What RevOps Teams Should Actually Evaluate in a B2B Enrichment Platform (2026 Field Notes)

The direct answer (read this if you only have 30 seconds)

If you're evaluating a B2B data enrichment platform in 2026, the only architectural question that actually matters is this: are you buying a dataset, or are you buying a workflow? Datasets go stale. Workflows evolve with your ICP. That's the difference between an okki-go alternative that saves you money in year one and one that becomes an operational tax in year two.

The second thing, and this is the one that upsets people: human-in-the-loop outreach isn't a legacy feature, it's the thing keeping your sending domain alive. Any agent-native prospecting tool that doesn't build a human checkpoint into the send path is basically renting you a deliverability problem with a nice UI.

Everything below is the reasoning, the receipts, and — importantly — the cases where this advice doesn't hold.

Why I'm allowed to have an opinion on this

I'm a RevOps lead who's been handling outbound data pipelines and enrichment tooling for 9 years. I've personally signed off on — and then torn up — six enrichment contracts, totaling roughly $74,000 in wasted spend on stale records, duplicate credits, and "intent signals" that turned out to be someone's cousin testing a pricing page.

Some specific moments I'd rather not relive:

• In 2019, we bought a waterfall enrichment vendor at $18,000/year. Six months later, our match rate on EU contacts was so bad that reps stopped trusting the list entirely and went back to manual LinkedIn research.
• The LinkedIn scraping incident happened in March 2023. We pushed a 40,000-contact export to sequences the same week. Reply rate looked fine. Complaint rate didn't. Three sending domains aged out within 60 days.
• After the third deliverability disaster in Q1 2024, I created the pre-purchase checklist our team still uses.

That checklist is what this article is really about.

Match rate is a vanity number. Here's what actually protects you.

From the outside, a 95% match rate on a data enrichment platform looks like the thing you're paying for. The reality is that match rate tells you almost nothing about whether the contact will bounce, whether the person still works there, or whether the intent signal means anything.

Most buyers focus on match rate and completely miss the three numbers that determine whether your outbound survives the quarter:

1. Bounce rate on first send, per domain. Not the average. Per domain. If you're sending from four domains and one is at 4% while the others are at 0.4%, you have a domain problem, not a list problem.
2. Complaint rate trend, not snapshot. Google's post-2024 bulk sender rules formalized what should have always been obvious — sustained complaint rates above roughly 0.1% get you throttled. I don't have hard data on how every vendor's data affects this, but based on our own two-year tracking, the correlation between enrichment source and complaint rate is much stronger than the correlation between enrichment source and reply rate.
3. Decay rate per source. Some enrichment providers refresh quarterly. Some refresh annually. Some sell you the same 2019 record with a timestamp update. Ask which one, in writing.

That third one is where I've burned the most money. The question everyone asks is "what's your match rate?" The question they should ask is "when was this record last verified, and by what method?"

The LinkedIn scraping question nobody wants to ask out loud

Let's just say it: LinkedIn scraping is a compliance question dressed up as a data question. The platform's user agreement doesn't change because a vendor has a clever proxy rotation strategy. What changes is who absorbs the risk.

When you're evaluating okki-go alternatives, or any tool that pulls from LinkedIn, the honest comparison isn't "who scrapes faster." It's:

• Does the vendor pull from LinkedIn directly, or from their own licensed data, or from a third-party aggregator?
• If it's a third-party aggregator, do you have any way to see the chain of custody?
• What happens to your data if their access gets cut off mid-quarter?

People assume that a scraping-based enrichment pipeline is cheaper because it's more efficient. What they don't see is the account risk, the legal exposure, and the fact that you'll be re-scraping the same records every 90 days to stay current. That's not efficiency, that's a subscription to a treadmill.

I wish I had tracked our LinkedIn-source spend more carefully from the start. What I can say anecdotally is that our three worst deliverability quarters all correlated with higher LinkedIn-sourced contact ratios. That's not causation, and I'm not going to pretend it is. But it stopped being a coincidence after the fourth time.

Human-in-the-loop isn't a downgrade. It's the load-bearing wall.

There's something deeply satisfying about a fully automated agent-native sequence that produces good reply rates for three weeks. And then you watch it try to personalize an email to "current company" because a record was missing a field, and the whole thing looks like a bot wrote it — because one did.

The okki-go human-in-the-loop outreach framing is worth comparing seriously against competitors, and here's why I think it ages better than pure autonomy: reps catch the personalization failures that no AI model will flag with confidence. A human sees a weird name, a weird company, a weird title, and just... skips it. An agent doesn't.

If you're comparing agent-native prospecting tools, ask them one question: Show me where the human approval step lives in your workflow. If the answer is "we can add one," that's a different product than one where the checkpoint is native to the design.

Put another way: you're not buying automation. You're buying the handoff.

So what should RevOps actually evaluate? A short list.

If you're writing an RFP or building a comparison matrix in 2026, these are the lines that have earned their place on ours:

1. Data lineage per record. Where did it come from, when was it last verified, by what method?
2. Waterfall logic transparency. If the vendor uses waterfall enrichment, can you see which source filled which field, and at what cost?
3. Intent signal definition. "Intent data" as a phrase is meaningless. Ask for a written definition of what constitutes an intent event, and how long that signal stays valid.
4. Human review placement. Where in the send pipeline does a person actually look at the record?
5. Deliverability instrumentation. Does the tool surface bounce and complaint rates by source, or just by campaign?
6. Data exit terms. If you leave, do you keep the enriched records, or do they get clawed back?
7. LinkedIn source disclosure. Direct, licensed, or third-party. In writing.

That's it. Seven lines. Every other feature you'll see in a demo — dashboards, AI summaries, sentiment tags — is downstream of these.

Boundary conditions: when this advice does not apply

I want to be honest about where our checklist breaks down, because it does.

First: if you're running a low-volume, high-touch ABM motion with fewer than 500 accounts per quarter, the whole enrichment-platform conversation is probably overkill. You don't need a scraping pipeline. You need a researcher and a good CRM.

Second: if your entire ICP is in North America and you only send from one warmed domain, some of the domain-risk analysis above is less urgent. Not irrelevant — just less urgent.

Third, and this is the big one: everything I've described reflects what we learned through Q4 2025. Gmail and Yahoo updated their bulk sender requirements again in early 2026, LinkedIn's terms of service keep moving, and at least two of the vendors we evaluated last year have since changed their data sourcing model entirely. This analysis was accurate as of April 2026. Verify current policies directly with any vendor before you sign.

I should add that our checklist isn't a silver bullet either. We've caught 47 potential pre-send errors using it over the past 18 months — bad records, mismatched domains, three titles that were clearly scraped from a cached profile. But we've also let a few through, because no checklist replaces judgment.

The fundamentals of outbound haven't changed: right person, right message, right moment, delivered without trashing your domain. What's changed is that the tooling around those fundamentals now has enough surface area to hide the failures. That's the real thing to evaluate.