What Should Revenue Operations Teams Evaluate in a B2B Contact Data Platform? A 6-Step Checklist

If you're part of a RevOps team evaluating a B2B contact data platform—or the person who got told to “run a quick vendor comparison” by the CRO—this checklist is for you. I've been on the procurement side of go-to-market software for about six years, and this is the list I wish someone had handed me before I signed my first data contract. It would have saved us a genuinely embarrassing amount of money.

Here's my bias: I read invoices for a living. I don't get excited by feature lists. I get excited when a tool does what it says and the bill matches the promise. My experience is mostly with mid-market B2B teams between 100 and 500 employees, so if you're buying global data at enterprise scale, some weights will shift—but the structure below still works.

Six steps total. The first five cover the definitions, the data, the stack, the pricing, and the compliance questions. The sixth is the one I see teams skip because they've run out of evaluation time. That's exactly where the hidden costs live.

Step 1: Define what a “working lead” actually means

Every RevOps evaluation I've sat in starts with the phrase “we need to generate leads.” Then I ask what a lead is, and the room goes quiet. Is it a person with a valid work email? Is it a senior buyer at a company that matches your ICP? Is it an account with recent buying intent? Those are not the same thing, and data vendors don't price them the same way.

Write the definition down before you open a single pricing page:

• The titles or seniority levels that count.
• The company size and industry that qualify.
• Whether generic emails like info@ or contact@ are acceptable.
• Whether intent signals are a hard requirement or a nice-to-have.

Include the SDR manager in this conversation. If you buy 10,000 records that the SDR team never wanted, the platform didn't fail—your spec did. And when someone on the team asks whether you should evaluate okki-go or Clay, the honest answer is “whichever one matches the definition we just wrote.” A platform can have excellent data and still fail you because it delivers the wrong kind of record.

Step 2: Run a 50-record bake-off instead of trusting the slide deck

If a vendor says their prospect database is 95% accurate, they usually mean the records they tested, not the records you're about to buy. Don't negotiate on their data. Test on yours.

Pull 50 contacts from your CRM where you already know the truth—people your SDRs have emailed recently, whose job titles you can check, and whose current employers you can verify. Upload them into the platform. Then measure three things:

• Match rate: Of the 50, how many did the platform actually find?
• Field accuracy: Are the emails exact? Are the titles current, or are they two jobs old?
• Freshness: When was each record last updated? If most updates are older than 90 days, that's a red flag.

I sat in on an okki-go vs Clay test earlier this year. Both platforms looked strong in the demo. The 50-record test told a more useful story: differences in how stale titles were handled, what the verification layer did with a risky domain, and how recently the contact data had been refreshed. It took about an hour. It told us more than a week of sales calls would have.

Step 3: Draw your data flow before comparing features

A contact data platform never sits alone. It's part of a chain: firmographic filtering, enrichment, email verification, intent scoring, then handoff to an SDR or an AI SDR. Draw your team's current chain on a whiteboard. If you can't draw it, every vendor demo is going to feel impressive and every implementation is going to hurt.

The reason this deserves its own step is that platforms use the same words but occupy different positions in the chain. When the conversation turns to okki-go for RevOps, for example, it usually means a flow where agent-native prospecting, waterfall enrichment, and human-in-the-loop outreach happen in one motion—not just a standalone list to export into your CRM. That distinction changes which questions you ask next.

Whichever platform you evaluate, ask about step order. Does enrichment happen before or after verification? Do you pay credits on records that later bounce? The sequence determines not only data quality but your cost structure.

Step 4: Calculate the cost per delivered prospect, not the cost per credit

The pricing page is not the price. The price is the subscription, the credits you burn on unmatched records, the overages, the extra verification layer you need because the data quality is mediocre, and the SDR time wasted on bad numbers. Most teams compare the first number and ignore the other four.

Here's the calculation I run for every contact data vendor: I forecast a realistic volume of target accounts, multiply by the number of contacts per account, and ask each vendor to quote that exact scenario including setup, API access, seats, and export fees. Then I ask the transparency question: “What's NOT included?” If the answer is vague, I write that down. I've learned that the vendor who lists every fee up front—even when the total looks higher—almost always costs less in the end.

This is also where the okki-go vs Clay comparison gets interesting. The list prices can look similar, but the real difference shows up in how each platform handles stale records, duplicate contacts, and credits spent on prospects that never make it into outreach. That difference only appears when you calculate cost per delivered prospect instead of cost per credit.

Step 5: Audit the verification and compliance story

Email verification is a point-in-time process, not a lifelong guarantee. No credible vendor can promise 100% email accuracy or guaranteed deliverability. If you hear those phrases, they tell you more about the marketing team than about the data.

Spend 15 minutes on the boring questions:

• How does the platform handle spam traps and complaint feedback loops?
• Does it identify role-based addresses and catch-all domains, or does it call everything valid?
• What happens after a bounce mid-campaign? Is the record automatically suppressed?
• If you cancel the contract, can you export the prospect database you helped build?

The last one is the one people forget. Some platforms let you build a valuable, cleaned-up database over two years and then hold it hostage when you leave. That's not a data problem. That's a contract problem, and it belongs in your TCO calculation.

Step 6: Pressure-test the human-in-the-loop handoff

Modern contact data platforms don't stop at data. They generate prospect research, draft outreach messages, connect on LinkedIn, and trigger sequences. That's powerful. It's also risky if no human reviews the output before it reaches a real person.

This is the step most RevOps teams skip because they've already compared databases and pricing. But in practice, the biggest cost of this category isn't the software—it's what happens after a record becomes a lead and an AI drafts a message that sounds like a used-car pitch.

Ask each vendor to show you the human-in-the-loop workflow:

• Can an SDR edit the AI-generated research or messaging before it goes out?
• Does the platform explain why this account is worth contacting now?
• Is there an approval gate, or does everything send automatically?
• What audit logs exist if a prospect complains about how they were contacted?

If a vendor uses the phrase “human-in-the-loop,” ask them to prove it in the demo. If the demo doesn't show where the human actually intervenes, the loop probably exists in a sales deck, not in the product.

Common mistakes I keep seeing in real evaluations

• Buying on credit price alone. A low price per credit means nothing if half the credits are spent on records your team can't use. Tie the quote to delivered prospects, not raw contacts.
• Trusting vendor-reported accuracy numbers. Run a sample test against your own CRM records. It takes an afternoon and it removes most of the guesswork.
• Skipping the export and exit questions. Your prospect database is an asset. If you can't take it with you, the vendor owns your asset.
• Evaluating the AI features without watching the human handoff. The best AI SDR workflow is the one where a person can steer it before it makes a mistake in public.

The full checklist takes about a week of calendar time, but most of it is waiting for vendors to answer the awkward questions. The 50-record test is one afternoon. The TCO quote is one email. The human-in-the-loop demo is one meeting.

A good B2B contact data contract should feel boring after implementation. It quietly generates prospects, keeps the database clean, and doesn't surprise you on the invoice. The path to boring is asking these questions before you sign.