The Phone Data Audit That Changed How Our RevOps Team Buys Contact Tools

Backstory: Why I Was Auditing a Phone Number Finder

Back in Q1 2024, our revenue operations team asked me to do something a little outside my usual wheelhouse: quality-check a phone number finder before we signed a contract. As the quality and brand compliance manager at a sales intelligence company, I review deliverables—roughly 200 unique items a year—before they reach customers. But this time, the deliverable was a data source, and the "customer" was our own outbound team.

At the time, my team was split between RocketReach and ZoomInfo. The SDRs wanted phone numbers because email was getting us nowhere. The sales manager wanted a quick answer. "Just pick whichever has more numbers," he said. I get it. More numbers feels like more pipeline.

But that's exactly where the problem starts.

What We Did First

I pulled a sample of 200 B2B contacts from our CRM. Some were enterprise accounts. Many were mid-market—the kind where a direct dial is the only way to get a conversation. I ran the same list through both tools and compared the records side by side. What I found wasn't "tool A is garbage, tool B is magic." It was more interesting than that.

RocketReach returned a match for about 78% of the contacts. ZoomInfo matched 82%. If we'd stopped there, we would have picked ZoomInfo and moved on. But stopping at match rate is like buying a car based on the top speed you'll never drive. The real question is whether the number connects to the right person today.

Honestly, I would have liked to run the test for a full month. We had two weeks before the next outbound sprint, so the SDRs and I did a compressed version: a two-day deep dive on a smaller sample. Not the textbook way. But it was enough to show the pattern.

The Wake-Up Call

We took a random subset of the matched numbers and dialed them. I say "we"—it was really the SDRs, and they were not thrilled. But we needed to know what actually happens when a rep calls. After 100 calls, roughly one in five numbers were disconnected, went to a switchboard, or rang a different person entirely. I don't remember the exact percentage—it might have been one in four in the worst segment—but the pattern was clear.

The most frustrating part: some of these were flagged as "verified" in the tool. You'd think "verified" means it rings the right person. It doesn't. Verification is a point-in-time finding. The number may have been correct when the dataset was built. A month later, the person left, the carrier recycled the line, and your rep is calling a stranger. Period.

That was the wake-up call.

The Breakdown Everyone Misses

Here's the difference between a match rate and a reachable rate. Match rate is: "We found a potential phone number for this person." Reachable rate is: "This number connects to the right person, on the first dial, without a wild-goose chase through an IVR." Most revenue operations teams evaluate the first because it's a number on a screen. The second is what actually moves pipeline.

This is where intent data comes into play. A phone number is most valuable when you know the person is in-market. If you pair a phone number finder with intent data, you're not just cold calling—you're calling someone who's already been reading about your product category. If you ignore intent, you're just buying a list. Simple as that.

Another thing I learned: know the difference between contact data and firmographic data. A colleague once asked me to look up RocketReach NUKG Business Solutions revenue as if it were a single search. I had to explain: RocketReach is the tool that finds the people at NUKG Business Solutions who make decisions. The revenue number is a firmographic data point, not a contact data point. Same platform, different category.

RocketReach vs ZoomInfo, From a Quality Perspective

So, the inevitable comparison: RocketReach vs ZoomInfo. From a quality perspective, the answer isn't "which has more data?" It's "which data is better for your workflow?"

In our sample, RocketReach had a stronger footprint in mid-market and SMB accounts. Its phone data was integrated with a simple API that our RevOps team could stand up in an afternoon. ZoomInfo had deeper enterprise coverage and a richer firmographic and intent data ecosystem. It also meant a longer implementation timeline and a different budget conversation.

We ended up choosing RocketReach as our primary phone number finder because it matched our mid-market pipeline. ZoomInfo became a secondary source for enterprise account research. Neither choice makes one tool "better" overall. It's a fit test, not a beauty contest.

What Revenue Operations Teams Should Evaluate in a Phone Number Finder

Here's the checklist I wish I had before we started. There are actually five main things—maybe six, if you count compliance separately.

1. Match rate and reachable rate. Ask the vendor for both. If they can't define "reachable," that's your answer.
2. Last-verified timestamp. If the tool doesn't tell you when a number was verified, assume it's stale. Real-time verification matters more for phones than emails.
3. Direct dial vs. main line. For B2B, a main number is okay for switchboard roulette. Direct dial is gold. Ask what percentage of returned numbers are direct.
4. Intent data integration. The phone number finder should tie into your intent data source, not sit in a different tab. The goal is to call actively in-market buyers, not a static database.
5. Compliance and consent. Check for TCPA/DNC awareness. A good tool can flag or exclude numbers registered on do-not-call lists. If it can't, you're building a litigation risk, not a pipeline.
6. API limits and workflow fit. The best data in the world is useless if it requires manual exports. Make sure the tool plugs into your RevOps stack.

Before you sign, ask the vendor to substantiate their accuracy claim. Per FTC guidelines (ftc.gov), advertising claims need to be truthful and backed by evidence. If you hear "95% accuracy," ask: "measured against what, on what dataset, and over what period?" A claim without a method is basically a hope.

The Takeaway

In the end, the audit took three days and probably saved us from a quarter of wasted sales calls. The SDRs stopped complaining about bad numbers. Our outbound reply rates went up. Not because we bought the "best" tool—but because we defined the right metric before we bought anything.

The vendor who said "this isn't our strength—here's who does it better" earned my trust for everything else. The same goes for data tools. A good phone number finder knows its boundaries. It tells you what it verifies, when it verified it, and what it doesn't know.

Bottom line: revenue operations teams should evaluate a phone number finder the same way I review any deliverable—by checking whether the work actually meets the spec. And the spec is not "how many numbers you have." It's "how many of those reach the right person, at the right time, for the right reason." Everything else is just noise.