If you've ever tried to get a clean list of companies—industry, size, location, and all that—from a data platform, you know it rarely goes smoothly. This guide is for anyone who needs to pull company firmographics out of Peopledatalabs and actually make use of them. Maybe you're in sales ops, marketing, product, or just trying to build a better list than the last person. Either way, if you're tired of messy exports and unclear fields, this one's for you.
Let's get right into it.
What are “firmographics” and why do they matter?
Firmographics are just business stats: company size, revenue, industry, location, founding date, and so on. It’s the stuff you use to segment companies, prioritize leads, or just figure out who you’re actually dealing with.
Why bother? Because if you’re selling to businesses, you need to know who’s big, who’s small, who’s in your industry, and who’s not worth your time. If you’re just pulling random lists, you’re wasting everyone’s time—including your own.
Step 1: Understand what Peopledatalabs actually offers
First, a reality check: Peopledatalabs is a B2B data provider. They’re not the only game in town, but they do have a big dataset, especially for tech and mid-market companies. They surface company-level data like:
- Name and domain
- Industry (often mapped to NAICS or proprietary categories)
- Size (employee count, sometimes revenue estimates)
- Location (HQ city, country)
- Founding year, social profiles, and more
What’s good: The breadth. You’ll find a lot of companies you won’t get from “free” sources.
What’s not: Data freshness can be hit-or-miss, especially for smaller or international firms. Don’t expect perfect accuracy—no one in this business has it.
Ignore: Hype about “AI-powered insights.” Focus on raw fields you can actually use.
Step 2: Decide what you actually need
Before you even log in, figure out what you want. Here’s why: The more precise your request, the less cleanup later.
Ask yourself: - Which firmographic fields do you really need? (e.g., employee count, revenue, industry) - What’s your filter? (Industry? Geography? Company size?) - Do you need unique companies, or are you mapping people to companies?
Pro tip: Don’t grab every field “just in case.” Every extra column adds more mess to clean up. Most people use:
- Company name, domain
- Industry
- Employee count
- HQ country/city
- LinkedIn URL (optional, but handy for enrichment)
Step 3: Exporting company firmographics from Peopledatalabs
There are a few ways to get data out of Peopledatalabs. Here’s the real talk on each:
Option 1: Use their web platform (if you have access)
If you’re on a paid plan, you might get a “Company Data” section. Search, filter, and export straight to CSV.
How-to: 1. Log in. Go to the Companies tab. 2. Set your filters (industry, country, company size, etc.). 3. Preview your results—make sure they look right. 4. Hit Export. Choose CSV.
What works: Fast, simple. Good for quick lists or one-off projects.
What doesn’t: Export limits are common. Sometimes you’ll only get a sample, or it’ll cap at a few thousand rows. If you need everything, you’ll hit a wall.
Option 2: Use the API
If you need more data—or want to automate—the API is the way to go. It’s RESTful, with documentation that’s readable (for once).
How-to: 1. Get your API key from your account dashboard. 2. Review the Company API docs. 3. Build your query. For example, to get all SaaS companies in the US with 50–200 employees:
bash
curl "https://api.peopledatalabs.com/v5/company/search" \
-H "X-Api-Key: YOUR_API_KEY" \
-G --data-urlencode 'industry=software' \
--data-urlencode 'location.country=United States' \
--data-urlencode 'size=50-200'
- You’ll get JSON back. Parse it and write to CSV (Python, Node, whatever you prefer).
What works: Full control, no UI limits, great for automation.
What doesn’t: Rate limits kick in fast. If you try to pull 100,000 records in one go, expect throttling or even a ban. You may need to “page” through results.
Ignore: Fancy enrichment endpoints unless you know exactly why you need them. Stick to the basics.
Option 3: Data enrichment (if you have a list of domains)
If you already have a list of company domains but want to fill in the blanks, use their enrichment endpoint.
How-to: 1. Upload or send a batch of domains. 2. Receive back a file with all available firmographics appended.
What works: Great for filling gaps—no need to start from scratch.
What doesn’t: Coverage depends on your input quality. Junk in, junk out.
Step 4: Cleaning up your exported data
No matter how you export, your data will be messy. Expect: - Duplicate companies (same company, different domains/variants) - Inconsistent industry names or codes - Empty or obviously wrong fields (e.g., “1 employee” for Microsoft)
Quick and dirty cleanup tips: - Deduplicate by domain—it’s more reliable than company name. - Standardize industry labels—pick one taxonomy (NAICS, SIC, or just a short list of your own). - Filter out junk—drop companies with missing domains or clearly bogus data. - Normalize employee and revenue bands—turn ranges into buckets you’ll actually use.
Pro tip: Use a spreadsheet for small lists, or Python/pandas if you’re dealing with thousands of rows.
Step 5: Analyzing firmographics for real insights
Here’s where most people go wrong: They pull a list and stare at it, hoping the answers jump out. Don’t do that.
Instead, ask specific questions, like: - What % of my list are in my target industry? - How many companies are actually in my “ideal customer size”? - Where are most of these companies based?
How to get useful answers: - Pivot tables (Excel/Sheets): Group by industry, size, country. See where your list is skewed. - Simple bar charts: Show distribution—don’t overcomplicate. - Look for outliers: If 80% of your “SaaS” companies have under 5 employees, something’s off with your filters or the source data.
What works: Simple visualizations and clear filters. Don’t get fancy unless you have a real reason.
What doesn’t: Blindly trusting the industry or size fields. Always sanity check—if it looks weird, it probably is.
Step 6: Keep your expectations in check
No data provider is perfect. Peopledatalabs is better than most, but: - Company sizes are estimates—nobody outside HR knows the real headcount. - Industry tags can be broad or outdated. - Some companies just don’t exist in any database.
If you need perfect accuracy, you’ll be disappointed. But for segmentation, lead scoring, or market scans, it’s good enough—if you keep your filters tight and do some cleanup.
Pro tips and pitfalls to avoid
- Don’t pay for more fields than you’ll use. More data is just more noise if you’re not using it.
- Test with a small export first. Don’t waste credits or time pulling 50,000 records before you know what you’re getting.
- Validate with a sample. Pick 10 random companies and Google them—do the fields match reality?
- Don’t obsess over tiny differences in employee counts. Use broad buckets (e.g., 1–10, 11–50, 51–200, etc.).
- Automate cleanup if you’re doing this often. A few scripts can save you hours.
Wrapping up: Keep it simple, iterate, and don’t chase “perfect” data
Exporting and analyzing company firmographics from Peopledatalabs is straightforward—if you know what you want and don’t expect the moon. Focus on the basics: get a clean export, do some smart cleanup, and ask questions that matter. Don’t drown in extra fields or try to make the data say more than it does.
Start small, learn as you go, and don’t be afraid to iterate. The best lists are the ones you actually use.