What RevOps Teams Should Evaluate in an Intent Data Platform: A 7-Step Checklist

Before You Start: Who This Checklist Is For

If you're a RevOps lead staring down a shortlist of intent data platforms and you need to pick one without burning a quarter, this is for you. I run data quality and vendor evaluation on the RevOps side at a B2B SaaS company—roughly 40-50 vendor deliverables cross my desk each quarter, and I reject about 18% of them before they ever reach a pilot. Most look fine in a demo. Most do not survive contact with our actual workflows.

This checklist has 7 steps. Budget an afternoon for steps 1-6, then a 30-day window for step 7. Skipping any step means you'll pay for it at renewal.

Step 1: Write Down the Decision You're Trying to Improve

This is the step most teams skip. A vendor demos a slick dashboard, signals flash, and everyone in the room decides they need it. But unless you write one sentence—"When signal X appears, we do Y"—you're buying entertainment, not infrastructure.

Concrete examples that actually work:

• "When a target account shows research intent + a LinkedIn engagement in the same week, the SDR calls the next business day."
• "When a competitor-research signal fires, the AE gets notified before the next renewal check-in."
• "When a target account goes quiet for 14 days after a demo request, we trigger a re-engagement sequence."

Without that sentence, every platform looks good. With it, you have a measuring stick.

Step 2: Ask for 50 Real Records — Not a Demo Account

Demo accounts are polished. The real test is handing over 50 target accounts you already know something about and seeing whether the platform surfaces what you know.

Here's a request script I use:

"Please return 50 records from our target account list with your enrichment and intent fields populated. Where you have no signal, mark it empty—don't infer."

That last clause matters. Platforms that fill every field with something vague look impressive until you start calling the wrong accounts.

Then reverse-check. Take each record against your CRM data, LinkedIn reality, and a handful of email spot checks. I don't have hard industry-wide data on false-positive rates—nobody publishes that cleanly—but based on reviewing maybe 600 vendor samples over the last four years, my sense is that roughly 1 in 3 platforms overclaims match rate by 10-20 percentage points when the sample is scored against ground truth. That's not a data point you'll find in a pitch deck.

Step 3: Test False Positive Rate, Not Match Rate

Every platform leads with match rate. It's a comfortable number. But match rate counts the contact you found, not whether the signal you attached to that contact is real. The number that matters is false positive rate—the share of records that get flagged as "high intent" when there's nothing actually happening.

How to test it in about two hours:

• Take 20 accounts the platform labels high-intent.
• Independently verify whether there's been any observable activity (site visits, LinkedIn engagement, email opens, job postings if relevant).
• If more than 4 of the 20 are ghosts, that's a problem at scale.

Also ask the platform to show you what a signal actually looks like in raw form. Vendors that mix "inferred" signals with "observed" signals in the same score are hiding something—usually that within their "observed" category, half the signals are educated guesses.

Step 4: Check Whether LinkedIn Prospecting Actually Connects

Most B2B teams already run something on LinkedIn. The question is whether the intent platform feeds that workflow or just sits next to it.

Ask specifically:

• Does LinkedIn engagement feed into the intent score, or is the score blind to it?
• Does activity in the platform push back to CRM with a timestamp and source, or does someone have to export a CSV?
• When an SDR works a high-intent account on LinkedIn, does the platform know, or does that interaction get lost?

Across eight vendors we shortlisted last summer, the difference was stark: two treated LinkedIn as a first-class signal source, three treated it as an export target, and the rest didn't address it. The operational friction of the CSV route cost us more time than any data quality gap.

To be fair, not every team works LinkedIn as a primary channel. If yours doesn't, deprioritize this step. If it does, it belongs in the first conversation.

Step 5: Read the Developer Integration Docs — Really Read Them

If you're RevOps, you probably won't write the code, but you'll live with the consequences of it. Spend 30 minutes on the public developer docs, or ask for a preview if they're gated behind login.

Take okki-go developer integration as a reference point, because it's one I've read end-to-end recently. A solid integration doc covers authentication, rate limits, webhook events, pagination, and—critically—the schema of a returned record. You don't need to understand all of it. You need to answer three questions:

• Ontology: Do the field names and value types match how your CRM models accounts and contacts? Or are you writing a translation layer?
• Volume and cost: What happens if you enrich 10,000 records a month versus 100,000? Is the pricing model per-call, per-matched-record, or per-seat?
• Failure modes: What does the okki-go API integration return when it can't find a match—null, a superset guess, or a 404? That difference determines whether your downstream pipeline breaks silently or loudly.

One more thing, and this is the one I've seen bite teams twice: who supports the integration when it breaks? A support ticket queue or a named contact? At a previous company, our enrichment API started returning stale emails after a vendor-side migration. Nobody noticed for 11 days because the failures were silent. By the time we caught it, we'd sent roughly 6,000 outbound emails with a stale domain. The vendor's support channel was a Zendesk form with a 72-hour SLA. That's a direct cost we didn't price in when we signed.

Step 6: Map Signals to Actions — Not Dashboards

This is where most intent data platforms quietly fail. Beautiful dashboards are cheap to build. Actionable signal-to-outreach paths are not.

Ask the vendor to walk you through, live, how a signal becomes outreach. Not the theoretical version—the actual triggered version. For example:

• Does a signal land in the SDR's queue with enough context to write a personalized opener, or just an account name?
• Can two signals fire in different channels (email + LinkedIn) and get treated as one coordinated outreach, or do they collide?
• When a signal fires at 11pm on a Friday, what happens? Real outbound teams know that timing matters more than most platforms admit.

If the platform can't show you the signal-to-action path in a product demo, it's a data product wearing an outbound costume. That's not necessarily bad—some teams only want the data—but price it and staff it accordingly.

Step 7: Run a 30-Day Structured Comparison

Don't sign an annual contract off a pilot. Segment two comparable pods (same ICP, same messaging, similar rep experience), give one the new platform and leave the other on your status quo, then track for 30 days.

Track these specifically:

• Meaningful reply rate — not raw reply rate. "Unsubscribe" and "wrong person" don't count.
• Meetings booked per 100 touches — this is the number that survives a QBR.
• Manual cleanup hours — ask the SDRs. Not the CSM. The people typing.
• False positive fallout — how often did a rep waste a call on a bad signal?

After 30 days, you'll have a real decision. Before that, you have a demo opinion.

Notes and Common Mistakes

A few things I wish we'd written into our internal wiki before the first vendor meeting:

• Don't decide on the dashboard. Decide on what lands in the outbound queue. Dashboards look great in reviews and convert to nothing.
• Be wary of "one platform does it all" claims. The vendors who say "this isn't our strength—here's who does it better" earn trust for everything else they claim. The ones who claim everything tend to underdeliver in the one area you actually need.
• Do not accept "100% email verification" or "guaranteed reply rate" as positioning. No platform can promise either. Ask for a sample and test it yourself.
• Clarify what "human-in-the-loop" means for each vendor. For some it means a human reviews edge cases. For others it means the customer is the human in the loop. Big difference.
• Pressure-test the API under load once. A clean sandbox call tells you nothing about what happens when 50,000 records hit a rate limit at 3am UTC.

Looking back, I should have asked for real data samples in our 2023 intent platform evaluation instead of trusting demo accounts and reference customers. At the time, the reference customers seemed credible and we were under pressure to pick before Q4. That decision cost us roughly four months and about $12,000 in rework when the enrichment layer turned out to be inferring more than observing. That's why this checklist exists.

Work it in order. The steps you're tempted to skip are usually the ones that decide whether the platform is still in your stack 12 months from now.