SerpAPI to Excel, clean LinkedIn lead lists fast

You find a promising LinkedIn search query, open a dozen results, copy a few URLs, then realize half the rows are messy. “300+ followers” becomes a sorting nightmare. Company names are missing. And now you’re stuck cleaning data instead of talking to prospects.

This is the kind of busywork that slows marketing ops down, but agency owners building lists for clients feel it too. Even a solo founder doing outbound runs into it fast. This SerpAPI Excel leads automation pulls profiles from Google search results, cleans them, and hands you a tidy file you can actually use.

You’ll see how the workflow turns a keyword + location into an Excel download and a NocoDB “source of truth”, so the list is shareable and reusable.

How This Automation Works

Here’s the complete workflow you’ll be setting up:

n8n Workflow Template: SerpAPI to Excel, clean LinkedIn lead lists fast

Click to explore

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Why This Matters: LinkedIn lead lists get messy fast

Building LinkedIn lead lists sounds simple until you do it more than once. You search. You click. You copy URLs into a spreadsheet. Then the cleanup begins: inconsistent fields, weird follower formats (like “1K+”), missing company names, and duplicate entries from different result types. The worst part is the mental load. You keep second-guessing your own list because you know it was assembled in a hurry, across too many tabs, with too many chances to make mistakes.

It adds up fast. Here’s where it usually breaks down.

Copy-pasting profiles one-by-one turns a 30-minute task into an afternoon when you need more than a few dozen leads.
Follower counts and similar fields arrive as text (“300+”), which makes filtering and scoring unreliable.
Company data is often incomplete, so reps can’t personalize outreach without another round of research.
Without a clean database, you end up rebuilding the same list next week because nobody trusts the last version.

What You’ll Build: keyword-to-Excel LinkedIn lead list generator

This workflow starts with a simple input: the keyword and location you want to search. n8n sends that criteria to SerpAPI, which runs the Google search in a way that’s far less likely to get blocked or throttled. The results are split into individual “profile candidates”, mapped into a consistent structure, and then cleaned so you keep only the fields you actually care about. Next, OpenAI fills in missing context like the company name and converts follower text (such as “300+”) into a usable number. Finally, you get two outputs: an Excel file you can download and share, plus a NocoDB table that becomes your ongoing lead database.

The workflow begins on demand, so you run it when you need a fresh list. After SerpAPI returns organic results, n8n normalizes the data, enriches key fields with OpenAI, then exports the final records to Excel and saves the same set into NocoDB.

What You’re Building

What Gets Automated

What You’ll Achieve

Search LinkedIn profiles using keyword and location via SerpAPI.
Split search results into individual lead records you can process.
Clean and map fields into a consistent schema, discarding junk metadata.
Enrich company names and normalize follower counts with OpenAI.

Turn a manual research session into a repeatable run that takes about 10 minutes of hands-on work.
Get an Excel file you can hand to a client or teammate immediately.
Make filtering and scoring possible by converting follower counts into real numbers.
Reduce duplicate cleanup cycles because the output format stays consistent run after run.
Keep a NocoDB source of truth so you can reuse leads instead of rebuilding lists.

Expected Results

Say you need a list of 80 LinkedIn profiles for “B2B SaaS marketing” in London. Manually, you might spend about 2 minutes per result between clicking, copying, and cleaning, which is roughly 2.5 hours. With this workflow, you enter criteria once, let SerpAPI pull the results, and let OpenAI normalize key fields. Your hands-on time drops to about 10 minutes, and you still end up with both an Excel download and a NocoDB table you can query later.

Before You Start

n8n instance (try n8n Cloud free)
Self-hosting option if you prefer (Hostinger works well)
SerpAPI for Google results without getting blocked
OpenAI to enrich company names and normalize followers
NocoDB API credentials (get them from your NocoDB account settings)

Skill level: Beginner. You will connect accounts, paste API keys, and adjust a few fields.

Want someone to build this for you? Talk to an automation expert (free 15-minute consultation).

Step by Step

You run it on demand. The workflow is designed for “run when needed” list building, so you trigger it manually in n8n when you want fresh leads for a campaign.

Your search criteria gets prepared. n8n sets up the keyword and location parameters, then sends them into a SerpAPI HTTP request to fetch Google organic results that point to LinkedIn profiles.

Results are cleaned and enriched. Each result is split into its own item, mapped into consistent profile fields, and stripped of extra metadata. OpenAI then adds the company name and converts follower formats like “300+” into a number you can filter on.

You get two usable outputs. Final records are merged, written to an Excel file for download, and saved into NocoDB so you have a database you can keep building on.

You can easily modify the search criteria to target different roles, industries, or geographies based on your needs. See the full implementation guide below for customization options.

Step-by-Step Implementation Guide

Step 1: Configure the Manual Trigger

Set up the on-demand trigger that starts the workflow and initializes the search criteria sequence.

Add the On-Demand Start node as the trigger.
Connect On-Demand Start to Query Criteria Setup.

Flowpast Branding as a reference note for documentation.

Step 2: Connect SerpAPI Search Request

Define your search criteria and configure the SerpAPI request to fetch profile results.

In Query Criteria Setup, set Keyword to nocode and Location to Germany.
Set Number of search results to be returned to 20, Host langauge to en, Geolocation to de, Search engine to google, and site to linkedin.com/in.
Open SerpAPI Search Request and set URL to https://serpapi.com/search.
Enable Send Query and set query parameters: q to =site:{{ $json.site }} {{ $json.Keyword }} {{ $json.Location }}, hl to ={{ $json['Host langauge'] }}, gl to ={{ $json.Geolocation }}, num to ={{ $json['Number of search results to be returned'] }}, and engine to ={{ $json['Search engine'] }}.
Credential Required: Connect your serpApi credentials in SerpAPI Search Request.

Step 3: Set Up Result Splitting and Mapping

Split the organic results array and map raw fields into a structured profile format.

In Split Organic Results, set Field to Split Out to organic_results.
In Map Profile Fields, add assignments for: NameInLinkedinProfile = ={{ $json.title }}, linkedinUrl = ={{ $json.link }}, Snippet = ={{ $json.snippet }}, Followers = ={{ $json.displayed_link }}, Keyword = ={{ $('Query Criteria Setup').item.json.Keyword }}, Location = ={{ $('Query Criteria Setup').item.json.Location }}, Rich snippet = ={{ $json.rich_snippet.top.extensions }}, and snippet_highlighted_words = ={{ $json.snippet_highlighted_words }}.
Confirm the execution flow: Map Profile Fields outputs to both Extract Company & Followers and Combine Final Records in parallel.

⚠️ Common Pitfall: Map Profile Fields contains two assignments named linkedinUrl. If you only want one field, remove the duplicate to avoid overwriting.

Step 4: Set Up the AI Enrichment

Use OpenAI to extract the company name and follower count, then prune the AI response to keep clean fields.

In Extract Company & Followers, select the model gpt-4o and enable JSON Output.
Set the message content to =Transform {{ $json.Followers }} into a number and extract where possible the name of the company in {{ $json.NameInLinkedinProfile }} or in {{ $json.Snippet }} Do not output things like location or name, only followers and company_name.
Credential Required: Connect your openAiApi credentials in Extract Company & Followers.
In Prune AI Metadata, map followers_number to ={{ $json.message.content.followers }} and NameOfCompany to ={{ $json.message.content.company_name }}.

Step 5: Configure Output Destinations

Combine base and AI-enriched records, then export to Excel and save to NocoDB.

In Combine Final Records, set Mode to combine and Combine By to combineByPosition.
Confirm the flow: Combine Final Records outputs to both Export Profiles to Excel and Save Records to NocoDB in parallel.
In Export Profiles to Excel, set Operation to xlsx.
In Save Records to NocoDB, set Operation to create, Project ID to [YOUR_ID], Table to [YOUR_ID], and Data to Send to autoMapInputData.
Credential Required: Connect your nocoDbApiToken credentials in Save Records to NocoDB.

⚠️ Common Pitfall: Replace [YOUR_ID] in Save Records to NocoDB with your actual NocoDB Project ID and Table ID, or the node will fail.

Step 6: Test and Activate Your Workflow

Run a manual test to verify results, then activate the workflow for production use.

Click Execute Workflow from On-Demand Start to run a test.
Verify that Export Profiles to Excel produces a downloadable XLSX file and that Save Records to NocoDB creates new records.
If the AI output is missing fields, revisit Extract Company & Followers and adjust the message content or model selection.
Toggle the workflow to Active to use it in production.

🔒

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Troubleshooting Tips

SerpAPI credentials can expire or need specific permissions. If things break, check your SerpAPI dashboard API key status and usage limits first.
If you’re using Wait nodes or external rendering, processing times vary. Bump up the wait duration if downstream nodes fail on empty responses.
Default prompts in AI nodes are generic. Add your brand voice early or you’ll be editing outputs forever.

Quick Answers

What’s the setup time for this SerpAPI Excel leads automation?

About 30 minutes if your API keys are ready.

Is coding required for this LinkedIn lead list cleanup?

No. You’ll connect SerpAPI, OpenAI, and NocoDB credentials, then edit a couple of fields like keyword and location.

Is n8n free to use for this SerpAPI Excel leads workflow?

Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in OpenAI API costs (often a few cents per run) and SerpAPI usage based on how many searches you do.

Where can I host n8n to run this automation?

Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.

Can I modify this SerpAPI Excel leads workflow for different use cases?

Yes, and you probably should. Change the criteria in the “Query Criteria Setup” node to target different keywords, locations, or LinkedIn URL patterns. If you want different enrichment, swap what the “Extract Company & Followers” OpenAI step returns (for example, add industry or role seniority if it’s in the snippet). You can also adjust what gets removed in “Prune AI Metadata” so your Excel export includes exactly what your team needs.

Why is my SerpAPI connection failing in this workflow?

Usually it’s an invalid or expired API key, or you’ve hit a usage limit in your SerpAPI account. Update the credential in n8n and re-run a single test search first. If that works, check the query you’re sending (keyword and location) because malformed parameters can also return empty results.

What volume can this SerpAPI Excel leads workflow process?

For most small teams, hundreds of leads per day is realistic, and the main limit is your SerpAPI plan plus how quickly you want OpenAI enrichment to run.

Is this SerpAPI Excel leads automation better than using Zapier or Make?

Often, yes, because this workflow isn’t just “send data from A to B”. You’re splitting results, reshaping fields, merging cleaned records, generating an Excel file, and writing to a database, all in one run, and that’s the kind of multi-step logic n8n handles comfortably. Self-hosting also matters when you start running lots of executions, since you’re not paying per task in the same way. Zapier or Make can be great for lightweight two-step zaps, but list-building usually turns into lots of small transformations. If you’re on the fence, Talk to an automation expert and you can sanity-check the best approach for your volume.

Once this is running, lead list building stops being a recurring chore. You’ll have cleaner data, a shareable Excel export, and a NocoDB base you can actually build on.

SerpAPI to Excel, clean LinkedIn lead lists fast

How This Automation Works

n8n Workflow Template: SerpAPI to Excel, clean LinkedIn lead lists fast

Why This Matters: LinkedIn lead lists get messy fast