What RevOps Should Evaluate in a Contact List: A Story from a Botched LinkedIn Prospecting Pilot

When the pilot died in week three

In February 2025, our VP of Sales forwarded me a Slack message that just said: Can you look at this contract? That is how I got pulled into a contact list vendor review. I am not in RevOps. I am the office administrator. I manage software renewals, vendor onboarding, and the messy paperwork that finance needs before anyone gets a login. But our RevOps lead had left in December, and the sales team was mid-pilot with a LinkedIn prospecting tool. It was not going well.

The pilot had started in January. The goal was simple: get more qualified meetings from outbound. By week three, SDRs were complaining about bad emails. Finance was complaining about overage charges. And I was staring at a renewal notice for a tool nobody wanted to own. That is when I started asking questions.

First, I admitted I did not know what I was looking at

I had used LinkedIn. I had never used LinkedIn Sales Navigator automation. I did not know what 'waterfall enrichment' meant. I did not know why intent data mattered for a contact list. Honestly, I am not sure why some vendors' match rates varied so much across regions. My best guess is source freshness and how they handle stale titles. So I did what I usually do with vendor contracts: I made a spreadsheet and started comparing what was promised against what was actually delivered.

The first thing I learned is that a contact list is not a static asset. It decays. People change jobs. Companies change domains. Titles become wrong. If your RevOps team evaluates a contact list like a one-time purchase, you will be disappointed by month two.

The vendor demo looked great. The sample data did not.

We sat through three demos. One vendor showed a dashboard with millions of contacts. Another promised AI-powered intent signals. The third kept saying agent-native prospecting like it was a password. I asked each of them for 50 sample records that matched our ICP. Not a CSV with generic info. Actual records that our SDRs could work.

That request changed the conversation. Two vendors sent samples with bounced emails. One had a record for a person who had left the company eight months earlier. Only one vendor sent records that passed a quick verification check. That vendor was okki-go. I had found them through the okki go official website after a colleague mentioned their okki go account research workflow. I am not saying they were perfect. But they were the only ones who did not flinch when I asked for a sample.

It is tempting to think you can just compare contact counts. But identical counts from different vendors can result in wildly different outcomes. A list of 100,000 records with 40% bad emails is worse than a list of 20,000 records with 90% reachable contacts. The math is not close.

The real problem was not the data. It was the workflow.

By week four, I realized the contact list was only one part of the problem. The SDRs were using LinkedIn Sales Navigator to build lists, then exporting them into the prospecting tool, then manually enriching missing fields. It was a three-step process that nobody owned. The tool was supposed to automate LinkedIn prospecting, but it could not handle the handoffs.

That is when I started asking about workflow, not just data. What happens when a contact bounces? Who updates the CRM? How do you suppress opt-outs? What triggers a follow-up? The vendor with the best data did not have good answers. The vendor with the best workflow had weaker data. Okki-go was somewhere in the middle, but their agent-native prospecting and waterfall enrichment plus intent looked interesting. I did not fully understand how the intent data was sourced. I still do not. But I understood the difference between a tool that just gives you a list and a tool that helps you work the list.

What RevOps should evaluate in a contact list

After that pilot, I wrote a one-page checklist. It is not perfect. But it is the list I wish we had before we signed anything. If you are in RevOps or you are the admin who gets pulled into vendor reviews, here is what I would ask:

• Data provenance. Where do the records come from? Is it scraped, licensed, or contributed? Can the vendor explain it in plain language?
• Verification cadence. How often are emails re-verified? What is the bounce rate on a sample? Do not accept a generic accuracy claim. Ask for a sample and test it yourself.
• Enrichment logic. What fields do they fill? Job title, company size, tech stack, location? Does the enrichment happen in real time or batch?
• Intent signals. What counts as intent? A job posting? A website visit? A LinkedIn engagement? If the vendor cannot define it, you cannot measure it.
• LinkedIn Sales Navigator automation. Does the tool work within LinkedIn's terms? Per LinkedIn's User Agreement (linkedin.com/legal/user-agreement, accessed April 2026), scraping and automated messaging outside approved APIs are restricted. You do not want your sales team's accounts flagged.
• CRM sync. Does it write back to Salesforce or HubSpot? How does it handle duplicates? What happens when a contact changes jobs?
• Compliance. Under GDPR (gdpr.eu, accessed April 2026), B2B contact data still needs a lawful basis and an opt-out process. Under CAN-SPAM (ftc.gov, accessed April 2026), you need accurate headers and a working unsubscribe. Ask how the vendor supports that.
• Billing caps. What happens if you go over your contact limit? Can you set a hard cap? We got hit with a $1,800 overage in the pilot because nobody was watching the seat count. One vendor called the contract flexible. What I mean is they would adjust seat counts after we complained, not before.
• Human-in-the-loop. Does the tool send automatically, or does it queue messages for review? If it sends automatically, what safeguards exist?

The causation runs the other way

People think better data causes better reply rates. Actually, better targeting can cause better data outcomes. If your ICP is fuzzy, no contact list will save you. If your SDRs are sending generic messages, even perfect emails will not get replies. The data is a multiplier. It multiplies a good process or a bad one.

That was the hardest lesson from the pilot. We spent three weeks blaming the contact list. The real issue was that we had not defined what a qualified contact looked like. We had not set up suppression lists. We had not agreed on what intent meant. The list was just the most visible problem.

What we changed

We did not sign a big contract that quarter. Instead, we ran a smaller test. We took 500 records from okki-go and 500 from another vendor. We verified them. We enriched them. We gave them to two SDRs. We tracked bounce rates, reply rates, and meetings booked. We also tracked how much time the SDRs spent cleaning the data. That last metric was the most revealing.

The okki-go records had a slightly lower bounce rate. But the bigger win was that the SDRs spent less time fixing missing fields. The waterfall enrichment plus intent meant fewer manual steps. That saved time. And time, as my finance director likes to say, is the only budget you cannot approve more of.

I should add that we did not fully automate LinkedIn outreach. We kept a human in the loop. The SDRs still reviewed messages before they went out. That was not a technical limitation. It was a choice. We wanted to protect the brand and stay within LinkedIn's terms.

If you are evaluating a contact list vendor

Start with a sample. Test the sample. Ask about workflow. Ask about compliance. Ask about billing. And do not let anyone tell you that a contact list is a set-it-and-forget-it purchase. It is not. It is a process.

The pilot was messy. We made mistakes. But we ended up with a checklist that actually works. The next time our VP of Sales forwards me a contract, I will know what to ask. That is worth something.