Clay + LeadsFactory.io + LinkedIn Sales Navigator: How to Find Decision Makers Automatically for Sales and Lead Generation

Every result around the search term “how to find decision makers” gives the same advice: use LinkedIn, try a database, look at the company’s About Us page, and maybe attend a conference.

The problem nobody talks about is that doing all of that manually, across disconnected tools, for every single target account, is a massive time sink for your sales process. And if you’re running outbound at any kind of scale, it’s borderline impossible.

Your sales reps are spending hours every week bouncing between LinkedIn Sales Navigator, Apollo, company websites, and spreadsheets trying to figure out who to contact at each target company.

They're copying and pasting names, guessing job titles, verifying emails one at a time, and still ending up with incomplete lists that are outdated by the time the first email goes out.

We know this because we work with sales teams every day at Nebor, and this is one of the most common bottlenecks we see.

The companies that come to us are not lazy or incompetent. They're just stuck doing everything manually because they never set up a system to handle this work for them.

This post is going to walk you through the traditional methods for finding decision makers (because they do work as individual tactics), and explain why relying on them without a system behind them eats your team's time alive.

Then we'll show you exactly how we automate this work for clients at Nebor so the people who matter end up in your pipeline without anyone having to chase them down.

TL,DR: what the automated workflow for finding decision makers looks like

When fully build and operational here’s what your automated workflow should look like.

The traditional approaches to finding decision makers (and why they fall apart without a system behind them)

Before we get to how we automate things, it's worth covering the standard methods because they form the building blocks of any good system. The issue is not the tactics themselves.

It's that most teams use them in isolation, manually, with no connective tissue between them.

Here's what we mean.

LinkedIn and LinkedIn Sales Navigator are the go-to starting point that eats up your team's time

LinkedIn Sales Navigator is one of the best tools out there for identifying key decision makers. You can filter by job title, seniority level, department, company size, industry, geography, and more.

You can save leads and accounts. You can build targeted lists of VPs of Sales at SaaS companies with 50 to 200 employees in the DACH region. It's incredibly powerful. You probably don't need us to know that.

But here's where it breaks down in practice. Your SDR opens Sales Navigator, runs a search, scrolls through profiles, clicks into each one to verify it's a fit, copies the name and title into a spreadsheet, and then moves on to the next one.

Even with good filters, you're talking about 30 to 60 seconds per lead just to evaluate and capture the basic info. Multiply that by hundreds of accounts and you'll soon realize that's a real problem at scale.

Prospecting databases like Apollo, Cognism, and ZoomInfo provide good data, but never the full picture

Tools like Apollo, Cognism, and ZoomInfo give you access to large databases of contacts with direct phone numbers, email addresses, LinkedIn profiles, and firmographic data for relevant decision makers and even general employee data at companies.

If you can sustain the budget, some of them also offer intent data (although, as we've talked about in previous posts, the quality of that intent data is just flat out poor).

The appeal is clear. Type in a company name, get back a list of employees with their contact details, and start reaching out. For one-off research, this works fine.

But there are a few things you need to know.

No single data provider has complete coverage. Apollo could have the CMO's email at Company A, but not at Company B. Cognism might have the phone number for one contact but return nothing for another. ZoomInfo might show you someone who left the company six months ago.

This is a well-documented issue in the data enrichment space, and it is why relying on one tool for your contact data will always leave gaps in your pipeline.

The other challenge is that these tools operate as standalone databases. Your reps log in, search, export a CSV, clean it up, upload it to their CRM or outreach tool, and then start a campaign.

That's a lot of manual steps between "I found a contact" and "I've sent them a personalized email." Every one of those steps introduces delays, errors, and wasted time.

At Nebor, we use multiple data providers layered together in a sales automation waterfall enrichment setup inside Clay. That means if Apollo doesn't have the email, LeadMagic tries next, then Findymail, then FullEnrich, and so on.

This approach consistently gets us higher enrichment coverage compared to using a single tool. But setting this up manually every time you need to find decision makers is impractical.

Leveraging company resources: The "old school" approach that still has its place

Some guides will tell you to check the company's About Us page, look for team members on their website, review press releases, or browse conference attendee lists and speaker rosters. That's not bad advice.

It can uncover key contacts that don't show up in databases, especially at smaller companies or in industries where people are less active on LinkedIn.

The problem is that this is the most manual and least scalable approach of all. You're literally clicking through individual company websites. Even if you find a great prospect this way, you still need to enrich the contact with a verified email, log the information in your CRM, and slot them into a sequence.

That's why we don't recommend this as a primary method for finding decision makers. It's a complement to a more systemic approach, not a replacement.

"Reverse" searching decision makers with AI tools like Clay (it's powerful, but not meant to be a manual task)

This is where things start getting interesting. Clay, specifically, lets you input a domain name or company name and then automatically find relevant contacts using its 100+ integrated data providers.

You can set up a Clay table, feed it a list of companies, and use enrichment steps to pull back the names, job titles, LinkedIn URLs, and email addresses of the people you want to talk to.

Clay can even use AI to interpret job titles and determine whether someone is a decision maker based on your criteria beyond the exact title match.

That means if you're looking for "the person who makes purchasing decisions for sales tools," Clay's AI can assess whether "Head of Revenue Operations" fits that description. This is a massive step up from the rigid title-matching logic in Sales Navigator or Apollo.

But here's where a lot of teams get stuck. They treat Clay as a research tool their reps jump into whenever they need to find a contact. That defeats the purpose entirely. You've just replaced the manual Google search with a manual Clay search.

Clay is at its most powerful when it sits at the center of an automated B2B lead generation workflow that finds decision makers without anyone needing to do anything. That's what we're going to talk about next.

How Nebor helps you find decision makers through workflow automation (so your team can focus on selling)

Everything we've covered above represents the ingredients. LinkedIn Sales Navigator gives you the filters. Data providers give you the contact details. AI gives you the intelligence to match the right people. Company resources give you additional context.

What most teams are missing is the recipe. A system that chains all of these ingredients together into an automated workflow that runs continuously, finds the right decision makers at the right companies, enriches them with accurate contact data, and pushes them into your outreach pipeline without your reps lifting a finger.

That's exactly what we build at Nebor. Here's how we approach it.

Start by defining your ICP and buyer persona so you know exactly who you want to reach

We start every engagement with this step, and it's the one most teams rush past.

If you don't have a clear and precise Ideal Customer Profile (ICP) and buyer persona, everything downstream breaks.

Your filters will be too broad, your enrichment will return irrelevant contacts, and your outreach will land in front of people who have zero authority (or zero interest) in buying what you sell.

When we say ICP, we mean the company-level criteria. Industry, company size (employee count and revenue), geography, tech stack, growth stage, and any other firmographic or technographic attributes that define your best-fit accounts.

When we say buyer persona, we mean the individual-level criteria. Job title patterns, seniority level, department, and decision-making authority.

For example, one of our clients sells event management software. Their ICP is (let's say) mid-market companies in Europe that organize at least 5 events per year.

Their buyer persona is the person responsible for event operations or marketing events. That could be titled "Head of Events," "Event Marketing Manager," "Director of Field Marketing," or even "VP of Marketing" at a smaller company. If we just searched for one of those titles, we'd miss a huge chunk of their addressable market.

Getting this right up front is critical because it determines everything. Which tools to use for sourcing, what filters to set, how to configure your persona waterfall, and what signals indicate someone is actually worth reaching out to.

At Nebor, we've learned from working with a lot of B2B companies that most businesses struggle to define their ICP with the precision needed for effective outbound campaigns, which is why many turn to specialist Clay agencies or in-house experts to close that gap.

So we help refine it as part of the process. We look at your existing customer data, identify patterns in your best accounts, and translate that into a clear targeting framework.

Standardize your strategy for finding decision makers based on your ICP and the tools that work best

Once your ICP and persona are defined, the next step is figuring out the best way to find decision makers at the companies that match. The answer depends on your specific market and motion, but here's the framework we use for tool selection at Nebor.

If your ICP is well-represented on LinkedIn (which is the case for most B2B SaaS, tech, consulting, and professional services companies), then LinkedIn Sales Navigator combined with LeadsFactory.io is your best bet for identifying and matching decision makers.

LeadsFactory.io is especially interesting here because it scrapes Sales Navigator in real time without requiring your LinkedIn credentials, and it uses smart persona waterfall logic.

You upload your company list, define your personas in priority order (for example, 1. CMO, 2. VP of Marketing, 3. Head of Growth), and LeadsFactory.io searches Sales Navigator to find the closest match at each company.

If the CMO doesn't exist at a particular company, it automatically falls back to the VP of Marketing, and so on. This solves the title inconsistency problem we talked about earlier.

And because it integrates with Clay, the data can flow directly into your enrichment and outreach workflows.

If your ICP includes industries or roles that are less visible on LinkedIn, a layered approach using Apollo, Cognism, or ZoomInfo combined with Clay's AI enrichment fills the gaps.

The point is that no single tool is the right answer. The right tool depends on who you're trying to reach and where they're most likely to surface.

This is exactly the kind of thinking that goes into building a sales tech stack that runs on autopilot with Clay and n8n.

Finding decision makers as part of an automated outbound sales and lead generation workflow

This is where everything comes together. Instead of having your reps manually research decision makers for each target account, you build an automated workflow that does it for them, continuously.

Here's how a typical Clay-based lead generation system and decision-maker workflow looks when we build it for our clients at Nebor.

Step 1: Source your target companies.

This can come from multiple sources. You can scrape a specific list using LinkedIn Sales Navigator filters (exported via PhantomBuster or a similar tool).

You can pull companies from your CRM that match certain firmographic criteria. You can use Clay's built-in company search to build a list based on industry, size, funding stage, or tech stack.

Or you can use a signal-based trigger (more on that shortly). The company list lands in a Clay table as the starting point.

Step 2: Find the decision makers at each company using LeadsFactory.io

For each company in the Clay table, we run an automated lookup through LeadsFactory.io. We input the persona priority list, and LeadsFactory.io searches Sales Navigator for the best-fit decision makers at each company.

The output is a list of names, job titles, and LinkedIn URLs of the people we want to reach.

This step alone replaces hours of manual searching and matching.

Step 3: Enrich contacts with verified emails, phone numbers, and additional data.

Once we have the LinkedIn URLs of the right contacts, we run a waterfall enrichment to find their verified emails, phone numbers, firmographics, technographic data, and any other data points needed for personalization.

If one provider doesn't return an email, the next one tries. This multi-source approach consistently delivers better coverage than relying on any single tool.

Step 4: Qualify and score each lead.

Not every decision maker is worth contacting right now. Using Clay's AI and custom scoring logic, we can qualify leads based on criteria you define. Company size, tech stack, recent funding, hiring signals, or engagement history.

Leads that meet your threshold move forward. Leads that don't get parked for later or excluded entirely. This makes sure your reps only see the best-fit prospects.

Step 5: Generate personalized outreach and push to your outreach platform.

Using Clay's native AI integrations (ChatGPT or Claude), we generate personalized message drafts based on the enriched data we've collected.

This isn't the typical "Hi {FirstName}, I noticed your company does X stuff." It's contextual messaging that references real intelligence about the prospect and their company.

The drafts then get pushed into your outreach platform (Instantly, Smartlead, Lemlist, or whatever you use), where the campaigns run automatically with proper deliverability protection.

The entire process, from sourcing companies to launching outreach, runs without your reps needing to do any of it. They show up to qualified meetings, not to a research pile.

A Manual Input Workflow that finds decision makers directly inside your CRM

Not every team needs a fully automated outbound engine from day one. Some teams just need a faster way to identify decision makers for accounts that are already in their CRM.

So, here's a simpler workflow we often set up for clients in this situation.

Your sales rep identifies a target company in your CRM (HubSpot, Salesforce, Pipedrive, whatever you use).

They flag the account, or it gets flagged automatically based on a trigger (like being added to a specific pipeline stage). That trigger sends the company data to a Clay table via webhook or n8n automation.

Clay then activates LeadsFactory.io to find the right decision makers based on your persona waterfall, enriches the contacts with verified emails and additional data, and pushes the finalized contacts back into your CRM, attached to the correct account.

From the rep's perspective, they flagged a company and a few minutes later, the right contacts appeared in their CRM with everything they need to start outreach. No logging into Sales Navigator. No opening Clay. No manual research at all.

This is a great starting point for teams that want to reduce the time their reps spend on research without overhauling their entire outbound process while exploring specialized sales automation companies and tools.

And once you see the speed and quality this workflow delivers, it becomes easier to scale up to fully automated outbound, with automated CRM hygiene and enrichment feeding the same engine.

How to find decision makers among people already visiting your website (through visitor identification)

There's another category of decision makers most teams completely ignore. People who are actively visiting your website but never fill out a form.

Most of your inbound interest probably never converts. People who visit your pricing page, read your blog posts, or check out your case studies match your ICP exactly. They're showing real interest, but if you don't know who they are, they leave your site and you never hear from them.

This is one of the highest-value opportunities most companies leave on the table. And it's the kind of work top outbound experts now treat as table stakes.

Website visitor identification tools like RB2B (which works for US-based visitors) and Leadinfo (best for Europe), or Snitcher can identify the individuals behind anonymous website visits, enrich them with contact details and profile data, and push that information into Clay or your CRM.

Here's how we set this up as a workflow at Nebor.

A visitor lands on your website and meets certain engagement criteria (for example, they visited the pricing page twice or spent more than 20 minutes on a specific product page).

The visitor identification tool de-anonymizes them and pushes the company and contact data into Clay. Clay enriches the lead with full firmographic and persona data, then qualifies them against your ICP criteria.

If they qualify, the rest of the workflow unfolds automatically. Find the decision maker, enrich, qualify, personalize, and send.

Your sales reps get notified when there's a warm lead to follow up on. That's it.

We extend this same logic beyond website visits. Social media engagement, content downloads, webinar registrations, and other digital interactions can all feed into the same workflow, which is exactly the kind of modern motion top Clay sales automation agencies now recommend.

The idea is that any meaningful engagement signal becomes a trigger that automatically identifies the decision maker, enriches the lead, and sets up the outreach. Your reps just focus on the conversation.

Hire Nebor to build Clay workflows that find decision makers automatically (so your sales reps can sell)

If you've read this far, you probably have a clear picture of what a modern, automated system for finding decision makers looks like. You understand the tools, the logic, the workflow structure, and why doing this manually doesn't scale.

Here is what it should look like.

But here's the reality. Building this yourself takes time, expertise, and a lot of trial and error. You need to know how to structure Clay tables, configure waterfall enrichments, connect tools via APIs and webhooks, set up n8n automations, handle data hygiene, and tune every step so the output is clean, accurate, and ready for outreach.

And that's just the technical side. You also need the sales strategy to ensure you're targeting the right people with the right message at the right time.

That's exactly what we do at Nebor. We're not just Clay experts or random workflow builders. We're sales and lead generation specialists who happen to use Clay as our core platform.

If you're tired of your sales reps spending more time researching than selling, book a 15-minute call with us and we'll show you exactly how we'd build this for your business, or help you evaluate top Clay experts for outbound sales automation if you're comparing partners.