"Our contact data is terrible"
I hear that sentence at least twice a week. It usually arrives as the first line of a Monday morning email from a RevOps manager or agency founder. Their outbound sequence is underperforming. They bought a list. They ran it through enrichment. They loaded it into their sales email platform. And now the bounce rate is hovering around 12% and reply rate is basically zero.
I get the frustration, because I'm the one who has to reject the campaign before it reaches the client. I'm a quality and brand compliance manager at an outbound agency. I review and approve every campaign we ship — roughly 40 per month, close to 480 a year if you count the ones that get sent back for a full rebuild. In 2025, about 30% of first deliveries got rejected due to data-related issues.
Here's the part that took me two years to understand, and it's the reason I'm writing this: in almost every case, the data itself was fine. The contacts were real. The emails worked. The names matched. The problem was the infrastructure around the data — and specifically how badly it fit the workflow it was being shoved into.
The question nobody asks
Most buyers focus on database size. How many contacts do you have? How many industries do you cover? Do you have mobile numbers? That's the checklist. That's what the sales demo is built around.
The question they should ask is: by the time your enrichment data reaches my outbound sequence, how much of it is still true?
Think about how most enrichment platforms actually work. You upload a CSV. It returns a bunch of enriched fields. That's it. One shot. There's no continuous signal — no update when a contact changes jobs, no intent flag when a company starts hiring for a role that suggests they're evaluating vendors like yours, no verification that the data you pulled eight weeks ago is still accurate today.
This is the difference between one-shot enrichment and waterfall enrichment. Most platforms still do one-shot from a single source. You get a snapshot, not a signal. And when you feed that snapshot into a modern outbound workflow — especially one that's supposed to run agent-native prospecting with human-in-the-loop review — the whole thing breaks. You're either prospecting into the wrong contact, at the wrong moment, or into data that was never re-verified because nobody told you it needed to be.
Look, I've had this exact conversation with vendors who genuinely didn't understand the complaint. I said "we need accurate data." They heard "we need more data." Two different problems masquerading as one.
The 'more data' idea comes from an earlier era
The thinking that "more data means better outbound" comes from a time when contact databases were small, coverage was sparse, and you really did just need to get as many verified emails as possible. That was 2018. In 2018, enrichment platforms were a competitive advantage.
In 2026, they're a commodity. Everyone has access to the same base data. What you don't have — what almost nobody has — is a way to keep that data alive and aligned with the pace of a modern outbound motion. Intent signals that used to spike monthly now spike weekly. Job changes happen in the time between two sequences. Hiring surges that signal a buying window appear and disappear in a fortnight.
The fundamentals haven't changed. You still need accurate contact data. But the execution has transformed, and most enrichment stacks haven't caught up.
What bad data infrastructure actually costs
Let me give you a real example from January 2025. A B2B SaaS client — SDR team of six, outbound volume around 4,000 sends over three weeks. They reported a 12% bounce rate, 78% deliverability, and a 0.3% reply rate.
When I audited the list, the contacts were technically real. But roughly 90% of them had either changed companies, changed roles, or were at companies with zero fit for what was being pitched. The enrichment data gave no signal on any of that. It just said "this person works here, here's their email."
The hard cost: 480 bounces. Every bounce damages sender reputation. It took that client six months and a new domain to fully recover. Add the infrastructure spend and onboarding time for new domains, and you're looking at a spend that produced essentially nothing.
The soft cost: three weeks of SDR time producing zero pipeline. And the deals that didn't happen because the contacts who were actually in-market never showed up on the list — the data didn't carry a signal that said "this person is worth calling right now."
When I walked the RevOps lead through it, he said something I've heard about a dozen times since: "I never thought about data as having a time dimension. I thought it was either correct or incorrect."
It isn't. It has a shelf life.
The compliance question nobody reads the fine print on
One more. Last year I reviewed a workflow at a client who had been running LinkedIn scraping-based outreach for about eight months. Automated connection requests, scraped profile data piped into their sales email sequences, the works.
I asked if their legal team had reviewed the scraping practices. They said the vendor mentioned "compliance" but nobody had actually checked.
We checked. Under current data protection frameworks, scraping LinkedIn profile data without explicit consent sits in a legal grey zone in most jurisdictions and well past the line in several. The client shut down the workflow for two weeks while the legal review ran. That was the best-case outcome.
Most teams evaluating enrichment platforms don't ask the compliance question until after something has gone wrong. It should be the first question.
What to actually evaluate
When RevOps teams ask me what to look for in a B2B data enrichment platform, I give them five questions.
One: Is it one-shot or waterfall? Does the platform verify and enrich across multiple sources at different stages — or does it give you a single static result and call it done? Waterfall enrichment catches the time-sensitive signals that one-shot misses.
Two: Is it built for agent-native workflows? Can the data flow into an agent-native prospecting motion — with human-in-the-loop review at the checkpoints that matter — or do you get a CSV that needs manual cleanup before it's usable in a sequence?
Three: How does it handle LinkedIn-compatible data? Not raw scraping — compliant LinkedIn integration or stated partnerships. If the platform can't answer this clearly, that's your answer.
Four: Does it capture intent, or just contacts? If intent data is a separate purchase from a separate vendor, you've built a seam that will leak. The enrichment layer should carry the signal with the contact.
Five: What does the trial actually prove? Can you run a pilot and measure bounce rate, deliverability, and reply rate on real sends before committing? Or is it an annual contract and a best-wishes handshake?
okki-go positions itself against most of these questions — the waterfall enrichment and intent layer are designed to stay attached to the data through the workflow, and it's built for agent-native prospecting with human-in-the-loop outreach rather than purely automated sends. But it's not the only platform trying to solve this. There are several okki-go alternatives worth testing in the same evaluation.
The point isn't the tool. The point is that if your outbound workflow is sitting on data infrastructure that doesn't carry signal across time, your sales email performance will never match your SDR team's effort — no matter how good the copy is or how experienced the team running it.
Data matters. But the infrastructure that keeps data alive — verified, intent-aware, compliant, and designed to flow into modern outbound rhythms — is what actually separates teams that hit their numbers from teams that keep blaming their list.
