If you’re running B2B lead forms or onboarding funnels, chances are you’re sitting on a goldmine of user data—and not using half of it. This guide’s for anyone who uses Formsort to collect submissions and actually wants to do something useful with that pile of CSVs. We’ll walk through exporting your data, slicing it up for real insights (not just pretty dashboards), and avoiding the usual traps that waste your time.
If you’re looking for a plug-and-play analytics utopia, look elsewhere. This is about getting real answers from your data—fast, cheap, and honest.
Step 1: Get Your Submission Data Out of Formsort
You can’t analyze what you can’t access. Formsort makes it pretty straightforward to export raw submission data, but there are a few ways to do it—and some are better than others depending on your use case.
Option 1: Manual CSV Export
For most teams, this will be enough.
- Go to your Formsort workspace and select the form you want data from.
- Head to the “Submissions” tab.
- Click “Export submissions.”
- Pick your date range. (Pro tip: Don’t grab everything if you’ve got thousands of rows—start small.)
- Download the CSV.
When to use:
- Quick analysis in Excel or Google Sheets
- Occasional reporting
- Small- to mid-sized datasets (under 50k rows, or your laptop will hate you)
What to watch out for:
- Large exports can time out or fail. If you’re dealing with tens of thousands of submissions, chunk your exports by week or month.
- Formsort exports all fields, including hidden/internal ones. You’ll need to clean up.
Option 2: Automated Exports (Webhooks, Integrations, API)
If you’re constantly pulling data or want it piped somewhere automatically:
- Webhooks: Set up Formsort to POST data to your server or a service like Zapier every time a submission happens.
- Integrations: Native integrations exist for some CRMs/data warehouses, but check what’s available in your plan.
- API: For more control, grab data via Formsort’s API (if you’re comfortable with scripting).
When to use:
- You need real-time or daily data dumps.
- You’re syncing with a BI tool or data warehouse.
- You have someone technical on hand.
What to ignore:
- Don’t bother with automation if you only pull data once in a blue moon. It’s overkill for ad-hoc reports.
Step 2: Clean and Prep Your Data
Think of this step like prepping ingredients before cooking. If you skip it, your “meal” will be a mess.
1. Open Your Export in Excel, Google Sheets, or Your Favorite Tool
If your file is huge (>50k rows), use Google Sheets with caution—it’ll lag. Excel is better, but for really big data, use a database or Python.
2. Review the Columns
- Which fields matter? Ditch internal tracking columns you don’t need (like UUIDs or timestamps you’ll never use).
- Are field names clear? If you’ve renamed fields in your form, make sure you know what’s what. Document as you go, or future-you will be annoyed.
3. Handle Missing and Messy Data
- Look for blanks. Are people skipping key fields? That’s a red flag for form design.
- Normalize values. “Yes”, “yes”, and “Y” aren’t the same to a spreadsheet. Standardize capitalization and formats.
- Date/time formats: Make sure they’re consistent. If you’re doing time-based analysis, convert everything to your working timezone.
4. Remove Test Submissions
- Filter out anything clearly fake (“asdf@asdf.com” or similar).
- If you use test accounts, flag submissions with those emails and delete or archive them.
Pro tip:
Create a “cleaned” copy of your data before you start poking at it. That way, if you mess up, you have the original untouched.
Step 3: Analyze for Real Insights (Not Just Vanity Metrics)
Here’s where most people get sidetracked building dashboards that look nice but don’t tell you anything actionable. Don’t be that person.
1. Start With a Simple Question
What do you actually want to know? Examples:
- Where are leads dropping off in the form?
- Which company sizes or industries convert best?
- Are certain sales reps getting better quality submissions?
- Is there a bottleneck in the onboarding flow?
Write your question down before you start slicing data. Otherwise, you’ll end up with a bunch of random pivot tables and no answers.
2. Use Pivot Tables or Grouping
- Excel/Google Sheets: Use pivot tables to break down submissions by field—e.g., count by company size, industry, or stage completed.
- SQL / Data Studio / BI Tools: If you’re comfortable, import your CSV and write simple queries (e.g.,
SELECT company_size, COUNT(*) FROM ... GROUP BY company_size).
3. Look for Trends, Not Just Totals
- Drop-off points: Calculate the percentage of people who bail at each step of your form. If 80% quit before uploading a document, maybe your instructions are confusing—or you’re asking too much, too soon.
- Field correlations: Do companies of a certain size answer some questions differently? Are some industries skipping optional fields?
- Submission timing: Are more leads coming in on Mondays? Is there seasonality?
4. Ignore the Fluff
- “Average time to complete” is often misleading—some people get distracted and come back hours later. Look for medians or percentiles, not just averages.
- Don’t obsess over micro-conversions unless you have a ton of data. Small sample sizes = unreliable trends.
- If you’re using Formsort’s built-in analytics, treat them as a starting point—not the gospel. They’re fine for high-level “what’s happening,” but you’ll always see more in the raw data.
Step 4: Share and Act on What Matters
Analysis is pointless if it doesn’t drive decisions.
1. Summarize the Real Takeaways
- Keep it short. One slide or a quick Loom video beats a 20-page deck.
- Focus on what’s actionable. “We lose 60% of leads at the document upload step” is gold. “Our NPS went up 0.2 points” is not.
2. Make Recommendations
- Suggest one or two changes based on your findings.
- Don’t try to fix everything at once. Change one thing, measure, repeat.
3. Share with Stakeholders
- Send a cleaned-up CSV or summary, not the raw dump.
- Highlight caveats. If your sample size is tiny, or you had to exclude a bunch of test data, say so.
Step 5: Repeat (But Don’t Overcomplicate)
Data analysis is a loop, not a one-off. Don’t wait six months to check if your changes worked.
- Set a regular cadence (monthly or quarterly) to export and review submissions.
- Track changes over time, but don’t drown in data. Look for big swings, not tiny blips.
What Actually Works (And What to Skip)
Works well: - Manual CSV exports for most teams - Focusing on 1–2 key questions per analysis - Cleaning your data before diving in
Doesn’t work: - Overengineering with daily automated exports unless you’re a big team - Chasing every possible metric - Trusting built-in analytics blindly
Skip: - Building fancy dashboards before you know what you’re looking for - Worrying about “AI-powered insights” unless you have real scale and a data team
Keep It Simple—Iterate as You Go
Form data is only useful if you do something with it. Start small: export, clean, answer one question, and share what matters. You can always get fancier later. The best insights usually come from simple, messy data, not complicated tools. Keep your process light, honest, and focused—and you’ll actually learn something worth sharing.