If your team depends on outbound, partnership pipelines, or ICP-based prospecting, you already know the problem: getting leads is not the hard part, getting clean, operationally usable leads is. Most “lead scraping” setups stop at raw JSON, and someone still has to clean fields, align columns, and prepare the list for action.

This workflow solves that gap. It automates the full path from filter input to sheet-ready output using Dievio for data retrieval and Google Sheets for team-level execution. n8n is the orchestration layer, but the business value is in repeatability, speed, and cleaner handoff to sales.

If you are evaluating data source quality or API behavior, start with Dievio directly at dievio.com, then review endpoint specifics in the API overview.

Most lead generation stacks break in one of four places:

This template was designed to eliminate those failures. The same workflow can handle multiple campaign types, while keeping output structure stable for teams that need predictable operations.

The goal is simple: move from “we can fetch data” to “we can run reliable lead campaigns at scale.”

At a high level, the pipeline looks straightforward. In practice, each stage is configured for operational clarity.

The workflow starts with a webhook request that contains campaign parameters: sheet target, row limits, email settings, and filter logic.
This avoids editing internal nodes for each new campaign and makes the workflow reusable for different market slices.

The workflow calls Dievio search from a dedicated HTTP node.
The API key is configured in header (X-API-Key) inside the workflow node, so credentials are not passed around from external clients.

This is a cleaner security pattern and helps keep external trigger payloads focused on business parameters.

Raw API responses are transformed into stable fields that sales and growth teams can use immediately.
Instead of handing over nested payloads, the workflow produces predictable columns such as name, company, title, location, email fields, and source metadata.

The transformed rows are appended into your chosen spreadsheet tab.
For many teams, this is the fastest operational endpoint because Sheets is already used for campaign planning, list review, assignment, and QA.

The workflow returns a JSON summary with request ID, write status, and row count.
This makes debugging easier and allows chaining into notifications, logs, or further automations.

When this flow is implemented correctly, you get more than API integration. You get a usable lead system.

This is exactly what growth teams need when running multiple ICP hypotheses in parallel.

The same template can serve multiple operating modes depending on your input filters.

Run focused pulls around titles like Founder, CEO, or Owner in priority regions.

Use one shared workflow and change only request payloads for different clients or verticals.

Standardize lead inflow into one sheet schema before enrichment, scoring, and CRM push.

Quickly test segments by geography, role, and business model without rebuilding workflow logic.

A strong practice is to treat payloads as campaign contracts.
Instead of embedding technical complexity in request builders, keep them expressive and business-readable: target market, row caps, email strictness, and role filters.

That makes handoff from marketing/sales to automation owners much simpler.

Example structure usually includes:

Without normalization, even good data creates process friction.
With normalization, every downstream step becomes faster: deduplication, assignment, scoring, enrichment QA, and CRM import.

The workflow standardizes common fields including:

That structure is what turns lead collection into a repeatable process instead of ad hoc extraction.

From a search perspective, this workflow naturally maps to high-intent commercial/operational queries:

This matters because users searching those terms are usually close to implementation.
A workflow page that explains setup, data path, and practical usage can attract qualified traffic with stronger conversion potential than generic “automation tips” content.

To avoid common runtime issues in production:

These small controls reduce support overhead when multiple teammates run the same flow.

Once base flow is stable, you can extend this into a stronger lead ops stack without changing core architecture.

The key is to preserve the current contract: clean request in, clean rows out.

If you are evaluating data source fit, start with the main product at dievio.com.
For integration details, use the official docs at docs.dievio.com/api-reference/overview, including authentication, search payloads, filters, and pagination behavior.

You can import and adapt the full template from:
github.com/hundevmode/n8n-dievio-b2b-lead-collector-template

This is not just an API connector. It is a practical B2B lead operations template: campaign-driven input, normalized output, and direct Google Sheets delivery with minimal manual overhead. If your objective is scalable outbound execution, this structure gives you a stable base you can reuse and extend across segments, clients, and growth cycles.