPS feedback into actionable Excel insights
The right follow-up questions combined with AI analysis unlock structured feedback you can actually use—and fast. Turn your surveys into conversations, then analyze every response in Excel without overwhelm. Ready to get sharper NPS insights? Create your own survey now and see how easy rich feedback analysis can be.