If you’re in B2B sales, you know the pain: endless prospect lists packed with people who’ll never buy, time wasted chasing the wrong leads, and spray-and-pray tactics that just annoy everyone. If you want real results, you need lists that are so targeted they feel almost unfair. This guide is for sales teams who want to use Datagma to actually find those prospects—the ones your competition misses—without spending hours cleaning up garbage data.
Let’s cut the fluff and get practical. Here’s exactly how to build hyper-targeted prospect lists in Datagma that actually help you hit quota.
Step 1: Get Clear on Who You Actually Want
You can’t automate your way out of a fuzzy ICP (ideal customer profile). Before you even open Datagma, take 10 minutes and sketch out:
- Industry: Who buys your stuff? Be honest—don’t write “any company with 10+ employees” unless you’re selling coffee filters.
- Company size: Revenue, headcount, or both? Be picky.
- Job titles & roles: Who signs the check? Who’s the real user?
- Geography: Where do you have traction? Ignore everywhere else.
- Tech stack, hiring signals, funding, etc.: Only if it really matters.
Pro tip: If your team can’t agree on this, you’re not ready for Datagma or any tool.
Step 2: Log In and Set Up Filters Like You Mean It
Once you’re in Datagma, resist the urge to “just see what’s in there.” Start with your ICP and build out filters that match exactly what you want.
The basics:
- Company filters: Industry, size, revenue, location, funding rounds.
- People filters: Job title, seniority, department, years at company.
- Tech & intent: What tools do they use? Are they hiring? Any recent signals?
What works:
Stacking multiple filters (e.g., “Fintech, 51-200 employees, US, Head of Product, uses Stripe”) narrows down lists fast. Don’t be afraid to get specific—even if the list shrinks a lot. Quality beats quantity every time.
What doesn’t:
Don’t rely on just job titles. “VP Product” at one company can mean “glorified project manager” at another. Use seniority and department, too.
Ignore:
Flimsy “intent data” unless you’ve tested it. Most of it is noise.
Step 3: Layer On Data Enrichment (Without Drowning In It)
Datagma pulls in a lot of data—emails, LinkedIn profiles, phone numbers, company info, tech stack, even recent hires. The trick is not to get overwhelmed.
How to not waste time:
- Focus on fields that matter: For most teams, you just need a verified email, LinkedIn URL, and maybe company size or tech stack.
- Test email deliverability: Datagma shows confidence levels. Only grab “valid” contacts unless you like high bounce rates.
- Spot check data: Always check a handful of entries manually. Every data tool has duds.
Pro tip: Don’t get greedy. Extra data fields are fun, but if you’re not using them in your outreach, skip them. Cluttered CRM = unhappy future you.
Step 4: Build, Save, and Export Your Segment
Once you’ve dialed in your filters and checked the data, save your segment in Datagma. Most teams overlook this, but it’s a lifesaver for iterating or repeating campaigns later.
Export tips:
- Choose your format: CSV works for almost anything—Salesforce, Outreach, Apollo, whatever.
- Only export what you need: Don’t pull 5,000 rows if you only plan to email 200 this week.
- Map your fields: Make sure your columns match what your CRM or outreach tool expects. Fixing imports later is a pain.
What works:
Naming your lists clearly (e.g., “US Fintech Heads of Product Q2 2024”) so you don’t accidentally nuke last month’s work.
What doesn’t:
Exporting “just in case.” Old lists go stale fast, and everyone ends up working off different versions.
Step 5: Personalize, Don’t Spray
The big promise of hyper-targeted lists is better response rates. But if you blast the same message to everyone, you’ll still look like spam.
How to get actual replies:
- Segment by persona: Send different messages to VPs vs. managers.
- Reference real signals: Mention their tech stack, recent funding, or something they actually care about.
- Keep it short: Nobody wants to read your “innovative solutions” pitch.
Pro tip: You don’t need to write 100% unique emails. Even swapping out a line or two for each segment moves you to the top 10%.
Step 6: Clean Up and Iterate—Don’t Settle for “Good Enough”
No data source is perfect. Datagma is better than most, but you’ll still hit bounces, duplicates, and the occasional person who left last month.
What you should actually do:
- Track bounce rates: If you’re getting more than 5% bounces, tighten your filters or try different data fields.
- Update or remove stale contacts: Once a month, prune your lists. Out-of-date contacts waste everyone’s time.
- A/B test your filters: Try small tweaks—like narrowing job titles or company size—and see what actually gets replies.
What works:
Regular maintenance. It’s boring, but it keeps your pipeline clean and your team sane.
What doesn’t:
Trusting any tool’s data as gospel. Even the best sources have gaps.
What to Ignore (And Where Datagma Won’t Save You)
It’s easy to get caught up in “AI-powered” promises and endless data fields. Here’s what to skip:
- Shiny features you won’t use: If you’re not running complex ABM campaigns, don’t get distracted.
- Intent data hype: Unless you’ve seen it work for your exact niche, most intent signals are guesswork.
- Quantity over quality: 1,000 semi-relevant leads will always underperform 100 laser-targeted ones.
And no, Datagma—or any tool—won’t magically make people reply to bad emails.
Keep It Simple, Iterate Fast
Building hyper-targeted prospect lists isn’t magic. It’s about being honest about who you should talk to, setting up tight filters, and only grabbing the data you’ll use. Datagma makes this a lot easier, but it won’t fix a vague ICP or bad outreach.
Start narrow. Test what works. Clean up as you go. If you keep it simple and iterate, you’ll spend less time chasing dead ends—and actually have more conversations that matter.