GetProspect Checklist: How I Stopped Wasting 60,000 Cold Emails on Bad Contact Lists

Somewhere in our old CRM, there's a contact list of 12,000 people I imported in 2021. I don't really want to talk about the bounce rate. But I'll talk about it: 34%. The kind of number that gets your domain reputation flagged and your boss asking uncomfortable questions.

For the last six years, I've run outbound prospecting for B2B SaaS companies, and I've personally made (and documented) 14 significant mistakes — totaling roughly $38,000 in wasted tool credits, wasted hours, and one very memorable sender-reputation crisis. This checklist is what came out of that.

If you're a RevOps manager, an SDR team lead, or a founder doing outbound alone, this is for you. It's the exact list I walk through before every campaign, with GetProspect as the main stack. Six steps, plus the mistakes I still see people make.

Let's get into it. (Yes, the spreadsheet of failures is real.)

Step 1: Define the Persona Before You Open the Extension

I know it's tempting. You open LinkedIn, the search results are full of potential leads, and you want to hit the GetProspect extension and start pulling emails. I did that in 2019. Imported 5,000 "managers" from a broad SaaS search, celebrated the volume, then discovered most of them had zero purchasing authority.

The list took two weeks to build and two days to prove useless. Not ideal, but workable as a lesson.

What I mean is: before you touch the GetProspect LinkedIn email finder, write down your ideal contact profile in one sentence. Something like: "Head of RevOps at Series B-C SaaS companies with 50-500 employees, using Salesforce, based in North America or Europe."

If you can't say it in one sentence, the contact list you build will be a mess. Checkpoint: does every person you're about to prospect fit that sentence? If the answer is "not sure," you're not ready for Step 2.

Step 2: Use the GetProspect LinkedIn Email Finder With Filters — Not as a Net

This is where the GetProspect extension shines, and also where people get into trouble. The extension pulls email addresses right from LinkedIn profiles and company domains, which is fast. The problem: "fast" makes you sloppy.

When you run a LinkedIn Sales Navigator search, don't blitz every profile. Set the filters first — seniority, industry, company size, location — then start clicking the extension. And here's the part most people ignore: check what the title you're filtering on actually means. I once collected 2,000 "Head of Sales" profiles from European companies and later realized that in some regions, that title maps to a different seniority level than I assumed.

The result was a very expensive list of sales managers who couldn't approve a $100 trial, let alone a six-figure software deal. Worse than expected.

Quality over volume in the first pass. For every 100 profiles you look at, pull emails from maybe 30-40. If you're pulling from 90 out of 100, your filters are too loose.

Step 3: What Revenue Operations Teams Should Evaluate in Email Verification Features

This is the part that cost me the most. In September 2023, I sent 18,000 emails from a freshly built list. Verification? Skipped it. The tool I used claimed it verified emails, so I trusted that and hit send. Twelve percent bounced. That might not sound catastrophic, but for a new domain, it nuked deliverability for months.

So when I evaluate email verification features now — and I've done this as part of RevOps tooling reviews — I don't just ask "does it verify?" I ask about verification layers:

• Syntax and format checks — the bare minimum, and nowhere near enough.
• Domain and MX record checks — does the domain exist and can it receive mail?
• Catch-all and mailbox-level verification — the difference between "domain exists" and "this specific address is real." This is where the real accuracy lives.
• Role-based account detection — flags like info@, sales@, admin@ that shouldn't be in a prospecting list.
• Disposable address filtering — mailinator and friends.
• Freshness of verification data — a list verified six months ago can still be garbage by send time.

One more thing worth checking: what the tool does when it can't verify a catch-all domain. Does it mark it valid, invalid, or "unknown"? If it defaults to "valid," you have a false-positive machine. I don't have hard data on how each vendor handles this, but based on the audits I've run, the "unknown" bucket is a lot healthier than a confident "valid" guess.

Also, as of February 2024, Google's bulk sender guidelines require senders to keep spam complaint rates below 0.1% and support one-click unsubscribe. That's not optional — it's a deliverability threshold. Verify current requirements at Google's official documentation before you scale, because they keep tightening this.

So glad I added this to the checklist. I almost sent another 18,000 emails using a tool that defaulted uncertain catch-alls to "valid" — caught it during a test run of 500 addresses. Dodged a bullet.

Step 4: Build the Contact List With Deduplication and Enrichment

The contact list is where everything comes together. GetProspect's tools — the extension, email finder, verifier — feed into a central list you can filter and export. But the list is only as good as its hygiene.

First, deduplicate by email address. You'd be surprised how often the same person shows up under slightly different name spellings or multiple titles. Merge those records before anything else.

Second, don't skip enrichment. GetProspect's waterfall enrichment checks multiple sources to fill in missing or outdated fields — job changes, company updates, and that kind of thing. Frequently cited industry benchmarks put B2B list decay at roughly 22.5% per year. People change jobs, switch providers, companies get acquired. I don't have hard data on whether that exact number applies to our segment, but I've seen enough lists go stale in 6-9 months to believe the direction.

Third — the step almost everyone skips — verify again right before export. The verification you ran at collection time is a snapshot; it can be stale by the time you're ready to send. My rule now: verify at collection, verify again at export, spot-check at send if it's been more than two weeks.

Step 5: Layer Intent Data Before You Personalize

Intent data sounds like buzzword soup. Let me explain how it actually works, because it's simpler than the AI marketing around it suggests.

Intent data is a set of signals that show which companies are exhibiting active buying behavior — not just "this company exists" but "this company is researching a problem you solve right now." The signals come from multiple places: website visits, content consumption, review-site activity, product comparison searches, and so on.

GetProspect's approach includes website visitor identification and sales signals — you can see which companies are visiting your site or researching your category, and use that as a prioritization layer on top of your contact list.

Here's the practical difference. In Q2 2024, I split a 4,000-contact list in two. First, the approach I'd always used: sent a personalized-but-cold email to everyone. The whole list. (Yes, I know.) Response rate: 2-3%. Then, I took the ~300 contacts showing recent intent and sent them a tailored message referencing the specific trigger I could see. Response rate: 11%.

Same tool, same list source, wildly different outcome. Intent data doesn't replace personalization; it tells you where personalization actually earns its keep. So before you send anything, apply an intent filter. If you don't have intent data yet, get the contact list right first and treat intent as the next layer — not the other way around.

Step 6: Run the Pre-Send Compliance Check

The least glamorous step, and the one that prevents the worst outcomes. Before a single email goes out, I walk through:

• Sender domain health — SPF, DKIM, DMARC in place.
• Unsubscribe — one-click, working, visible. A requirement in most jurisdictions, and a deliverability requirement under Google's guidelines.
• Relevant content — the email has to be on-topic for the recipient. This isn't a nicety; it's what keeps complaint rates down.
• Platform limits — know what your tool allows. Don't try to bypass rate limits or scrape outside its intended workflow. That's the fastest way to get your account flagged, and it violates platform terms.
• What your plan includes — GetProspect's pricing page lists what's included per tier, including verified email credits and limits, as of January 2025. I always look at what's NOT included, because that's where surprise costs hide.

I've learned to ask "what's NOT included" before "what's the price." The tool that lists everything upfront — even if the total looks higher — usually costs less in the end. You're not buying emails; you're buying a clean, deliverable list. Treat it that way.

Common Mistakes That Still Show Up

After six years, I see patterns repeating. Maybe naming them saves you the pain.

Buying a list instead of building one. Purchased lists are full of stale and role-based addresses. I don't care how "targeted" the seller says it is. Worse than expected, every time.

Verifying too early, then never re-checking. Verification is a point-in-time snapshot. A verified email can go bad while you're still writing the campaign. Re-verify at export, not just at collection.

Personalizing without signals. "I saw you're a SaaS company" isn't personalization; it's the sentence every other email in their inbox starts with. If you don't have intent data yet, use account research. Better yet, use intent data to prioritize which accounts deserve deep research.

Scaling before the system works. A brand-new domain sending 50,000 emails in week one isn't a strategy; it's how you become a spam statistic. Start with 500. Measure bounce, complaint, and reply rates. Then scale what works.

The good news: every one of these is avoidable. The checklist isn't complicated — it's sequential. Persona first, LinkedIn email finder with filters, real verification, list hygiene, intent layer, compliance.

Do it in that order, and you'll save yourself the 34% bounce rate that still haunts my CRM. A lesson I'd rather you not repeat.