What Should Revenue Operations Teams Evaluate in a B2B Contact Data Platform? A Six-Gate Checklist

If you're a revenue operations lead comparing B2B contact data platforms, you probably already built a spreadsheet with columns for record volume, enrichment coverage, integrations, and price. I build spreadsheets like that too — I just fill in different columns.

I'm the quality and brand compliance manager at okkigo. On a normal week, that means reviewing exports before they reach customers: anywhere from 5,000 to 300,000 records each, roughly 150 exports a month — maybe 160, I'd need to check the exact system count. In 2025 I rejected just over 11% of first-pass deliveries. The records usually didn't bounce. They failed because something couldn't be substantiated: a verification label with no method behind it, enrichment with no source timestamp, an intent score with no underlying signal.

I'm sharing this for a practical reason. If a RevOps team evaluates our platform with the same rigor I apply internally, the conversation gets better. This six-gate checklist is what I'd run on any shortlisted vendor — including us.

1. Separate “verified” from “guessable”

The first thing I audit in a prospect database is how an email address earned the label “verified.” Not whether the label exists. The method behind it.

Ask any B2B contact data vendor “are your emails verified?” and nearly all will say yes. The question that exposes the difference: what does “verified” mean in your data schema? At okkigo, we don't allow a single verified flag. Records carry method-level status: syntax, domain/MX check, SMTP handshake, catch-all detection, or a seed-based deliverability signal. Why does this matter? Because a catch-all address will accept an SMTP handshake and then bounce days later or silently land in spam. A platform that counts catch-all as verified isn't lying, exactly, but it's handing you confidence you can't act on.

Before joining okkigo, I worked on the buying side. We accepted a batch of 8,000 “SMTP verified” contacts, launched a sequence, and watched about 9% hard-bounce. The vendor stood by their methodology — the mail servers had accepted the messages. The data was technically verified and commercially useless. That mistake cost us around $14,000 and the domain reputation took weeks to recover.

Check three things in your review: the record-level status codes, the date of last verification, and the share of records labeled catch-all or unverified. A platform that discloses those weaknesses is more trustworthy than one that presents an unbroken green field.

2. Ask where the enrichment actually came from

I'm not a data engineer, so I can't speak to the mathematics of merging graph databases or probabilistic matching. What I can speak to, from a quality perspective, is simpler: an enriched contact is only enriched if you can check the source at the moment you consume it.

“Enriched” should not be a binary field in an export. When we review enrichment output, we look for two attributes per data point: source category and collection date. A title like “Head of Sales” is only useful if you know whether it was collected three months ago from a company website or fourteen months ago from a compiled firmographic file. Put another way: 82% enrichment coverage sounds impressive until you realize 60% of those records have no freshness timestamp.

Waterfall enrichment, when it works, is a hypothesis: you query multiple sources in order of reliability rather than whoever answers first, and you keep the version with the strongest evidence. The practical evaluation question for RevOps: does the platform expose source metadata per contact, or does it hand you a cleaned, unverifiable summary? The second option looks better in a demo. It is worse in production.

3. Make intent a signal you can interrogate

Every platform now sells intent. The difference is between intent you can query and intent you can only admire. A red “high intent” badge on a dashboard is decoration if your SDR team can't answer what triggered it, or when.

We audit intent by asking: is the trigger stored as a timestamped field, or is it an opaque score? Can I filter for accounts where the VP of Sales changed roles in the last 30 days? Can I build a list of accounts that visited pricing pages in the last week, or is that action buried in an aggregated score? When the underlying trigger events are searchable — job changes, funding announcements, product adoption activity — generating leads from intent becomes a repeatable workflow instead of a hope.

The question isn't “does this platform have intent data?” Every platform says yes. The real question is whether your operations team can pull the exact signal they need, for the accounts they care about, without a custom research project. If not, the intent is decorative.

4. Test the handoff into a real outbound workflow

Data sitting in a prospect database doesn't create pipeline. The handoff does — the point where verified, enriched records become sequences, tasks, and CRM records. Most evaluations skip this because it's invisible on a features page.

Ask to see a live export mapped to the fields your sequences actually use. Test for duplicates against your existing database and suppression list. If the platform hands you forty thousand rows and your ops team spends two weeks deduplicating, that cost belongs on the quote.

At okkigo, we route everything through human-in-the-loop review: the agent builds the list, and a person approves it before outreach starts. That isn't just a philosophical preference; it's a quality gate. But you don't need our specific design. You need to know how many steps stand between “lead generated” and “sequence launched” — and whether those steps are your ops team's unpaid overtime or the vendor's responsibility.

5. Compare architecture, not just features — the “okki go vs clay” question

If your shortlist includes okki-go for RevOps, the comparison you'll see most often is “okki go vs clay.” Most of those posts argue about templates, integrations, and per-record pricing. They miss what actually determines whether a tool succeeds in your organization: where the work happens.

Clay is a legitimate, powerful orchestration layer. For teams with dedicated RevOps automation capacity who want to assemble their own data workflows, spreadsheet-native orchestration is a real advantage. It gives you flexibility, but it also gives you maintenance. Every enrichment source you add, every change to a sequence trigger, every new ICP requirement — someone has to build and maintain that workflow.

Okki-go approaches the same job differently. The agent is native to the prospecting workflow: it researches the account, runs the enrichment waterfall, verifies the contacts, checks the intent context, and returns a recommended prospect list for human approval. Your RevOps team sets the criteria and judges the output. Put another way: Clay gives builders a workshop, and okki-go gives RevOps teams an agent that does the construction.

Neither architecture is universally right. The honest evaluation question is whether your team has the capacity to operate complex workflows, or whether they'd rather set standards and review output. If the platform's architecture doesn't match what you can sustain, no feature checklist will save you.

6. Audit compliance controls as quality controls

This is the gate almost every revenue operations team forgets, because it feels like a legal question. I'm not a lawyer, and I won't pretend to be one. From a quality perspective, though, a vendor's compliance posture tells you a lot about how they think about your domain reputation.

For email outreach in the U.S., the FTC's CAN-SPAM guidance (ftc.gov) sets the baseline: truthful subject lines, a valid physical postal address, and a working opt-out mechanism. And per the FTC's advertising guidance, claims have to be truthful, not misleading, and substantiated with evidence. When a platform says their data is “99% accurate,” ask for the methodology report that proves it. In practice, I also check source disclosures, privacy policy commitments, and how quickly a suppression request propagates through connected integrations. If opt-outs only apply inside the vendor's database but not in the sequence tools it connects to, the export carries a compliance risk the moment it leaves their system.

When a vendor says “we'll share compliance documentation after you sign,” treat that as a data quality warning. It's like a supplier asking you to approve specifications after the goods have already shipped.

Three mistakes that make this whole checklist fail

First: buying on price per record instead of total cost. From my experience reviewing quality across thousands of exports, the lowest price per contact is the most expensive option once you count hard bounces, deduplication hours, and lost sender reputation. I once tested a candidate platform because it was 40% cheaper per record. The dashboard said its database was 94% net-new. My gut said that number was too convenient. We ran a 50-record overlap test, and 38% of their “new” prospects already existed in our database. The effective price of net-new records was closer to a 60% premium, not a 40% discount.

Second: skipping a 30-account acceptance test. Demos run on curated data; your reality won't. Ask the vendor to generate 30 records for your actual ICP, then inspect every one. Check what “verified” meant on each record. Look up an enrichment source. Run the records against your CRM and suppression list. Half a day of this tells you more than a month of sales calls.

Third: treating data quality as a one-time decision instead of an ongoing audit. Databases age, verification methods change, and enrichment sources degrade. Whatever platform you choose, re-test a random sample quarterly. The strongest contract you can negotiate is one where quality expectations are written down and measured.

One more note: everything here reflects what I've seen through my audits as of Q1 2026. The data provider market shifts quickly, so verify current capabilities against a vendor's latest documentation before you commit. What gets through your checklist determines what your SDRs spend their mornings doing — talking to prospects or cleaning up after bad data.