What Revenue Operations Teams Should Evaluate in Lead Enrichment: A 7-Step Checklist

Who this checklist is for

If you're a RevOps lead, GTM engineer, or the person stuck managing software vendors for a B2B sales team, this is for you. It's a 7-step checklist for evaluating lead enrichment tools before you sign an annual contract. I'm not a sales ops guru. I'm the office administrator who handles software procurement for a 150-person company. I manage about $180k in SaaS spend across 12 vendors, and I report to both RevOps and finance. So I care about workflow, invoicing, and whether the thing actually works after the demo.

We've done maybe 40 software vendor reviews. Maybe 35, I'd have to check the spreadsheet. Use this when you're comparing data enrichment platforms, email verification tools, intent data providers, or something like okki-go. It won't tell you which vendor is best. It'll tell you what to test so you don't get surprised in month three.

Step 1: Map your actual data gaps before you take a demo

Most buyers focus on coverage numbers and completely miss their own workflow gaps. The question everyone asks is 'how many contacts do you have?' The question they should ask is 'which fields are missing in my CRM right now, and which missing fields actually block sequencing?'

Pull 200-500 records from your CRM. Mark what's missing: work email, direct dial, title, company size, tech stack, intent signals. Then rank those gaps by revenue impact. If your SDRs can't sequence without verified emails, that's priority one. If they need intent data to prioritize accounts, that's a different test.

When I ran our 2024 vendor consolidation, we almost bought a tool because its coverage looked great. Then we realized it didn't fill the one field our outbound team actually needed. That would've been a $9k mistake.

Step 2: Check waterfall enrichment logic, not just match rate claims

Waterfall enrichment means a vendor checks multiple data sources in sequence until it finds a match. It's a good idea. But the details matter. What most people don't realize is that a '90% match rate' can mean different things depending on the record set, the geography, and how the vendor counts a match.

Ask these questions:

• Which sources are in the waterfall, and in what order?
• How does it handle conflicting data between sources? Does it pick the newest, the most frequent, or the one that matches your CRM?
• Does it enrich at the contact level, account level, or both?
• What happens when no source has a match? Does it return a partial record or nothing?

If you're evaluating okki-go for GTM engineers, test its waterfall enrichment plus intent signals on your own list. Don't trust a generic sample file. Your ICP is not their demo data.

Step 3: Test email verification on your worst records

Email verification is where a lot of lead gen budgets leak. Vendors won't tell you this, but 'valid' can mean 'syntactically valid' or 'SMTP-verified' or 'catch-all domain we can't really verify.' Those are not the same.

Take 100 records you already know are risky: old leads, catch-all domains, role-based addresses. Run them through the tool. Check how many are marked valid, invalid, catch-all, or unknown. Then send a small test sequence and watch bounce rates. I'm not saying any tool can guarantee 100% accuracy—that's a red flag if they do. I'm saying you need to know how the tool behaves on the messy data you actually have.

For B2B lead generation, a 5% bounce rate can still hurt your domain reputation. So test before you scale.

Step 4: Evaluate intent data for relevance and freshness

Intent data is one of those things that sounds amazing in a pitch. In practice, it's only useful if the signals match your buying cycle and your ICP. Most buyers ask 'what intent topics do you cover?' The better question is 'how often is the data refreshed, and what's the decay curve?'

Ask the vendor to show you intent signals for 10 accounts you already know are in-market. Then show you 10 accounts that are not. If the tool can't separate them, the intent data isn't ready for your workflow.

Also check how intent data gets into your system. Is it a dashboard? A CRM field? A webhook? For GTM engineers, this is where okki-go features like agent-native prospecting and API-first enrichment can matter. You want intent to trigger actions, not just sit in a report.

Step 5: Verify GTM engineer compatibility

If you have a GTM engineer, they'll care about APIs, webhooks, rate limits, and reverse ETL. If you don't, you'll care about whether the tool works inside your CRM without a six-week integration project.

Here's a quick compatibility checklist:

• Does it have a sandbox or test environment?
• Can it sync bi-directionally with your CRM, or is it one-way?
• How does it handle deduplication and record ownership?
• What are the rate limits, and what happens when you hit them?
• Can you export raw enrichment JSON, or are you locked into their UI?

I've seen vendors promise a 'seamless integration' that turned out to be a CSV upload. That's not seamless. That's a part-time job.

Step 6: Calculate true cost per usable record

This is the step finance will thank you for. Seat-based pricing looks simple until you add credits, overages, enrichment failures, and the manual time your team spends cleaning data.

Build a simple model:

1. Take the number of records you need to enrich per month.
2. Multiply by the vendor's credit cost per successful match.
3. Add the cost of failed matches or partial records.
4. Add the hours your team spends on QA and deduplication.

When I compared vendors in January 2025, the cheapest headline price was actually the most expensive once we factored in failed matches. I had to explain that to our CFO. Not fun.

Had 2 hours to decide before our renewal window closed. Normally I'd run a 30-day pilot, but there was no time. Went with the vendor that had a sandbox and transparent credit pricing. In hindsight, I should have pushed back on the timeline. But with the CEO waiting, I made the call with incomplete information.

If you're looking at okki-go, ask for a cost-per-usable-record estimate, not just a credit price. And don't accept 'guaranteed ROI' claims. No one can guarantee reply rates.

Step 7: Run a human-in-the-loop pilot

This is the step most teams skip. They buy the tool, turn it on, and let it sequence 5,000 contacts. Then they wonder why the reply rate is terrible.

Run a pilot with 50-100 records and a human reviewing every message before it goes out. The goal isn't to prove the AI can write. The goal is to prove the data is good enough that a human can write a relevant message in under two minutes.

That's where human-in-the-loop outreach matters. okki-go is built around that idea. It's not designed to fully replace your SDRs or RevOps team. If that's what you need, this isn't the right category. But if you want your team to spend less time hunting for data and more time talking to prospects, it's worth testing.

Common mistakes to avoid

Here are the things I've seen go wrong—sometimes because of vendors, sometimes because of us.

• Buying on coverage alone. A million contacts mean nothing if 30% are stale.
• Skipping the negative test. Always test invalid records. If everything comes back valid, the tool isn't verifying.
• Ignoring data retention and compliance. Ask where data is stored, how long it's kept, and whether you can delete it on request. GDPR and CCPA don't care about your sales targets.
• Treating enrichment as one-time. Data decays. You need a refresh cadence, not a one-off project.
• Letting the vendor define success. You define success: fewer bounces, higher SQL conversion, less manual research time.

The honest limitation

No lead enrichment tool fixes a bad ICP or a weak offer. If your team doesn't know who to target, data enrichment just helps you target the wrong people faster. And if you're a tiny team with no GTM engineer and no clean CRM, a platform like okki-go might be more than you need. Start with a spreadsheet and a verification tool. That's okay.

For teams doing B2B lead generation at scale, though, the right evaluation process is worth the effort. Use this checklist. Test on your data. Ask the uncomfortable questions. Then decide.

What most people don't realize is that vendors would rather you compare features than outcomes. Compare outcomes on your own records, and the right choice gets a lot clearer.