I just activated my LiSeller account and I’m staring at what feels like 10,000 potential leads. The smart lead filtering feature is promising, but I’m not totally sure how to set it up on day one without overcomplicating things.
Here’s my situation: I know generally who I want to target (B2B SaaS companies with 20-200 employees, hiring managers in tech), but I don’t have a crystallized ICP yet. I’m worried if I’m too strict with my filters, I’ll miss good people. But if I’m too loose, I’ll spend all week following up with tire-kickers.
I guess my real question is: what does a realistic day-one filter setup look like? Like, should I start with just job title + company size? Or should I layer in more attributes? And how do I actually know if my filters are any good without running the full campaign?
Anyone else go through this? How did you keep it simple on day one?
Filters are just the targeting infrastructure—they don’t make or break your campaign. Here’s what matters: your message copy. You could have the perfect filter that surfaces 100 qualified leads, but if your opener sucks, they won’t respond.
That said, for day one, keep it stupid simple: job title + company industry. Maybe add company size if you have strong data. Two filters max. Let your message variation do the heavy lifting—that’s where personalization actually happens. Test different hooks against your loosely-filtered audience. The data tells you who really converts.
This is where the LiSeller dashboard really shines. On day one, I’d set up these filters in order:
- Geography (if you care about timezone/language)
- Job title (2-3 primary titles max—anything more is scope creep)
- Company size (revenue or headcount range)
- Industries (3-5 verticals—again, focus over breadth)
After you apply those, you can see how many leads surface. If it’s fewer than 50, you’re too tight. If it’s more than 5,000, too loose. Aim for 200-1,000 as your starting pool. Then I export to Google Sheets and set up conditional columns to track which filter combos actually generate replies. That feedback loop helps you tighten filters for week two.
From recruiting, I can tell you: filter by who you’d actually want to talk to, not just who’s available. Too many people filter by “has a pulse” and wonder why their conversion is trash.
For tech talent specifically, I layer in things like: seniority level, company stability (are they at a hypergrowth startup or Fortune 500?), and whether they’ve changed jobs recently. Recent job changers are way more likely to engage because they’re still settling in and open to conversations.
Don’t overthink it day one, but do think about motivation. Why would this specific person want to take your call? Build your filters around that question.
Safety angle: don’t import a massive list of unvetted leads and blast them all at once, even with smart filters. That’s how you trigger spam signals.
Better approach—apply your filters, then manually review 10-20 of the filtered results before you activate the sequence. Make sure they actually look legit. Real people, active profiles, relevant titles. This takes 15 minutes and saves you from burning your account on junk data.
Also, if you’re using smart filters, make sure they’re pulling from LinkedIn directly, not from a third-party list. Third-party data is often stale and low-quality. Native LinkedIn filtering = safer, better results.
I spent way too long on filters my first week. Honestly, my best campaign started with three filters: job title, company size, and “recently posted on LinkedIn.” That last one is underrated—people who are active on the platform are more likely to engage.
Once my first 100 outreach messages went out, I tracked open rates and reply rates. I could see which filter combinations actually converted. Next week, I tightened based on data, not guesses. Don’t get analysis paralysis day one—just start with reasonable filters and iterate.
From a sales strategy perspective, the quality of your filter setup directly impacts your conversion funnel downstream. A poorly filtered audience wastes follow-up efforts. A well-filtered audience shows patterns.
Here’s how I think about it: your filter should surface people who have three things: (1) the problem you solve, (2) the authority to spend money or make decisions, and (3) a reason to care right now. Most people filter for #1 and #2, but miss #3.
Recent job changers, recently-promoted people, or folks who posted about your problem area—they have urgency. Layer that into your day-one setup if you can. Urgency + relevance = replies.