The 7-Step Okki Go Workflow I Use to QA Outbound Campaigns (Before Any Send Button Gets Pressed)

Who this Okki Go workflow is actually for

If you run outbound for a small agency or you're the RevOps person who has to sign off on every list before it goes live, this is for you. It's a 7-step pass I run on every campaign our team ships — roughly 40 campaigns a quarter across 12 client accounts. Okki Go is the workflow layer we sit on top of (list build, waterfall enrichment, LinkedIn email finding, verification, intent signals), and honestly, most of the value isn't in the tool itself. It's in the order you run things.

Fair warning: this assumes you already have an ICP written down somewhere, not just in your head. If you don't, fix that first. The rest of this checklist falls apart without it.

Step 1 — Lock the ICP before any list gets built

This sounds obvious. It's the step people skip most, and it's the one that poisons everything downstream.

Before anyone opens Okki Go, I want three things on one page: title ranges (with the seniority cutoff, not just the title), company size in headcount not revenue, and one disqualifier. That last one matters — if you can't name something that makes an account a hard no, your filters aren't real.

On the revenue ops side, this is also where I check whether the ABM segment actually matches. Most teams pitching "ABM" are really running broad outbound with a fancy name. Real account-based work means you've pre-decided the accounts. If your list builder is still casting a wide net, that's demand gen, not ABM, and the evaluation criteria are different.

Step 2 — Set up sending infrastructure like it's production

I'm not an email deliverability engineer — that's a specialty of its own, and I'd rather not fake it. What I can tell you from a QA seat is what I check before campaigns launch:

• Domains warmed for at least 14 days (21 if we've had any complaints in the past 90 days)
• SPF, DKIM, DMARC all aligned — not just "present"
• Send limits per mailbox under 50/day for the first two weeks, then evaluate
• A reply-to that a human actually reads

We didn't have a formal pre-launch deliverability check until late 2024. Cost us when a client's primary domain got throttled three days into a 4,000-contact push. That one was a $6,800 retainer month we had to credit back. Now it's a checkbox, and it stays a checkbox.

Step 3 — Run waterfall enrichment in full, then audit each layer

The whole point of waterfall enrichment is that no single provider covers everyone. Okki Go chains several sources, and the coverage jump is real. But here's the part most people miss: coverage and accuracy aren't the same number.

My audit looks like this — pull 200 enriched records at random, then verify each field against the company site or LinkedIn directly. If the email matches at 92%+ but the title field is wrong 30% of the time, that's a personalization problem waiting to happen. Personalization built on bad titles reads worse than no personalization at all.

I usually find one source in the chain is doing most of the heavy lifting and one is barely contributing. Worth knowing which is which before you negotiate renewals.

Step 4 — Treat the LinkedIn email finder as a filter, not an oracle

A LinkedIn email finder is brilliant at one thing: turning a profile URL into a probable address. It is not a verification tool, and treating it like one is where campaigns quietly rot.

What I do: run the finder, then tag every result as high confidence (found at the company domain, matches pattern), medium (found at a personal or secondary domain), or low (pattern guess). Only high goes straight into the send pool. Medium goes through a second pass. Low never gets hit without a manual check.

Rough split on our clients' lists in Q1 2025 was about 55% high, 30% medium, 15% low. If your LinkedIn finder is returning "100% match" on everything, something's off with how it's being run.

Step 5 — Tier your email verification, don't run one setting on everything

Email verification features in modern platforms are more granular than they used to be, and that's a good thing — but only if you use the tiers. The lazy move is a single "verified-only" filter, which throws away valid contacts and still doesn't catch catch-all domains properly.

Our current tiers:

1. Tier 1 — verified, domain-matched. Send as normal.
2. Tier 2 — catch-all domains. Send, but at reduced volume (20/day per domain max) and watch bounce rate closely for 72 hours.
3. Tier 3 — risky or unknown. Hold for a second pass after 7 days, some turn valid on re-check.
4. Tier 4 — hard fail. Suppress permanently, not temporarily.

No verification tool is 100% accurate — anyone claiming that is selling you something. What you want is a tool whose false positives (verifying a bad address) are lower than its false negatives (rejecting a good one), because false positives burn your sender reputation. Ask vendors for that data specifically.

Step 6 — Put a threshold on intent data, not an open door

Intent signals are useful right up to the moment you decide every signal is a buying signal. Then they're noise with a dashboard.

The question most teams ask is "who's showing intent?" The question they should ask is "how many intent signals do we require before we change our touch pattern?"

Our threshold: two or more independent signals within 30 days from separate source types, then the account gets bumped into a higher-touch sequence. One signal gets tagged, not acted on. That alone cut our "hot lead" list by 68% and doubled reply rate on the accounts that made the cut.

Step 7 — Run the human-in-the-loop review on the bottom, not the top

This is the counterintuitive one. Most QA reviews scan the top-scoring leads first — the ones the tool already likes. Waste of time. The tool already likes them.

Review the bottom 15% by confidence score. That's where you'll find the systematic errors: wrong title formats, personalization that pasted the wrong company name, duplicate accounts from different enrichment sources. Fixing the tail improves the whole list; polishing the head doesn't.

Human-in-the-loop isn't a marketing line. If a human isn't touching the low-confidence slice of every batch, you're not running a human-in-the-loop process — you're running automation with a manager who checks the highlights.

Things that go wrong, and where this workflow stops

A few patterns I've seen enough times to flag:

• No suppression hygiene. Unsubscribes, hard bounces, and past customers need to be excluded at the list-build stage, not the send stage. We didn't have this until mid-2024 and every campaign was a small fire.
• Personalization at scale with no review. If your opening line references the wrong product, it's worse than "Hi {{first_name}}".
• Reporting on open rates. Apple's Mail Privacy Protection broke that metric around 2021. Reply rate and meeting-booked rate are what I look at now.

One boundary worth naming: this workflow is tuned for agencies running 2,000–15,000 contacts a month. If you're a 200-seat enterprise sales org with a dedicated data team and a custom warehouse, some of these steps are probably too manual and you'll want to rebuild them. That's a different workflow. I can only speak to what works at the agency scale we run.

Nothing here is a guarantee of reply rates — nobody can honestly promise that. What it does is cut the failure modes that make outbound waste money quietly: bounced sends, wrong-person personalization, and lists that look big and clean and aren't. Run the seven steps in order once, and you'll notice which ones you'd been skipping.