How to Build No-Code Lead Generation Workflows? (2026)

Most lead lists are built by hand. Someone runs a search, copies the names into a sheet, hunts for emails and decides who looks right.

It works for 20 leads. It breaks at 200, and by the time the list is ready, half of it is stale.

No-code lead generation workflows do the repeat work for you: find the leads, keep the good ones, score them against your ideal customer and drop them where your team works, every week, without code.

There is no single right tool for this. What matters is a clear profile, a clean lead source and a scoring step that judges every lead the same way.

We built the full workflow twice, on real data: once in n8n and once in Make. Copy either one below, or follow the same method with your own stack.

We will walk you through both step by step.

The public GitHub repo has the ICP template and setup notes, and you can copy the n8n JSON or the Make blueprint straight from the box.

{ }

Weekly lead list: Sales Navigator to Google Sheets, scored by AI

sales-navigator-to-google-sheets-scored.json · 3 ways to run

GitHub Code

n8n JSON, Make blueprint and ICP.md template, with a step-by-step setup guide.

View repo

n8n No-code

Import the workflow into n8n. Paste it onto an empty canvas or upload the JSON.

Copy workflow Download JSON

Make No-code

Import the blueprint into Make. Evaboot pushes each weekly export to it.

Download blueprint

We used Sales Navigator, OpenAI and Google Sheets in our example, but with a few tweaks, the same workflow can send leads to a CRM or use another AI model.

What Is a No-Code Lead Generation Workflow?

A no-code lead generation workflow is an automation you build in a visual tool instead of writing code.

You connect blocks on a canvas, and each block does one job: start the run, fetch leads, check them, score them, save them.

It replaces three manual jobs:

The result is a list that refreshes itself. Ours lands in a Google Sheet every Monday at 8am, with an AI score from 0 to 100 next to each lead.

5 No-Code Lead Generation Workflows You Can Build

Most teams start with one of these five. They share the same blocks; only the source and the destination change.

The job-change workflow is what we call IBAO: Intent Based Automated Outreach.

A recurring Evaboot export catches an intent signal, such as a new job, and pushes the lead into your CRM, so outreach starts without anyone building a list.

We built the first one twice, in n8n and in Make, and the steps below come from those runs. Swap the source or the destination and the same template covers most of the others.

The 5 Building Blocks of a Lead Generation Workflow

Every lead workflow we have built, whatever the tool, uses the same five blocks. Get each one right and the platform matters much less.

1. A Trigger

The trigger decides when the workflow runs. A schedule (every Monday at 8am) suits a weekly list.

An event, such as "an export finished", "a form was submitted" or "someone registered for a webinar", suits leads that should move at once. A manual button is useful for testing.

2. A Lead Source

Where the leads come from. Inbound sources are forms, ads, demo requests and event sign-ups; outbound sources are LinkedIn Sales Navigator, a CRM view or a database.

Sales Navigator has the best B2B filters, but no export button, so the workflow needs a tool such as Evaboot to export the search with verified emails.

See proven methods to generate B2B leads for other sources.

3. A Filter and Enrichment Step

The filter removes the leads you would never contact: no email, a risky email, the wrong title or a company outside your target.

It runs before the score, so you do not pay to score leads you will throw away.

Enrichment fills what the source left out. An inbound form gives you a name and an email; the score needs the company's industry, size and location.

An Evaboot export already includes verified emails and company data, so our workflow skips this step.

4. A Score

The score ranks what is left against your ideal customer profile (ICP). Simple rules work ("Director or above AND 50+ employees"), but they break on every new job title.

An AI model reads "VP Revenue" and "Head of Sales" the same way you would.

Write the profile down first. A workflow cannot score against a profile that only lives in your head:

5. A Destination

Where the leads end up: a Google Sheet, a CRM or an outreach tool, often with a Slack alert to the rep who owns the lead.

A sheet is the easiest place to review a new workflow.

Once you trust the scores, send only the top band to your CRM, and look up the email first so a lead that already exists is updated, not duplicated.

Explore more automated lead list building solutions for other destinations.

n8n vs Make vs Zapier: Which Platform Should You Use?

All three can run the five blocks. They differ in how much logic they handle and how they bill you.

For ease of use, Zapier is the gentlest, Make sits in the middle and n8n has the steepest learning curve, especially self-hosted. For a wider list, see these lead generation automation tools.

Evaboot connects to all three from Integrations > Automations:

We built our example in n8n and Make, because they handle the loop over each lead without extra cost per step in n8n's case, and with a clear visual flow in Make's.

How to Build a Lead Generation Workflow in n8n: Step by Step

Our n8n workflow builds a weekly lead list from a Sales Navigator search.

Every Monday it exports the search with Evaboot, keeps the leads that match, scores each one against your ICP and adds them to a Google Sheet.

We ran it on n8n Cloud on a real search:

Heads and VPs of Sales at UK software companies with 51 to 500 employees who changed jobs recently.

Every number and screenshot below comes from that run.

1. Write Your ICP.md File

Split your profile into company must-haves, buyer must-haves, positive signals and disqualifiers. Copy the template below, or download ICP.md from GitHub, then replace every line with your own:

# Ideal customer profile

## Company must-haves

- Industry: B2B software (SaaS), IT services, or recruiting agencies.
- Size: 11 to 500 employees.
- Location: Europe or North America.
- Sells to other businesses, with an outbound sales team.

## Buyer must-haves

- Function: Sales, Business Development, Growth, or Revenue Operations.
- Seniority: Head of, Director, VP, C-level, or Founder.

## Positive signals (raise the fit, not required)

- Job title mentions outbound, prospecting, lead generation, or SDR team.
- Company is hiring SDRs or BDRs.
- Lead uses LinkedIn Sales Navigator.

## Disqualifiers (always No fit)

- Students, interns, job seekers, or freelancers with no team.
- Competitors: lead-data or email-finder vendors.
- Company with more than 5,000 employees.
- Government, education, or non-profit organizations.

Keep it under one page: the model reads the whole file for every lead.

2. Build a Focused Sales Navigator Search

Set your filters in Sales Navigator and copy the URL. Keep the search small: Evaboot exports the whole search and uses one credit per lead, so a 2,500-lead search costs 2,500 credits every week.

Add the "Changed jobs in the past 90 days" filter. It brings new people every week, and people new in a role are more open to new tools.

Our search returned 37 leads. For the export itself, see how to export leads from Sales Navigator.

3. Import the Template and Add Your Settings

First install the Evaboot node. It is a verified community node, so it also works on n8n Cloud: search "Evaboot" in the node panel and click Install node.

Copy the n8n JSON from the box at the top of this guide and paste it on an empty canvas, or download it and use Import from file.

Add three credentials:

Evaboot (API key from the API key page in Evaboot), OpenAI and Google Sheets.

Then open Your settings and fill in three fields: the Sales Navigator search URL, your ICP (paste ICP.md) and the Google Sheet URL.

Copy the sheet URL while the right tab is open, so it ends with gid=…:

The sheet needs one header row: LinkedIn URL, First name, Last name, Job title, Company, Company domain, Industry, Employees, Company location, Email, ICP score, ICP fit, Confidence, Added on.

4. Export the Search With Evaboot

The Export Sales Navigator search node sends your URL to Evaboot and gets an extraction ID back. The workflow then waits 2 minutes and asks Evaboot for the result:

Check the export returns the status. When it says EXECUTED, the leads are ready; if not, the Export finished? node loops back and waits again.

Our export of 37 leads was ready on the first check:

5. Keep Only Matching Leads With a Safe Email

Evaboot adds two columns that make filtering easy. Matches Filters says whether the lead still matches your Sales Navigator filters, and Email Status says whether the email is safe to send to.

The filter keeps a lead only if Matches Filters is YES and Email Status is safe. From our 37 leads, it kept 11 and discarded 26:

That sounds harsh, but those 26 had the wrong industry, the wrong title or a risky email. Scoring them would only cost money.

6. Score Each Lead Against Your ICP

The Score against ICP node calls the OpenAI Decisions API. A decision model answers a typed question with fixed levels and calibrated probabilities, so every lead is judged on the same scale.

A chat model would invent a new number each time.

The request sends the lead as the input and your ICP inside the question. The four levels go from lowest to highest:

{
  "model": "gpt-6-luna",
  "input": "Job title: ...\nCompany: ...\nCompany industry: ...\nCompany employees: ...\nCompany location: ...\nCompany description: ...",
  "questions": [{
    "type": "score",
    "name": "icp_fit",
    "instructions": "How well does this lead match the ideal customer profile below?\n\n<the content of ICP.md>",
    "levels": [
      {"label": "No fit", "description": "Fails a disqualifier, or matches none of the must-haves."},
      {"label": "Weak fit", "description": "Matches one must-have. The other must-haves fail or are unknown."},
      {"label": "Partial fit", "description": "Matches most must-haves, but one fails or is unknown."},
      {"label": "Strong fit", "description": "Matches every must-have and no disqualifier."}
    ]
  }]
}

For the Head of Sales Development at BlueOptima, a 111-person software company in London, the answer was a score of 2.24 out of 3, with Strong fit at 47% and Partial fit at 38%.

The call used 519 input tokens, so 1,000 leads cost about 5 cents:

Build the sheet row converts it: score ÷ 3 × 100 gives 75, and the nearest level gives the label, Partial fit.

7. Add the Leads to Google Sheets

The last node writes each lead to your sheet with Append or update, matched on the LinkedIn URL column.

A lead that is already in the sheet is updated, not added twice, so you can run the workflow every week without duplicates:

The whole run, from export to sheet, took 2 minutes and 7 seconds:

8. Publish It

Click Execute workflow once to test, check the sheet, then click Publish. The Every Monday at 8am trigger runs it from then on, and the Test run trigger stays available for manual runs:

How to Build the Same Workflow in Make: Step by Step

The Make version gives the same result with a different start. The Evaboot Make app can watch for finished exports, but it cannot start a Sales Navigator export.

So Evaboot runs the search as a weekly recurring export and pushes each run to a Make webhook.

We tested it on an export of 50 leads. The scenario ran in 29 seconds and used 92 Make credits.

On the Free plan (1,000 credits a month) that is about ten such runs.

1. Import the Blueprint

Download the Make blueprint from the box at the top of this guide. In Make, create a scenario, open the ⋮ menu and choose Import blueprint:

The scenario has five modules: Evaboot export finished (a webhook), One bundle per lead (an iterator), Build the OpenAI request, Score against ICP and Add to the lead sheet.

2. Fill in the Three Modules

Then open Add to the lead sheet, connect Google and pick your sheet and tab, with the same header row as above. Turn the scenario on.

3. Connect Evaboot to the Webhook

In Evaboot, open Integrations > New integration > Make. Name it, paste the webhook URL, tick Sales Navigator exports, and send leads from All recurring exports (or pick one recurring export):

Next, make your search recurring. Open Sales Nav Exports, click the export's row menu, choose Repeat automatically, pick Weekly and click Turn on recurring.

Each run then pushes its leads to Make:

To test the scenario once, push any finished export to the new integration with the + button in its row:

4. Check the Run

Open the scenario's History. Each module shows how many bundles it handled.

In our run, all 50 leads arrived, 30 passed the Matching leads with a safe email filter, and all 30 were scored and added to the sheet:

Click the count on Score against ICP to see what OpenAI returned. For the SVP of Sales at B2B Rocket, the answer was a score of 2, Partial fit:

And the count on Add to the lead sheet shows the exact row it wrote, with an ICP score of 67:

One difference between the two versions matters for weekly runs:

No-Code Lead Generation Example: 41 Scored Sales Leaders

Here is the sheet after both runs: 11 leads from n8n and 30 from Make, each with a score, a fit level and a confidence:

Five of those leads, and what we would do with each:

Across all 41 leads: 2 Strong, 31 Partial, 6 Weak and 2 not scored. The two not scored came back without a score, so the workflow wrote "Not scored" instead of guessing.

Two things stand out. Most leads above 75 are still "Partial fit", because the label is just the nearest level: sort the sheet by the ICP score, not by the label.

And the filter did more work than the score: it removed 26 of 37 leads in the n8n run before any AI was involved.

How to Turn the Lead List Into Actions

A scored list only helps if each band has a clear next step.

Each band gets its own next step:

1. Contact the Top Band First

Sort by ICP score and start with 75 and above. In our sheet that was 19 of 41 leads, a realistic week for one rep.

2. Check Low-Confidence Leads

A confidence below 0.3 means the model hesitated between two levels, often because the company description is short or vague. Open the profile before you write.

3. Look for a Better Contact

A Weak fit at a Strong-fit company is an account, not a dead lead. Find the sales leader at the same company and add them to the next run.

4. Send the Best Leads On

Once the scores match your judgement, add a step that sends only the top band to your CRM or outreach tool.

Evaboot can also push an export straight to HubSpot, Pipedrive or Instantly when you do not need the score.

Common No-Code Lead Generation Mistakes

Most broken lead workflows fail in one of four ways:

1. Exporting a Search That Is Too Wide

Evaboot exports every lead in the search, at one credit each. A broad search with 2,000 results uses 2,000 credits a week.

Narrow the filters until the search returns a list your team can actually work, and add "Changed jobs" to keep it fresh.

2. Scoring Before You Filter

Leads with a risky email or outside your filters cost the same to score as good ones. Put the filter first; in our test it cut the list by 70%.

3. Re-Running Single Steps in n8n

In n8n, Execute step on a node that reads Your settings re-runs the whole workflow, including a new export. We started three exports this way.

Test with the Test run trigger, then use the pinned data of the last run to adjust later nodes.

4. Letting Duplicates Pile Up

A weekly workflow sees the same people again.

In n8n, append or update on the LinkedIn URL keeps one row per lead; in Make, remove duplicates in the sheet or add a search step before you add a row.

Frequently asked questions