Finding the right people—actual decision makers, not just anyone with “manager” in their title—in a specific industry can eat up hours you’ll never get back. If you’re in sales, recruiting, or market research, you already know the pain: endless LinkedIn searches, outdated databases, and way too many false positives.
Peopledatalabs is a data platform that claims to make this a lot easier (read: faster and less frustrating). If you want to cut through the noise and get straight to the people who can say “yes” to your pitch, product, or partnership, this guide is for you.
Below, I’ll walk you through how to use Peopledatalabs filters to zero in on decision makers in any industry. I’ll tell you what works, what to skip, and how to avoid time-wasting dead ends.
1. Get Clear on Who You Actually Need
Before you touch a single filter, do yourself a favor: define what “decision maker” really means for your use case. This is where most people screw up.
- Titles are not enough. “VP” at a 10-person startup is very different from “VP” at a Fortune 500.
- Industry matters. “Head of Operations” in healthcare isn’t the same as in logistics.
- Seniority ≠ Influence. Sometimes a “Director” runs the show; other times, it’s a “Lead.”
Pro tip: Write down a quick list: - Job titles that make sense (be specific—e.g., “Head of Procurement” vs. just “Procurement”) - Company sizes that match your target - Industries that actually buy what you’re selling
2. Understand Peopledatalabs Filters (and Their Limits)
Peopledatalabs gives you a lot of ways to slice and dice their data. Some are super useful; others less so. Here’s the lay of the land:
The filters you’ll use most: - job_title: The person’s job title (standardized, but people get creative—so it’s imperfect) - job_seniority: Their level (e.g., “manager,” “director,” “vp,” “cxo”) - industry: What industry their company is in (can be broad or very specific) - company_size: Employee headcount at their company (ranges or specific numbers) - location: City, state, or country - department: The function (e.g., “engineering,” “sales,” “hr”)
The filters to take with a grain of salt: - skills: Sometimes accurate, sometimes wishful thinking - keywords: Can pull in odd results if you’re too vague
What’s missing:
Peopledatalabs doesn’t always have up-to-the-minute data. People change jobs, companies get acquired, titles shift. Don’t expect perfection. Use the filters to get close, then plan to sanity-check your final list.
3. Build Your Search: Step-by-Step
Here’s how you actually use the filters to find decision makers in a given industry:
Step 1: Set Your Target Industry
Start narrow. Use the industry filter to zero in on your sector. Peopledatalabs uses standard industry codes (like NAICS or custom categories), so double-check what terminology they use.
- Example:
industry: "information technology"
or
industry: "healthcare"
Watch out:
Go too broad (“tech”) and you’ll get a noisy list. Go too narrow and you’ll miss people. Don’t be afraid to run the search a couple of ways.
Step 2: Add Company Size (If It Matters)
If you only want enterprise accounts or SMBs, use company_size. This saves you from doing a lot of manual cleanup later.
- Example:
company_size: [201-500, 501-1000]
Heads up:
Company size data is not always perfect—especially for fast-growing or stealth companies. Treat it as a filter, not gospel.
Step 3: Focus on Seniority
The job_seniority filter is your friend. Typical values: “manager,” “director,” “vp,” “cxo,” “owner,” etc.
- Example:
job_seniority: ["vp", "cxo", "director"]
Don’t just pick “cxo.”
In some industries, Directors or even Managers make big decisions. Look at your customer list, see what titles sign the contracts, and use those.
Step 4: Refine with Job Titles
Now, get specific. Use job_title to hunt for the exact roles you want.
- Example:
job_title: ["Head of Marketing", "VP Marketing", "Chief Marketing Officer"]
Why bother?
Seniority alone gets you close, but “VP of Operations” and “VP of Sales” have very different priorities.
Tip:
Search for common variations and abbreviations. People write their titles in all sorts of ways (e.g., “VP Sales,” “Vice President Sales,” “V.P. of Sales”).
Step 5: Narrow by Department (Optional)
If you’re getting too many irrelevant titles, add department.
- Example:
department: "finance"
This is especially helpful for big companies where “VP” could mean anything from “VP of Engineering” to “VP of Snacks.”
Step 6: Filter by Location (Optional)
If you care where the decision maker sits (for territory reasons, compliance, or just avoiding jet lag), use location.
- Example:
location: "San Francisco"
or
location: "United States"
4. Avoid Common Pitfalls
- Don’t overfilter. Stack too many filters and you’ll get zero results. Start broad, then tighten things up.
- Don’t trust job titles 100%. People fluff their resumes. “Head of Growth” at a 3-person shop isn’t a power broker.
- Don’t assume company size is current. It’s as good as the last time someone updated it. Sanity-check big deals.
- Don’t ignore department. “VP” means something totally different in HR than in Product.
5. Clean Up Your Results
Peopledatalabs can get you 80% of the way there, but there’s always some junk in the list:
- Remove obvious mismatches. Titles that don’t fit, strange companies, or duplicate contacts.
- Check for recency. If you can, filter by “last updated” to weed out stale profiles.
- Spot check a few profiles on LinkedIn or company websites before you hit “export.”
Shortcut:
If you’re pulling a big list, sample 10-20 entries at random and see how many are genuinely usable. If it’s less than 60-70%, tweak your filters.
6. Export and Take Action (But Don’t Spam)
Once you’re happy with your list, export it into your CRM or outreach tool. But remember:
- Don’t blast everyone. Targeted, personalized outreach always works better.
- Don’t rely on email accuracy. Data platforms do their best, but bounces happen. Always validate emails before sending.
- Don’t push your luck. If people ask to be removed or say no, respect it. Burning bridges isn’t worth it.
What Works (and What Doesn’t)
What actually works: - Layering industry, seniority, and title filters (in that order) - Sampling your results before committing to mass outreach - Staying flexible—adjust your filters as you learn what comes back
What doesn’t: - Blindly trusting every “decision maker” label - Overcomplicating with too many niche filters (you’ll just shrink your pool) - Expecting real-time accuracy from any data provider (Peopledatalabs included)
What to ignore: - Hype about “AI-powered” matching—stick with clear, human-checked filters - Overly broad filters (“executive,” “technology”)—these just waste your time
Keep It Simple—and Iterate
Don’t overthink it. Start with industry, add seniority, then layer in job titles. Run your search, check your results, and adjust. You’ll get better at spotting the patterns (and pitfalls) the more you do it.
Finding real decision makers isn’t magic—it’s process. Use Peopledatalabs filters smartly, keep your expectations realistic, and you’ll save yourself a lot of headaches. When in doubt, less is more.