If you’re tired of slogging through endless lists of leads that aren’t even close to your target customers, you’re not alone. Most B2B prospecting tools promise “precision” but hand you a firehose of data instead of the handful of real, winnable leads you actually want. This guide is for sales teams, agency folks, and founders who want to cut through the noise and use custom filters in Listkit to find B2B contacts that actually fit their ICP (ideal customer profile). No fluff—just clear steps and a few honest warnings about what to skip.
Why Custom Filters Matter (and Where Most Go Wrong)
Let’s be real: default filters in prospecting tools are built for the lowest common denominator. You get broad categories, generic industries, and a river of leads, most of whom will never buy from you. If you want real results—higher reply rates, fewer wasted emails, and more closed deals—you need to go way more specific.
Custom filters help you: - Zero in on companies that actually need what you’re selling. - Avoid time-wasters who will never buy (or can’t afford you). - Stop relying on generic “industry” tags that mean nothing.
But here’s what a lot of people get wrong: - Overcomplicating it: Building a 10-layer filter tree that’s impossible to manage. - Chasing hype fields: Filtering by things like “AI-adoption” or “web3” because it sounds cool, not because it matters to your business. - Ignoring data quality: Trusting every filter as gospel. (Spoiler: no database is perfect.)
Alright, let’s get into the practical steps.
Step 1: Define Your Ideal Customer (Before Touching a Filter)
You can’t build good filters if you don’t know who you’re actually after. This isn’t about buzzwords; it’s about the real-world details that separate a dream client from a dud.
Ask yourself: - What industry or niche are they really in? (Be specific. “Tech” is not a niche.) - How big are they? (Employee count, revenue, or funding stage—not all three at once.) - Where are they located? (If you only sell in the US, don’t waste time with international leads.) - What tech do they use? (If you sell a Salesforce plugin, you need companies using Salesforce, not HubSpot.) - Who’s the buyer? (Job titles or departments—CEO, Head of Marketing, etc.)
Pro Tip: Write this out first. If your team can’t describe your ICP in a few sentences, filters won’t save you.
Step 2: Get Familiar with Listkit’s Filter Options
Before you start building, take a quick tour of what Listkit actually lets you filter on. No tool covers everything, so you need to know the limits.
Common filter options in Listkit: - Industry / Vertical: Usually a dropdown. Sometimes too broad—combine with keywords for more precision. - Company Size: Employee count or revenue bands. Useful, but don’t get hung up on tiny differences. - Location: Country, state, or city. Geographic targeting is basic but powerful. - Tech Stack: What software they use. Goldmine if you have a very specific integration. - Job Titles / Roles: Who you want to contact. Some tools have better data here than others. - Funding / Ownership: For startups, filtering by recent funding can help. For others, skip it.
What to ignore: Fancy “intent” signals (like “researched X topic last week”) are often unreliable. Stick to hard facts unless you have proof the intent data in Listkit is rock solid.
Step 3: Build Your First Custom Filter Set
Start simple. Overly complex filters usually backfire by shrinking your list to zero—or including junk anyway.
Here’s how to do it in Listkit:
- Start with company-level filters.
- Pick your industry.
- Set company size (e.g., 50–250 employees).
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Choose location.
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Add tech stack or firmographic filters.
- Only use these if they’re critical to your product.
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Example: If your SaaS only works with Shopify stores, add Shopify as a required tech.
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Refine with job title filters.
- Listkit usually lets you filter by job title or department. Go for the decision maker (not “Marketing Specialist” if you really need the CMO).
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Don’t get too granular—“VP Growth,” “Head of Growth,” and “Growth Lead” are all fine. Broader is better than missing people.
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Review your filter logic.
- Ask: “If I got 100 leads from this, would I actually want to email all of them?”
- If not, tweak.
Pro Tip: Test your filter with a small sample before pulling the trigger on a huge export. It’s faster to adjust now than to clean up a mess later.
Step 4: Avoid the Most Common Filtering Pitfalls
Here’s where even experienced teams trip up.
- Overfiltering: Every filter you add cuts out potential leads. If your list is tiny, try relaxing a filter and see what comes back.
- Underfiltering: If you’re still getting junk, tighten up the most important filter only—don’t add five new ones at once.
- Assuming data is perfect: No lead tool is 100% accurate. Always double-check a sample before blasting out emails.
- Chasing shiny objects: If a new filter pops up (“AI readiness!”), ask if it really matters for your business or if it’s just marketing noise.
Step 5: Iterate Based on Real Results
Your first filter set will not be perfect. That’s normal.
After you run your first campaign, pay attention to: - Reply rates: If no one’s answering, you’re probably off-base with your targeting. - Lead quality: Are these companies actually a fit? Or are you getting a bunch of randoms? - Bounce rates: High bounce = bad data. Tweak filters or try a different segment.
Pro Tip: Keep a “filter changelog.” When you adjust your filters, jot down what you changed and why. It’ll save you a ton of time when you want to retrace your steps.
What Actually Works (and What to Skip)
Based on real-world use, here’s what tends to work best:
Most useful filters:
- Industry + company size + location: The holy trinity for most B2B sellers.
- Job title broad matching: Focus on decision makers—don’t get lost in title soup.
- Tech stack (if you have a real, product-based need): Only use if it’s a make-or-break factor.
Filters to use with caution:
- “Intent” or “interest” signals: Often overhyped or stale. Use only if you’ve validated them.
- Funding rounds: Useful for some niches, but don’t assume a company with funding is automatically a good fit.
What to ignore:
- Trendy “AI” filters: Unless your product is truly for AI-first companies, this is just noise.
- Overly granular filters: If you’re filtering by 6+ variables, your pool is probably too small.
Wrapping Up: Simple Beats Fancy
Custom filters in Listkit are powerful—but only if you keep things grounded in reality. Start with a clear picture of your ideal customer, build simple filters, and test the results. Don’t chase hype or overcomplicate. Most importantly, keep tweaking based on what works, not what looks cool.
You’ll save time, skip the headaches, and actually find B2B customers who want what you’re selling. Keep it simple, iterate often, and don’t be afraid to toss out what isn’t working. That’s how you win.