So you’ve got a list of prospects, but you’re not sure how good it is. Maybe you scraped it from LinkedIn, got it from a list vendor, or pieced it together by hand. Either way, you know the truth: prospect data is almost always messy, out of date, or just plain wrong. If you want to waste less time chasing dead ends (and stop annoying people who don’t work there anymore), you need to enrich and validate that data before you do anything else.
That’s where Browse comes in. It’s a tool that helps you check, fill in, and double-check prospect data using real web sources—without the usual headaches. This guide is for anyone who wants to clean up their contact lists, spot the fakes, and get a fighting chance at real conversations.
Let’s get into how to actually use Browse to make your prospect data less of a dumpster fire.
Step 1: Get Your List Ready
Before Browse can help, you need something to feed it. Here’s what to do:
- Start with what you have. Excel, Google Sheets, CSVs, or a CRM export all work. You don’t have to have perfect fields—just try to get names, company, job title, and email if you have them.
- Don’t obsess over formatting. Browse is built to handle messy input, so don’t waste hours fixing column names or removing empty rows. Just make sure you’re not uploading a 10,000-row file if you only want to check 200 people.
- Remove obvious junk. Scan for rows that are clearly spam, have missing names, or look like gibberish. The cleaner your starting list, the less noise you’ll get later.
Pro tip: If you’re combining lists from different sources, add a column for “Source” so you can track where bad data is coming from. You’ll thank yourself later.
Step 2: Upload to Browse
Now, bring your list into Browse. Here’s how:
- Log into your Browse account. If you don’t have one, signing up is straightforward—no credit card required for the trial.
- Upload your file. Drag and drop your CSV or spreadsheet file. Browse will try to auto-detect columns.
- Map your fields. Double-check that Browse’s guesses about columns match what’s actually in your file. Fix anything that’s off—especially emails, names, and company names.
What works: Browse usually handles even ugly spreadsheets well. You don’t need a fancy template.
What doesn’t: If your data is in a weird text format, or you have merged cells everywhere, clean that up first. Browse can’t read your mind.
Step 3: Pick What to Enrich and Validate
Browse isn’t magic—it needs to know what you care about. For most prospecting, you’ll want to:
- Enrich missing data: Fill in missing job titles, company domains, or LinkedIn URLs.
- Validate emails: Check if an email is likely valid, risky, or a dead end.
- Confirm job and company info: Make sure “Sally Jones, CTO at Acme Corp” actually works there, and that Acme Corp is a real company.
Ignore the noise: Don’t bother enriching every single possible field “just because.” Focus on what you actually need for outreach—usually name, title, company, and email.
Step 4: Let Browse Do Its Thing
When you hit “Go,” here’s what Browse actually does:
- Searches live web sources: LinkedIn, company websites, and other directories to cross-check info.
- Flags mismatches: If the person’s email domain doesn’t match their company, or if their title doesn’t show up anywhere, you’ll know.
- Fills in blanks: Finds missing company domains, LinkedIn profiles, or job titles if they’re available publicly.
- Checks for obvious fakes: Flags disposable emails, “catch-all” addresses, or records that look auto-generated.
What works: Browse is great at catching obvious errors—like someone who left the company years ago, or emails that never existed.
What doesn’t: Don’t expect it to magically find private cellphone numbers, or fix data that doesn’t exist anywhere online. If someone is stealthy or new to their job, no tool can invent that info.
Step 5: Review and Sanity-Check the Results
Browse gives you a cleaned-up spreadsheet (or lets you view results in-app). Here’s how to handle it:
- Spot the rejects: Browse will flag records it couldn’t validate, or where info didn’t match. Don’t just delete these—sometimes there’s a typo or a legit new hire.
- Look for patterns: If a whole batch of records from one source is bad, you may want to toss the whole batch.
- Export your “best” list: Filter for verified emails, matched job titles, and real companies. This is your gold list.
Pro tip: Don’t trust any tool 100%. If a record seems “off” (like a CEO whose LinkedIn says “retired”), double-check before you send that big pitch.
Step 6: Use the Data—But Don’t Get Greedy
With your cleaned and enriched list, you’re ready for outreach, but here’s the honest reality:
- Outreach still fails sometimes. Even the best data can go stale overnight—a person changes jobs, or the company rebrands.
- Don’t spam everyone. Use your data as a starting point for real, personalized outreach. “Dear {First Name}” won’t cut it.
- Keep improving your source lists. Tag sources, track which ones yield good results, and ditch the ones that always turn up duds.
What Browse Does Well (and What It Doesn’t)
Let’s be honest:
What works: - Cleans up messy prospect lists fast. - Flags the obviously bad or outdated info. - Fills in blanks for key fields like company, title, and email—when that info is public.
What doesn’t: - Can’t invent data that doesn’t exist online. - Won’t scrape private databases or break any laws. - Not a crystal ball—if someone’s just started a new job, it may lag a few weeks behind.
Ignore any hype about “100% accurate” data. No tool can promise that, and anyone saying otherwise is selling snake oil.
Pro Tips for Getting the Most Out of Browse
- Combine with manual checks. For your top prospects, do a quick LinkedIn or company site check yourself.
- Don’t pay for more enrichment than you need. Focus on the fields that move the needle—usually company, role, and email.
- Stay compliant. Always check your own outreach is legal and ethical, especially if you’re dealing with personal emails.
Keep It Simple, Iterate, and Don’t Chase Perfection
You don’t need a complicated, 12-step enrichment process. The key is to get your list clean enough to avoid obvious mistakes, then move fast and update as you learn. Browse is a solid tool for making your data better—not perfect, but way less painful. Start small, see what works, and keep tuning as you go. Don’t let perfect be the enemy of “good enough to get started.”