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You’ve been collecting feedback. Google Forms, Typeform, NPS surveys, feature request spreadsheets—it’s all there. But it’s messy. Hundreds of rows. No clear patterns. You scroll through trying to find themes, but it takes forever and you’re never confident you found everything. This guide shows you how to import your feedback spreadsheet into BuildBetter and turn it into a prioritized roadmap in 1 hour.

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The Spreadsheet Analysis Problem

Your current reality:
  • 📊 200+ rows of feedback in Google Sheets
  • 😵 Can’t remember what row 47 said vs row 143
  • 🔍 Ctrl+F only finds exact matches (misses similar ideas)
  • 🤷 No idea which problems are most common
  • ⏰ Manual analysis takes 8+ hours
  • 📝 Results in gut-feel prioritization anyway
The question: Is there actually a pattern, or are you just seeing what you want to see?

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What You’ll Achieve

In 1 hour with BuildBetter:
  • ✅ Import 100-1000 feedback items from CSV/spreadsheet
  • ✅ Auto-extract problems, requests, praise from text
  • ✅ Find the top 5-10 themes automatically
  • ✅ Visualize patterns you couldn’t see in spreadsheet
  • ✅ Prioritize roadmap based on data, not hunches
  • ✅ Export insights ready to share with team
Time saved: Manual analysis: 8+ hours. With BuildBetter: 1 hour. Accuracy: Spreadsheet misses 40-60% of patterns. BuildBetter catches 95%+.

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Prerequisites

BuildBetter account (buildbetter.ai)
Feedback spreadsheet (Google Sheets, CSV, Excel, Typeform export, etc.)
At least 50+ feedback items (more is better)

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Step 1: Prepare Your Data (15 minutes)

BuildBetter needs your feedback in the right format. Don’t worry—it’s flexible.
1

Export to CSV

From Google Sheets:
  1. File → Download → CSV (.csv)
From Typeform:
  1. Results → Export → Export to CSV
From SurveyMonkey, NPS tools, etc:
  1. Export responses as CSV
[screenshot: Google Sheets export menu showing CSV option]Save the file somewhere you can find it.
2

Check Your Columns

Open the CSV and make sure you have these columns (names can vary):Required:
  • Feedback text (the actual comment/response)
    • Could be named: “Response”, “Comment”, “Feedback”, “Message”, etc.
Helpful (but optional):
  • Customer/User name or email
  • Date submitted
  • Company name (if B2B)
  • User type (customer, prospect, internal, etc.)
  • Rating (if NPS/CSAT survey)
[screenshot: Example CSV with columns: Date, Email, Company, Feedback, NPS Score]
Don’t have all these? That’s fine. BuildBetter only needs the feedback text. Everything else just adds context.
3

Clean Up (Optional)

Quick fixes to improve results:Remove completely empty rows:
  • Delete rows with no feedback text
Merge multi-line responses:
  • If a response spans multiple rows, combine into one cell
That’s it. Don’t obsess over perfection. BuildBetter handles messy data well.

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Step 2: Import to BuildBetter (10 minutes)

1

Access Text Data Import

  1. In BuildBetter, click Upload (top right)
  2. Select Import Text Data or CSV Upload
[screenshot: Upload dropdown showing “Import Text Data” option]
2

Upload Your CSV

  1. Drag and drop your CSV file
  2. Or click to browse and select
[screenshot: CSV upload interface with drag-and-drop zone]BuildBetter starts processing immediately.
3

Map Your Columns

BuildBetter shows you a preview and asks you to map columns:[screenshot: Column mapping interface showing CSV columns on left, BuildBetter fields on right]Map these if you have them:
  • Content/Message → Your feedback text column
  • Author/User → Name or email column
  • Date → Submission date column
  • Company → Company name column
  • Metadata → Any other useful columns (NPS score, user segment, etc.)
Only the Content/Message field is required. Everything else is optional metadata.
4

Processing

Click Import and BuildBetter:
  • Processes each row of feedback
  • Extracts signals (problems, requests, praise, questions)
  • Identifies sentiment
  • Categorizes by theme
  • Makes everything searchable
[screenshot: Processing screen showing “Importing 243 feedback items…”]Time: 5-15 minutes for 100-500 items. You can navigate away—it continues in background.

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Step 3: Explore Your Patterns (20 minutes)

Now the magic happens. Let’s find what’s actually in your data.
1

View All Signals

  1. Go to Signals in left navigation
  2. You’ll see signals extracted from every feedback item
[screenshot: Signals page showing list of extracted feedback with types and sentiment]Each signal shows:
  • Summary (what the feedback is about)
  • Type (Problem, Feature Request, Praise, Question)
  • Sentiment (positive, neutral, negative)
  • Source (which row/person it came from)
2

Find Top Problems

Let’s find what customers complain about most:
  1. Click Filter
  2. Select Signal Type → Problem or Complaint
  3. Click Apply
[screenshot: Filtered signals showing only problems]Scroll through and you’ll start seeing patterns:
  • Same issue mentioned with different words
  • Varying levels of frustration
  • Specific vs vague complaints
Look for problems mentioned 3+ times. If different people describe the same issue independently, it’s real.
3

Ask Chat to Find Themes

Use AI to analyze patterns you might miss:Query: “What are the top 5 most common problems in this feedback? Group similar issues together.”[screenshot: Chat analyzing feedback and listing top 5 problem themes]Example output:
Top 5 Problems from 243 Feedback Items:

1. Onboarding Confusion (47 mentions)
   - "Don't know where to start"
   - "Setup too complicated"
   - "Needs better tutorial"

2. Mobile App Issues (34 mentions)
   - "Doesn't work on iPhone"
   - "Mobile version missing features"
   - "Can't access on phone"

3. Slow Performance (28 mentions)
   - "Takes forever to load"
   - "Dashboard is laggy"
   - "Timeout errors"

4. Export Limitations (19 mentions)
   - "Can't export to Excel"
   - "Need PDF export"
   - "Export missing data"

5. Integration Requests (17 mentions)
   - "Need Salesforce integration"
   - "Zapier connection wanted"
   - "API access needed"
This is gold. You just found your roadmap priorities.
4

Segment by Customer Type

If you imported customer metadata, segment the feedback:Filter by company size:
  • Enterprise customers want different things than SMBs
Filter by user role:
  • Admins care about different features than end users
Filter by NPS score:
  • Detractors (0-6) vs Promoters (9-10) have different feedback
[screenshot: Signals filtered by NPS score showing detractor feedback]Ask Chat: “What do detractors (NPS 0-6) complain about most?”

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Step 4: Visualize & Prioritize (15 minutes)

Turn insights into dashboards your team can understand.
1

Create Feedback Dashboard

  1. Go to Clustering
  2. Click Customize Dashboard
Add these visualizations:Chart 1: Top Problems (Bar Chart)
  • Data: Signals (type = Problem)
  • Group by: Theme/Topic
  • Sort by: Count (descending)
[screenshot: Bar chart showing top 5 problems with counts]Chart 2: Feature Requests (Bar Chart)
  • Data: Signals (type = Feature Request)
  • Group by: Theme
  • Sort by: Count
[screenshot: Bar chart showing most-requested features]Chart 3: Sentiment Distribution (Pie Chart)
  • Data: All signals
  • Group by: Sentiment
  • Shows: % Positive, Neutral, Negative
[screenshot: Pie chart showing sentiment breakdown]Chart 4: Feedback Over Time (Line Chart)
  • Data: Signals
  • X-axis: Date submitted
  • Y-axis: Count
  • Shows: When feedback peaked
[screenshot: Line chart showing feedback volume over time]
2

Create Prioritization Matrix

Ask Chat to help prioritize:Query: “Based on this feedback, create a prioritization matrix. For each major problem/request, tell me: 1) How many people mentioned it, 2) How frustrated they are (sentiment), 3) Your recommended priority (high/medium/low).”[screenshot: Chat generating prioritization matrix]Example output:
Roadmap Prioritization Matrix:

🔴 HIGH PRIORITY
1. Onboarding Confusion
   - Mentions: 47
   - Avg Sentiment: -6.2 (frustrated)
   - Why: Affects all new users, blocks activation

2. Mobile App Issues
   - Mentions: 34
   - Avg Sentiment: -5.8 (very frustrated)
   - Why: Growing mobile usage, competitive gap

🟡 MEDIUM PRIORITY
3. Slow Performance
   - Mentions: 28
   - Avg Sentiment: -4.1 (annoyed)
   - Why: Impacts daily usage, but workarounds exist

🟢 LOW PRIORITY (but track)
4. Export Limitations
   - Mentions: 19
   - Avg Sentiment: -2.3 (mild annoyance)
   - Why: Edge case, only power users

5. Integration Requests
   - Mentions: 17
   - Avg Sentiment: +1.2 (wishlist, not blocker)
   - Why: Nice-to-have, not deal-breaker
3

Generate Product Brief

Turn insights into shareable document:
  1. In Chat, click Generate button
  2. Select Generate Document
  3. Choose template: “Product Requirements” or “Research Summary”
[screenshot: Document generation dialog]BuildBetter creates a formatted document with:
  • Executive summary
  • Top problems/requests
  • Customer quotes
  • Prioritization recommendations
  • Next steps
[screenshot: Generated PRD document]Share with your team, stakeholders, or use in roadmap planning.

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Step 5: Close the Loop (10 minutes)

Let customers know you heard them.
1

Find Quick Wins

Query Chat: “Which problems could we fix quickly (low effort) that would make customers happy?”[screenshot: Chat identifying quick win opportunities]Look for:
  • High frustration, low effort fixes
  • Small UI tweaks mentioned repeatedly
  • Documentation gaps
2

Communicate Back

For high-priority items:If you’re building it:
  • Email customers who requested it: “We heard you, this is coming in Q2”
  • Include them in beta testing
If you’re not building it:
  • Explain why (helps set expectations)
  • Suggest alternatives
Use BuildBetter’s Export feature to get a list of customers who mentioned specific features. Easy outreach.
3

Track Over Time

As you ship fixes:
  1. Import new feedback monthly
  2. Check if complaints are decreasing
  3. See if sentiment is improving
  4. Validate your roadmap decisions
[screenshot: Trend chart showing problem mentions declining after fix]Data-driven validation: Did fixing onboarding actually reduce complaints? Now you know.

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Real Example: Emma’s Roadmap Transformation

Background: Emma is PM at a B2B SaaS tool. Had 387 feedback items in a Google Sheet from 6 months of NPS surveys. Never analyzed them properly. Before BuildBetter:
  • Skimmed ~50 responses
  • Guessed top problems
  • Built features based on loudest customer (who happened to email her)
  • 2/5 features flopped (low adoption)
Week 1 with BuildBetter: Monday (1 hour):
  • Exported NPS feedback to CSV
  • Uploaded to BuildBetter
  • Let it process during lunch
Monday afternoon:
  • Reviewed signals: 387 responses → 842 signals extracted
  • Top problem: “Can’t collaborate with team” (89 mentions)
  • Emma was surprised—she thought it was “slow performance” (only 23 mentions)
Tuesday:
  • Built dashboard showing all themes
  • Asked Chat for prioritization
  • Generated product brief
Wednesday:
  • Presented to team: “89 customers mentioned team collaboration issues, here are exact quotes”
  • Team pivoted roadmap priority
  • Started building collaboration features
3 months later:
  • Shipped team collaboration
  • NPS went from 32 → 48
  • 67% of detractors mentioned it as reason they upgraded score
  • Next feature adoption: 78% (vs usual 35%)
Emma’s quote: “I wasted 6 months building what I THOUGHT customers wanted. One hour with BuildBetter showed me what they ACTUALLY wanted. Completely changed how I prioritize.”

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Common Questions

BuildBetter handles messy data well. The AI:
  • Corrects obvious typos
  • Understands incomplete sentences
  • Ignores gibberish
  • Focuses on extracting meaningful signals
Don’t waste time cleaning. Just import and let the AI handle it.
Not yet (integrations coming). For now:
  1. Export from those tools to CSV
  2. Upload CSV to BuildBetter
Takes 2 extra minutes. Still way faster than manual analysis.
Import them all! BuildBetter can handle multiple CSVs:
  1. Upload NPS feedback CSV
  2. Tag it “NPS Feedback”
  3. Upload support feedback CSV
  4. Tag it “Support Tickets”
  5. Upload form responses CSV
  6. Tag it “Feature Requests”
Then filter/segment by source or analyze across all sources.[screenshot: Signals filtered by source/tag]
BuildBetter supports 30+ languages:
  • Auto-detects language per feedback item
  • Translates to English for analysis (or keeps original)
  • Signals work across languages
Just upload. It works.
Short feedback is harder to analyze but still works:
  • “Slow” → Extracted as Problem signal
  • “Love it!” → Extracted as Praise signal
  • “Need API” → Extracted as Feature Request signal
Better results with longer feedback, but BuildBetter does its best with what you have.

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Your 1-Hour Transformation Checklist

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Minute 0-15: Prepare

Export feedback to CSV
Check columns are mapped correctly
Quick cleanup (remove empty rows)

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Minute 15-25: Import

Upload CSV to BuildBetter
Map columns
Start processing

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Minute 25-45: Explore

Review signals (all types)
Filter for top problems
Ask Chat for top themes
Segment by customer type (if metadata exists)

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Minute 45-60: Visualize & Share

Build dashboard (4 key charts)
Create prioritization matrix
Generate product brief

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What’s Next?

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After Your First Import

Ongoing Feedback Analysis

Set up monthly imports to track trends over time

Combine with Calls

Add user interview recordings for deeper context

User Research at Scale

Analyze 100s of conversations + surveys together

Roadmap Automation

Auto-generate PRDs from feedback patterns

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Make It a Habit

Monthly (1 hour):
  • Export latest feedback
  • Import to BuildBetter
  • Review new themes
  • Update prioritization
Quarterly (Half day):
  • Analyze 3 months of feedback
  • Trend analysis (what’s improving/worsening?)
  • Roadmap alignment check
  • Team presentation
The magic isn’t in doing this once—it’s in making it a monthly habit. Track how feedback changes as you ship features. That’s how you know you’re building the right things.

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Resources

CSV Import Guide

Detailed CSV formatting and import docs

Template: Feedback CSV

Download example CSV format

Video: CSV to Roadmap

Watch this workflow in action (8 min)

Book: Strategy Call

Get help with your first import (free)
Your feedback spreadsheet isn’t useless. It’s a goldmine. You just needed the right tool to mine it.
The best product decisions are backed by data, not hunches. Now you have the data.
Average product team has 3-6 months of feedback sitting in spreadsheets, never properly analyzed. You just analyzed all of it in 1 hour. That’s a competitive advantage.