How to validate and update outdated contact records with Peopledatalabs

If you’re sitting on a pile of old contact records, you know the drill: bounced emails, wrong job titles, people who don’t work there anymore. Dirty data wastes time and money. This guide is for anyone—marketers, sales ops, CRM admins—who actually wants to fix it instead of just talking about “data hygiene.” Let’s get straight to how you can validate and update those dusty records with Peopledatalabs, what works well, and what’s honestly a waste of time.

Why bother with contact validation and updates?

Bad data isn’t just annoying—it’s expensive. Here’s what usually goes wrong:

  • Emails bounce or get you flagged as spam.
  • Sales teams chase ghosts.
  • Marketing dollars get burned on dead leads.

A lot of people throw tools at the problem and hope for the best. But if you want real results, you need a process. Peopledatalabs can help, but only if you use it right.

Step 1: Figure Out What Needs Updating

Before you touch Peopledatalabs, get your house in order. No tool can fix a mess if you don’t know what you’re fixing.

  • Export your contacts. Pull a CSV or spreadsheet from your CRM, marketing tool, or wherever you keep contacts.
  • Decide what “outdated” means for you. Is it anyone not updated in 12 months? Anyone with missing info? Define it.
  • Pick the fields you care about. Usually it’s email, job title, company, maybe phone. Don’t bother with info you never use.

Pro tip: If your data is really messy (lots of duplicates, weird formatting), clean it up a bit first. Garbage in, garbage out.

Step 2: Prep Your Data for Peopledatalabs

Peopledatalabs works best if you feed it clean, well-structured data. Here’s how to prep:

  • Standardize your columns. Make sure each column in your export matches what Peopledatalabs expects (e.g., email, first_name, last_name, company).
  • Remove unnecessary data. Strip out columns you don’t need to update.
  • Check for obvious errors. No point running “test@test.com” or “Mickey Mouse” through an API.

If you skip this, you’ll waste credits (and possibly money) on junk.

Step 3: Choose Your Peopledatalabs Approach

Peopledatalabs gives you a few ways to work with their data:

  • Enrichment API: Good if you want to automate updates or do it regularly.
  • Bulk Enrichment: Better for a one-off, big batch (think: thousands of contacts).
  • Manual lookup: Fine for one-off checks, but no one wants to do this at scale.

For most folks updating old records, the Bulk Enrichment method makes the most sense. The API is great if you want ongoing syncs, but you’ll need engineering help.

What to ignore: Don’t bother with manual lookups unless you’re updating a handful of VIPs. It’s tedious and slow.

Step 4: Set Up and Run Your Bulk Enrichment

Here’s what you actually do:

  1. Sign up or log in to Peopledatalabs.
  2. Find the Bulk Enrichment section. (The UI changes sometimes, but look for “Bulk” or “Batch” options.)
  3. Upload your CSV. Make sure the columns match what Peopledatalabs expects. Most systems are picky about headers.
  4. Map your columns. Double-check that “email” matches their “email,” “company” matches “company,” etc.
  5. Set your output fields. Only pull what you need—more fields usually means more cost.
  6. Start the enrichment. Depending on how many records you have, this could take minutes or hours.
  7. Download your results. You’ll get a new CSV with updated info (or, sometimes, blanks if PDL couldn’t find a match).

Heads up: Peopledatalabs charges by the record (or “credit”), even if they don’t find new info. So filter your list before you upload.

Step 5: Make Sense of the Results

Now you’ve got a file with updated records, but you’re not done.

  • Check the “match” quality. Not every result will be accurate or up to date. Some matches will be guesses.
  • Spot-check important contacts. Double-check C-levels or key accounts. Don’t blindly trust automation.
  • Handle blanks or partial updates. If PDL can’t find new info, those fields will stay empty. Don’t overwrite good data with blanks.

Pro tip: Don’t bulk overwrite everything in your CRM. Import the updates to a staging area first, or flag updated fields so you can review.

Step 6: Update Your CRM (Safely)

Time to get the clean data back into your main system:

  • Back up your CRM first. Seriously, don’t skip this. Even pros mess up imports.
  • Use import tools with “update” mode. Most CRMs (Salesforce, HubSpot, etc.) let you match on email or ID and update fields.
  • Don’t overwrite fields you care about. If someone’s email changed but you’ve been talking to them at their old address, think before updating.
  • Flag records you’ve updated. Add a custom field like “Last Validated” so you know what’s fresh.

What to watch for: Automated imports can break things. Run a small test batch first.

What Works Well (and What Doesn’t)

What works:

  • PDL is fast for big batches. Set it up, run it, and you’ll get results back quickly.
  • It’s especially good at updating job titles, companies, and LinkedIn URLs.
  • If you have emails, PDL can usually find the latest info—even if someone changed jobs.

What doesn’t:

  • PDL isn’t magic. It can’t find everyone, especially if people keep their profiles locked down.
  • Phone numbers are hit-or-miss. Don’t expect miracles.
  • If your data is really old or fake, don’t expect much.
  • You pay for every lookup—even the misses. Be picky about what you send.

Ignore the hype: No tool will give you 100% accuracy. Always sanity-check the results, especially for important records.

Keep It Simple and Iterate

Don’t overthink it. Start small—a subset of your database, not the whole thing. See what comes back, update your process, and iterate. Chasing “perfect” data is a fool’s errand. Your goal is simply to make it better than it was, and keep it that way with regular checkups.

You’ll save yourself (and your team) a ton of headaches by keeping your process straightforward: prep, enrich, review, and update. Rinse and repeat when things get stale again. That’s all there is to it.