What Revenue Operations Teams Should Evaluate in B2B Contact Data Solutions: A 7-Step Checklist

Who This Checklist Is For

If your revenue operations team is evaluating B2B contact data solutions—whether for the first time or as a replacement for something that's stopped working—this is for you. It's especially relevant if you're layering AI SDR tools or an agent-native prospecting platform like okki-go on top of your existing GTM stack.

I'm the operations purchaser for a 55-person B2B services firm. I handle roughly $32,000 a year in sales tooling across about 10 vendors. I report to both revenue operations and finance, which means I get it from both sides when a tool underdelivers.

I don't have hard data on how many RevOps teams regret their contact data purchases, but based on two full stack migrations and a few partial ones since 2021, my sense is that most of the pain comes from skipping evaluation steps that feel obvious in hindsight.

This checklist has 7 steps. Work through them in order, and give yourself at least a week before signing anything. Here's what actually matters.

Step 1: Define the Actual Problem Before You Look at Vendors

Most contact data evaluations start because someone said "we need better data." That's not a problem statement—that's a symptom.

Before you take a single demo, write down the specific failure you're trying to fix. Examples that are actually useful:

• "38% of our outbound emails bounce in the first week."
• "Our SDRs spend 6 hours a week manually enriching records in the CRM."
• "We can't prioritize accounts because we don't have intent signals."
• "Our LinkedIn outreach sequences aren't syncing back to the CRM, so attribution is broken."

If you can't write it as a specific, measurable failure, you're not ready to evaluate vendors. You'll end up buying features you don't need because they demo well.

Here's something vendors won't tell you: the first demo call is designed to make you feel like you have every problem their product solves. It's not a neutral assessment. Go in with your list, and stick to it.

Step 2: Test Email Verification Accuracy With Your Own Leads

Every vendor claims high accuracy. What most people don't realize is that "accuracy" numbers are usually measured against their own curated lists—not the messy, real-world data your team is actually working with.

Ask for a live test. Give them a sample of 500–1,000 contacts from your own CRM, including ones you know are bad. Then check:

• False positives: Does it pass emails that bounce immediately?
• False negatives: Does it flag good emails as invalid?
• Catch-all handling: How does it classify ambiguous domains? (This is where most vendors quietly fail.)

I still kick myself for not doing this on our first vendor. We signed a 12-month contract, and three months in, our SDRs were manually checking catch-all domains because the tool flagged all of them as "risky." That vendor no longer exists, but the wasted budget and the mess in our CRM are still things I hear about.

Waterfall enrichment models—where multiple data sources are queried and results are cross-referenced—tend to perform better on catch-all domains than single-source tools. But you have to test that yourself. Don't take the sales deck's word for it.

Step 3: Check CRM Enrichment Against Real Records, Not Sample Data

CRM enrichment is where deals get made or broken. A tool that looks great on a clean demo dataset can fall apart when you point it at your actual pipeline.

For this step, export 200–300 real records from your CRM—closed-won accounts are ideal because you know what the correct fields should be. Then evaluate:

• Field coverage: Does it fill in job titles, company size, technology stack, and location accurately?
• Overwrite behavior: Will it replace existing values, or only fill blanks? This matters enormously if your team has manually corrected records.
• Sync frequency: Is it a one-time enrichment or an ongoing sync? Job changes happen constantly.
• Cost per record: Ask about pricing on a per-enrichment basis—not just a flat seat fee. These numbers can surprise you mid-quarter.

I have mixed feelings about real-time enrichment versus batch. On one hand, real-time keeps records fresh without anyone thinking about it. On the other, batch gives you a predictable cost structure and a chance to review changes before they hit the pipeline. We ended up with a hybrid—batch weekly, plus real-time for high-priority accounts.

Step 4: Verify Where Intent Data Comes From and How Fresh It Is

Intent data is the trickiest category in this whole evaluation, because it's hard to verify independently. A lot of what's sold as "intent signals" is repackaged web traffic or content download logs—useful, but not the same thing as active buying intent.

Ask these questions directly, and don't accept vague answers:

• What are the primary sources of intent data? (Third-party co-ops, first-party pixel data, content syndication networks?)
• How long is the signal considered "active"? (7 days? 30? 90?)
• Can you see the specific topics or keywords that triggered the signal?
• How often is the data refreshed?

If a vendor can't tell you where their intent data comes from, that's your answer. Move on.

One more thing: intent data works best when it's paired with enrichment data in a single workflow. That's one of the arguments for an integrated platform like okki-go rather than stitching together three separate tools. But integration isn't automatically better—it depends on whether the integrated tool does each piece well. Test the pieces separately, then test them together.

Step 5: Trial LinkedIn Automation on a Sequence You Already Run

LinkedIn automation is where teams get excited and where things go wrong quietly. A tool that sends connection requests flawlessly at scale can still wreck your account if it doesn't handle throttling, personalization, or message variation well.

Before you buy, run a 2-week trial on a sequence you're already executing manually. Compare:

• Reply rate: Does automated outreach perform comparably to human-sent messages?
• Account safety: Did your LinkedIn account get flagged, restricted, or throttled?
• CRM logging: Do activities sync back automatically, or does someone have to copy-paste?
• Personalization quality: Can you inject custom fields without making the message sound like a mail merge?

To be fair, some account restrictions are LinkedIn's doing, not the tool's—their detection algorithms have gotten aggressive since 2024. But a good vendor will have built safeguards around that. Ask what those safeguards actually are.

Step 6: Evaluate Whether the Tool Fits an Agent-Native Workflow

This is the step most teams haven't even thought about yet, and it's the one that will matter most over the next 12–18 months.

Traditional prospecting tools were built for humans clicking buttons. You upload a list, you launch a sequence, you check results. Agent-native platforms—okki-go is the example I'm most familiar with—are built with AI agents as the primary operator. The workflow is different: agents qualify, enrich, sequence, and follow up, with humans reviewing exceptions.

Here's what to check:

• API-first architecture: Can agents interact with the tool without a UI? If not, it's not really agent-native.
• Human-in-the-loop checkpoints: Can you define where a human must approve before the agent proceeds?
• Observability: Can you see what the agent did and why? This matters for compliance and for trust.
• Fallback handling: What happens when the agent encounters something it can't handle?

I'll be honest—I learned these criteria in early 2025, and the landscape is evolving fast. Verify current capabilities directly with vendors, because what shipped six months ago may already be outdated.

Step 7: Audit Compliance and Data Handling Before You Sign

This is the least exciting step and the one most likely to cause real problems if skipped.

Under GDPR, you need a legal basis for processing personal data—usually "legitimate interest" for B2B outreach, but that's not automatic. It requires a documented assessment. Under CAN-SPAM, you need clear opt-out mechanisms and accurate header information. These aren't vendor problems—they're your problems, but your vendor needs to help you stay compliant.

Ask for:

• Data processing agreement (DPA) ready to sign
• Where data is stored and processed geographically
• What happens to your data if you leave the platform
• Retention and deletion policies
• Any relevant certifications (SOC 2, ISO 27001)

If a vendor is evasive about any of these, that's a signal. Plenty of tools in this space are solid on the sales side and thin on compliance. Don't find that out after you've uploaded 50,000 contacts.

Notes and Common Mistakes

A few things I've seen go wrong repeatedly:

Over-buying seats. Most teams buy seats for everyone who might use the tool. Buy for the people who will use it daily. Expansion is easier than contraction.

Skipping the parallel trial. If you're replacing a tool, run both for at least two weeks. Side-by-side comparison catches things single-vendor demos never will.

Ignoring total cost. Base license, enrichment credits, intent data add-ons, API overage fees, implementation costs—these add up. Ask for a full 12-month cost projection in writing.

Buying for today's process. If your team is moving toward agent-native prospecting (and most are heading that direction), evaluate tools that can support that shift—not just the workflow you're running right now.

Granted, this checklist takes more work upfront than a single demo call. But every step here exists because skipping it cost us something real—money, time, or credibility with the sales team. The evaluation process is where you protect all three.