How to Stop Manually Qualifying Apollo Leads (Clay + n8n Walkthrough)

The Part of Sales That Eats Afternoons

You've got Apollo. You've built lists. You've set up sequences. The problem is the middle part: the 2-3 hours between "export from Apollo" and "confidently send to sequence" that involves manually checking each company like it's 2014.

Is this company big enough to afford us? Did they just raise money? Do they use the tech stack our product integrates with? Are they actively hiring in this function (usually signals growth)?

You check LinkedIn. You check Crunchbase. You Google the company. You make a judgment call. You move on to the next one and do it again 40 more times.

This is the part that Clay and n8n can take off your plate. Not the outreach, not the relationship building. Just the mechanical gatekeeping that doesn't require a human.

What the Workflow Actually Does

The full pipeline looks like this:

Apollo export (CSV) → Clay table enrichment → firmographic scoring → filtered list pushed to Apollo sequences (or Instantly)

Let's go through each step.

Step 1: Getting Your Apollo Export Into Clay

Go to your Apollo search, apply whatever filters you already use (job title, geography, industry). Export to CSV. Don't try to apply too many filters in Apollo to compensate for the fact that you're about to score them anyway. Cast a wider net in Apollo, let Clay do the tightening.

In Clay, create a new table and import the CSV. The columns you need from Apollo at minimum: Company Name, Website, Employee Count (Apollo's estimate), LinkedIn URL, and the contact's email and name.

That's your raw input. Everything else Clay will go and find.

Step 2: The Enrichment Columns

This is where Clay earns its cost. You're going to add several enrichment columns that pull data from external sources automatically.

Column: "LinkedIn Employee Count (Verified)"

Apollo's employee count is often wrong. Clay has a LinkedIn data enrichment that pulls a more current number. Create a column using the Clay LinkedIn enrichment, map it to the Company LinkedIn URL, and have it return headcount. Column name in your table: li_headcount.

Column: "Tech Stack Match"

Create a column using Clay's Clearbit or BuiltWith enrichment. Set it to return a list of technologies the company uses. Then create a formula column next to it that checks: does the tech list contain any of your target tools? For example, if you're selling something that integrates with HubSpot, the formula column returns TRUE if "hubspot" appears in the tech list. Column name: uses_target_tech.

Column: "Last Funding Date"

Clay can pull Crunchbase funding data. Set up an enrichment for the company domain and return the date of last funding round. Column name: last_funding_date.

Column: "Recent Hiring Signal"

This one is optional but worth doing if your product sells into a specific function (say, sales ops or marketing). Use Clay's hiring data enrichment to check if the company has posted a relevant job in the last 90 days. Returns TRUE/FALSE. Column name: active_hiring_signal.

Each enrichment column runs automatically when you add a row to the table. No manual lookups.

Step 3: The Scoring Logic

Once enrichments are populated, you create a formula column called qual_score. This is just arithmetic on your other columns.

Here's a scoring setup that works well for B2B SaaS or service businesses targeting mid-market:

• Employee count between 25 and 500: +3 points
• Employee count 501-2000: +2 points (slightly less ideal, can still convert)
• uses_target_tech is TRUE: +3 points
• last_funding_date within the last 18 months: +2 points
• active_hiring_signal is TRUE: +1 point

Maximum possible score: 9 points. Threshold for sequencing: 6 or above.

The 6-out-of-9 threshold took us a while to calibrate. Too low (4+) and you're still wasting sequence slots on unlikely buyers. Too high (8+) and you're leaving good-fit companies out. 6 is roughly "two strong signals plus one soft signal," which in practice means the company is the right size, uses relevant tech, and has shown some recent growth or activity.

Write that formula in Clay's formula column editor. It's just IF/AND logic.

Column: qual_score

=IF(AND(li_headcount>=25, li_headcount<=500), 3,
  IF(AND(li_headcount>500, li_headcount<=2000), 2, 0))
+ IF(uses_target_tech=TRUE, 3, 0)
+ IF(last_funding_date_days_ago <= 548, 2, 0)
+ IF(active_hiring_signal=TRUE, 1, 0)

(Clay's formula syntax is slightly different from Excel but the logic is identical. The 548 is 18 months in days.)

Step 4: Filtering and Pushing to Sequence

In Clay, you can filter the table view to show only rows where qual_score >= 6. Export that filtered list.

If you want to go further and automate the push to sequences without manual export, that's where n8n comes in.

Set up an n8n workflow with a schedule trigger (runs every morning at 7am, say). The workflow:

1. Calls the Clay API to fetch rows from your table where qual_score >= 6 and sent_to_sequence = FALSE (you add this status column in Clay to track what's been pushed already)
2. For each row, calls the Apollo Sequences API (or Instantly API if you're using that) to add the contact to a sequence
3. Updates the sent_to_sequence column in Clay to TRUE for each row it processed
4. Sends you a Slack message: "Pushed 14 leads to sequence today. Avg score: 6.8."

The whole thing runs without you opening it. Each morning you wake up to a Slack message telling you how many new leads entered your sequences overnight.

What You're Actually Saving

A typical Apollo export session before this workflow: 90 minutes to qualify 50 leads. You're doing this 3-4 times per week if you're serious about outbound. That's 4-6 hours a week of a salesperson or sales manager doing mechanical filtering work.

After: the filtering is done by the time you open your laptop. You spend that time on actual conversations.

There's a subtler benefit too. When you're doing manual qualification after lunch on a Wednesday, your judgment is inconsistent. The 35th company gets a quicker look than the 5th. The scoring formula doesn't have that problem. It applies the same criteria to every row at 3am the same way it does at 9am.

A Few Things That Break This Workflow

Clay enrichments aren't always right. LinkedIn headcount can lag by a few months. Funding data from Crunchbase is incomplete for companies that didn't announce rounds. Sometimes uses_target_tech misses tools that aren't indexed.

The answer isn't to distrust the workflow. It's to treat it as a first filter, not the final word. The score doesn't say "this is definitely a buyer." It says "this company passed enough signals to be worth a sequence slot." Your sequence is doing the real qualifying after that.

Also: if your Apollo searches are already very tight (very specific titles, very specific industries), the scoring may add less value because you've already done most of the filtering. In that case, the main benefit of the Clay layer is the tech stack and hiring signal columns. Worth doing, just recalibrate what you're adding.

One last practical note: Clay credits run out. At medium outbound volumes (500-1000 rows per month), you're spending $50-100/month in Clay credits depending on which enrichments you use. That's still vastly cheaper than a person doing this manually.

Getting Started

You need: an Apollo account (any plan), a Clay account (free tier lets you test with small batches), and n8n (self-hosted free or cloud paid). The build takes about a day if you haven't used Clay before. Less if you have.

The scoring criteria above are starting points. Calibrate them against your existing won deals. Look at your last 20 closed deals and see what scores they would have gotten. Adjust the thresholds accordingly. That calibration step takes an hour and makes the whole thing dramatically more useful.

If you want us to build this for you, book a free workflow audit at airtective.com