okkigo (okki-go) FAQ: Account Research, Reply Rate Benchmarks, and Sales Navigator Scrapers

I'm a quality and brand compliance manager at a B2B revenue operations company. I review outbound deliverables before they touch a customer's domain — lists, enrichment jobs, campaign assets, the occasional email sequence. Around 300 items a year, four years running. In our Q1 2026 audit I rejected roughly 18% of first-round submissions, and nearly every rejection came down to one of three things: stale job titles, unverified catch-all addresses, or intent signals with no timestamp on them.

So I end up answering the same questions a lot — from vendors, from our SDR leads, from other ops people. Here's the FAQ version, which is also the version I hand to new hires. Jump to whatever you need:

• What is okkigo (often typed as okki-go)?
• What does okki go account research actually give you?
• okki go vs Smartlead — how do I decide?
• What's a realistic cold email reply rate benchmark?
• What is company enrichment in sales intelligence, and why does cheap enrichment cost more?
• What is a Sales Navigator scraper, and when should a B2B team use one?
• What should I check before I approve a data deliverable?
• What if my team's already doing this by hand?

What is okkigo (often typed as okki-go)?

okkigo is a prospecting layer built around agents rather than dashboards. In plain terms: you point it at an account list, and agents go do the research and enrichment work a junior SDR would otherwise grind through manually. It combines waterfall enrichment with intent signals, and keeps a human in the loop before anything actually goes out the door.

What it isn't: a sending tool. It doesn't warm your inboxes or run your sequences. If you're already running Smartlead or something like it, okkigo sits upstream. That distinction matters more than any feature grid, because it's the difference between "replace this" and "this fills the gap you've been covering with people's afternoons."

What does okki go account research actually give you?

Account research means pulling the things that decide whether a company is worth contacting — hiring patterns, tech stack, funding, product moves, whatever buying signal you actually care about. The output shouldn't be a summary paragraph. It should be structured fields you can filter on.

Here's the part I care about as a reviewer: can the tool tell me where each field came from? I ran a side-by-side once — same 200-account list, two researchers against an agent-native pass. The humans spent just over 25 hours on it. The agent pass got most of it in about 20 minutes, and I hand-checked the fields that looked off. The agent saved maybe 80% of the time. The other 20% — the verification — is where the real value sits.

That's my rule, and it's not negotiable: if a tool can't show a source and a date on a field, I treat the field as a guess.

okki go vs Smartlead — how do I decide?

Most teams are comparing the wrong two things. Smartlead is sending infrastructure and sequencing — inboxes, warmup, rotation, delivery. okkigo is the research and intent layer that decides who goes into the sequence in the first place. Plenty of teams run both, and it's not redundancy; they're answering different questions.

The question that actually decides it is where your team is losing hours right now. If someone's building account lists by hand every afternoon, the gap is upstream. If your lists are solid but nothing gets replies and domains keep burning, the gap is in sending.

I watched a team buy a second sending tool when their real problem was data quality. That was roughly $6,000 and two months of onboarding spent on the wrong layer. Not a disaster, just avoidable.

What's a realistic cold email reply rate benchmark?

I don't trust any single number, and you shouldn't either. A lot of published reply-rate averages come from companies selling outbound software — which is a bit like asking a barber whether you need a haircut. The numbers also swing wildly by segment, list source, and offer. A 4% reply rate on 500 hand-vetted enterprise accounts and 4% on 50,000 scraped SMB contacts are not the same achievement, even though they look identical in a slide.

What I will quote is a published threshold, because it's checkable: Google's bulk sender requirements, in effect since February 2024, call for senders to Gmail addresses to keep spam complaint rates under 0.3% and to authenticate with SPF, DKIM, and DMARC. That's a hard floor, not a benchmark. Go over it and deliverability falls apart — at which point your reply rate is irrelevant, because nobody's seeing the mail.

Build your own baseline instead. Segment by list source and campaign type, measure against emails actually delivered rather than sent, and give it 90 days before you conclude anything. Ours took about six months to stabilize enough to be useful. Tedious, but now I can tell you which of our list sources is quietly dragging the average down.

What is company enrichment in sales intelligence, and why does cheap enrichment cost more?

Enrichment is filling in the fields your record is missing — employee count, industry, tech stack, funding stage, current title. Waterfall enrichment means trying one provider, then the next, then the next, until a field gets a hit, instead of trusting a single source to have everything.

Here's what vendors won't lead with: match rate and accuracy are two different numbers, and almost everyone quotes the first one. A provider can match 85% of your records and still be wrong on a meaningful chunk of them, especially titles and headcount, which go stale fast. I've had a vendor proudly report a 92% match rate on a batch where I found 14% of the mobile numbers dead on arrival.

This is where price tricks people. A dataset that's 30% cheaper per record looks great, right up until 12,000 bad records cost you a re-run, the SDR hours burned on dead contacts, and — in our case — around $9,000 in cleanup plus six weeks of rebuilding domain reputation. The cheap batch was the expensive one. That's not a slogan, it's just what the invoice said.

What is a Sales Navigator scraper, and when should a B2B team use one?

A Sales Navigator scraper pulls your LinkedIn Sales Navigator search results into a spreadsheet — names, titles, companies, sometimes contact details. People use them because Sales Navigator's filters are genuinely good and its export limits are genuinely frustrating.

Two things worth knowing before you buy one. First, scraping Sales Navigator violates LinkedIn's user agreement, and accounts do get restricted. Treat that as a known risk, not a rumor. Second, scraped contact data decays fast — promotions, job changes, and layoffs break your records within a quarter or two, usually without any visible warning sign.

Where it makes sense: a one-off, tightly filtered list you'll act on within 30 days. Say 300 accounts in one region with one specific tech stack. Where it doesn't: anything you need to refresh monthly, or anything feeding a long-term nurture sequence. For those, licensed data or an enrichment API is the boring answer that holds up.

We lost most of a quarter once to a scraped list where the contact data had gone stale between pull and send. Nobody caught it, because the fields all looked complete.

What should I check before I approve a data deliverable?

My checklist, and it's short on purpose.

One: does every field carry a source and a date? Two: how were email addresses verified — specifically, are catch-alls labeled as catch-alls, or quietly counted as valid? A catch-all domain accepts mail to any address at that domain, so calling one "verified" is close to meaningless. Three: what's the title accuracy on a random sample? I pull 4% and check by hand. Anything under roughly 90% on recent titles goes back.

Four: does the deliverable respect suppression lists and unsubscribe state? That's legal, not a preference — CAN-SPAM requires a working opt-out mechanism and accurate headers, and the FTC does enforce it. Five: is the format usable, or am I spending a day reformatting columns before anyone can load it?

Bottom line, I'd rather pay more for a deliverable that survives my audit than save on one I have to send back. Sending it back costs two weeks I don't have.

What if my team is already doing this by hand?

Then you're not doing it wrong. A senior SDR who knows the ICP cold will beat a badly configured tool every single time. I've watched that happen more than once, and it's never close.

The real question isn't manual versus automated. It's which part of the work deserves a person's judgment and which part is just repetition. Researching 200 accounts, deduping records, checking whether an address bounced — repetition. Deciding which of those 200 accounts is worth a real conversation — judgment, and that's the expensive part you don't want to spend on data entry.

The teams that get this right usually run a hybrid: agents handle the pull and the enrichment, a human reviews the shortlist before anything sends. Human-in-the-loop isn't a compromise position. It's the only version of this that survives contact with a real pipeline.