Okki-Go Outbound Research: A 7-Step Checklist for RevOps Teams Evaluating Data Enrichment, Email Verification, and GTM Automation

I'm a procurement manager at a 180-person B2B SaaS company. I've managed our sales tooling and data budget ($210,000 annually) for 6 years, negotiated with 30+ vendors, and documented every order in our cost tracking system. When RevOps asks me to evaluate data enrichment and GTM automation, I don't start with feature checklists. I start with TCO.

This checklist is for RevOps teams evaluating B2B contact data solutions, email verification services, and GTM automation platforms—including Okki Go outbound research tools from okkigo and AI agent workflows. It has 7 steps. Follow them in order and you'll avoid the two biggest traps: comparing per-credit prices and assuming the vendor's demo data reflects your actual CRM.

Quick caveat: This worked for us, but our situation was a North America and EMEA B2B SaaS company with a defined ICP. Your mileage may vary if you're in APAC, healthcare, or a highly regulated industry.

Step 1: Map the real cost per usable record

Vendors love to quote cost per credit. That's basically meaningless until you know how many records are actually usable. In Q1 2026, we collected quotes from 8 vendors. The per-credit prices ranged from $0.08 to $0.42. But when we ran a 500-record sample through each vendor's email verification service, the usable rate ranged from 61% to 88%.

Do the math on cost per usable record:

• Vendor A: $0.10 per credit, 65% usable = $0.154 per usable contact.
• Vendor B: $0.18 per credit, 85% usable = $0.212 per usable contact.

Vendor A looked cheaper. It wasn't. Plus, you need to factor in enrichment gaps. Ask for field-level fill rates for the fields you actually use: direct dial, title, company size, technology stack.

Checkpoint: Don't accept a demo. Ask for a sample of 500 contacts that match your ICP. Run it through your own email verification service or a third-party validator. If the vendor won't share a sample, that's a red flag.

Step 2: Separate contact data, intent data, and automation seats

B2B contact data solutions, intent data, and GTM automation are usually sold as separate line items. But they often get bundled into one platform quote. That's where hidden costs hide. I built a TCO spreadsheet after getting burned on hidden fees twice.

Line items to demand in writing:

1. Data credits (contacts, companies, exports)
2. Enrichment credits (waterfall enrichment, phone, technographics)
3. Email verification seats or verification volume
4. Intent data (topics, surges, web visitors)
5. Automation seats (AI agent, sequences, CRM sync)
6. API calls, webhooks, and overage charges
7. Onboarding, training, premium support, and professional services

In 2023, we saved $3,200 by skipping a vendor's enrichment add-on. Ended up spending $9,800 on SDR manual research and bounce cleanup. Net loss: $6,600. That's the penny-wise, pound-foolish trap.

Checkpoint: Ask for a sample invoice after 30 days of projected usage. If the vendor can't produce one, you're likely going to see overages.

Step 3: Ask how the AI agent handles outbound research—and where humans stay in the loop

Agent-native prospecting is the hot category. Okki Go AI agent and similar tools promise to do outbound research, draft sequences, and enrich records automatically. Honestly, some of it is pretty good. But you need to know what the agent actually does versus what it claims.

Questions to ask:

• Can the agent show its sources for each data point?
• Can you edit the research prompt or scoring logic?
• Does it send automatically, or does a human approve each message?
• How does it handle opt-outs and suppression lists?
• What happens when the agent is wrong—who fixes the CRM record?

I think human-in-the-loop outreach is non-negotiable for most B2B teams. Not because AI can't write, but because a bad data point sent to a prospect can cost you a relationship. No AI agent fully replaces human SDRs or RevOps teams. The good ones reduce manual research time so your team can focus on conversations.

Checkpoint: Run a live test. Give the agent 20 target accounts and compare its output to what a human SDR would produce. Measure accuracy, time saved, and CRM cleanliness.

Step 4: Test enrichment accuracy on your actual CRM, not demo data

Demo data is clean. Your CRM is not. Before we signed anything, we took 200 records from our CRM with known-good data: 100 that were accurate and 100 that had missing or stale fields. Then we ran them through each vendor's waterfall enrichment + intent workflow.

We measured:

• Field-level accuracy (email, direct dial, title, company size)
• Freshness (how many records were updated in the last 90 days?)
• Duplicate creation rate
• CRM sync errors

One vendor had a 92% match rate on email but only 54% on direct dial. Another had great firmographics but no intent signals for our niche. This worked for us, but our situation was a mid-size B2B SaaS company with a North America and EMEA focus. If you're dealing with APAC phone numbers or healthcare compliance, the calculus might be different.

Checkpoint: Define good enough before the test. For us, it was 85% email accuracy, 70% direct dial, and less than 2% duplicate rate. Your numbers will vary by ICP.

Step 5: Model the cost of a bad contact—and the cost of a missed deadline

Here's where time certainty matters. In March 2024, we paid an extra 15% for guaranteed delivery of a verified contact list before a webinar. The alternative was missing a $15,000 event. That premium bought certainty, not just speed. After getting burned twice by probably-on-time promises, we now budget for guaranteed delivery when a campaign has a hard deadline.

Ask vendors about:

• SLA (service-level agreement) for delivery and support response
• Rush delivery fees and what guaranteed actually means
• Credits or refunds if they miss the SLA
• Turnaround time for custom enrichment or intent data

No email verification service can guarantee 100% deliverability. Anyone who promises that is lying or doesn't understand how email works. But a vendor can guarantee a delivery window, a replacement policy, and a support escalation path. That's what you're paying for.

Checkpoint: Put the SLA in the contract. We'll try is not an SLA.

Step 6: Negotiate exit rights and data portability before you sign

This is the step most RevOps teams skip. You focus on onboarding, integrations, and pilot results. Then two years later you want to switch vendors and realize your enriched data is stuck in their platform.

Before signing, ask:

• Can we export all enriched fields, including waterfall enrichment history?
• Do we own the enriched data, or does the vendor?
• What happens to unused credits if we cancel?
• Is there a termination-for-convenience clause?
• How long after cancellation do you retain our data?

I can only speak to our experience, but we now require data portability in every data vendor contract. It's not about being pessimistic. It's about not being locked in. Long-term supplier relationships are great—when both sides are aligned. If the vendor won't give you a clean exit, that tells you something.

Checkpoint: Get the export format in writing. CSV is fine. A proprietary JSON schema is not.

Step 7: Run a 30-day parallel pilot with one ICP segment

The final step is a controlled pilot. Don't roll out to the whole sales team. Pick one ICP segment, one SDR or one AI agent workflow, and one success metric. Run it parallel to your current process for 30 days.

What should revenue operations teams evaluate in data enrichment company GTM automation? I'd track:

• Cost per usable contact
• Cost per qualified meeting or opportunity
• Email bounce rate (not reply rate—vendors can't guarantee replies)
• CRM data health before and after
• Time saved per SDR per week
• Support ticket response time

In Q2 2024, we ran a pilot with a vendor that looked great in demos. After 30 days, the cost per qualified meeting was 22% higher than our existing process. We didn't renew. The pilot paid for itself by preventing a bad annual contract.

Checkpoint: Define the kill criteria before the pilot starts. If you don't, you'll rationalize bad results because switching is painful.

Common Mistakes and Watch-Outs

So, bottom line: the cheapest per-credit price is usually not the lowest TCO. Here are the mistakes I see most often:

• Buying on per-credit price. Always calculate cost per usable record.
• Skipping legal review. Under GDPR (effective May 25, 2018), you need a lawful basis for processing personal data. Verify current requirements at gdpr.eu. For commercial email in the U.S., the FTC's CAN-SPAM Act (effective January 1, 2004) requires a clear opt-out and physical address. Verify at ftc.gov.
• Letting AI send without human review. Agent-native prospecting is powerful, but your brand reputation is on the line.
• Assuming vendor data is 100% accurate. No one is. Build a verification step into your workflow.
• Forgetting ramp time. New tools take 2-6 weeks to tune. Include that in your ROI model.

Prices I mentioned are based on vendor quotes we collected in Q1 2026; verify current pricing, as rates change. And remember: this worked for us, but your mileage may vary depending on your ICP, regions, and compliance requirements.