This article provides a complete, practical walkthrough of the Pipedrive Stickynote Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Ideal Lead Enrichment in Pipedrive Using n8n and Datagma
   
   Meta Description:
   Discover how to automate your B2B lead enrichment and qualification process using n8n, Pipedrive, and Datagma. Learn how this workflow populates company and contact data, identifies ideal buyers, and enhances your CRM pipeline.
   
   Keywords:
   lead enrichment automation, Pipedrive workflow, n8n integration, Datagma API, ideal customer profile, B2B lead scoring, CRM automation, marketing automation, sales automation, contact enrichment, Pipedrive lead generation
   
   Third-Party APIs Used:
   
   1. Datagma API
   2. Pipedrive API
   
   Article:
   
   Automating B2B Lead Enrichment with Pipedrive, n8n, and Datagma
   
   In today’s competitive B2B landscape, getting high-quality leads into your CRM isn’t enough—you need context-rich data to prioritize your time and efforts. That's where workflow automation platforms like n8n shine. By integrating tools like Pipedrive and Datagma into an automated system, businesses can streamline lead enrichment, qualify leads using custom criteria, and maintain a clean, accurate CRM. This article breaks down a powerful n8n workflow that does exactly that.
   
   Overview: The Power of Combining Pipedrive + Datagma via n8n
   
   This workflow is designed to automatically enrich, assess, and populate Pipedrive CRM with enriched company and contact data from Datagma. It triggers the moment a new company is added in Pipedrive, fetches extensive data on that company and its relevant employees from Datagma, identifies ideal buyers based on predefined personas, enriches contact details, and adds them back into Pipedrive—fully automatically.
   
   Let’s unpack how each component works.
   
   1. Trigger: When a New Company is Created in Pipedrive
   
   The journey begins with the “Pipedrive Trigger - New Company Created” node. This node listens for an "organization added" event in Pipedrive, meaning whenever a new company is created, it automatically sets off the rest of the workflow.
   
   2. Enrich Company Information with Datagma
   
   Once triggered, it moves to “Datagma - Enrich Company,” which sends an HTTP request to Datagma’s enrichment API. This query pulls full information about the company, including premium data such as:
   
   - Total funding amount
   - Website traffic
   - Industry tags
   - Number of employees
   - HQ address
   - Relevant employee data (filtered by job titles)
   
   Pro tip: You can tailor the employeeTitle parameter in this request to focus on your ideal buyer persona, such as “Head of Marketing” or “Director of Sales.”
   
   3. Update Pipedrive Company Profile
   
   The enriched company data is then passed into the “Pipedrive - Enrich Organization” node, which updates the newly created organization in Pipedrive with custom properties, such as:
   
   - Total Funding Amount
   - Website Traffic
   - LinkedIn URL
   - Number of Employees
   - Address
   - Industry
   - Company Website
   
   Using Pipedrive’s custom fields and property IDs, this step ensures every lead in your CRM carries rich context without manual data entry.
   
   4. List and Filter Employees
   
   Next up, the “list people” node extracts the employees returned from Datagma. The workflow loops through these to evaluate each one using the “If lead is Ideal Buyer” node.
   
   This filter uses two criteria:
   - Their job title contains "sales"
   - Their score (employeeCompanyScore) is higher than 0.1
   
   This is a basic lead scoring mechanism that allows you to assess whether an individual fits your Ideal Customer Profile (ICP).
   
   5. Advanced Contact Enrichment with Email Lookup
   
   For each ideal lead, an additional request is made using the Datagma “Find Emails” API. This attempts to locate an email address based on the person’s name and company.
   
   Once found, the workflow moves into “Prepare Data Before Merge,” which standardizes the key information needed for person creation in Pipedrive (LinkedIn URL, enriched email).
   
   6. Data Merge & Contact Insertion into Pipedrive
   
   The “Merge” node combines the contact data and email information by matching on LinkedIn URLs. After merging:
   
   - “Pipedrive - Add Person” node is triggered to create a new contact in your Pipedrive environment.
   - This contact is linked to the previously enriched company and contains:
     - Name
     - Job Title
     - Email
     - LinkedIn URL
   
   Custom properties are used to map these details appropriately in your CRM.
   
   Bonus: Workflow Flexibility
   
   The workflow is designed with flexibility in mind:
   
   - Want different job titles? Just change the employeeTitle parameter in the "Datagma - Enrich Company" node.
   - Have a unique ICP? Adjust the filtering logic in “If lead is Ideal Buyer” to reflect your own conditions, like job seniority or department.
   - Need specific data fields? Customize the custom properties mapping in both organization and person nodes to match your Pipedrive configuration.
   
   Conclusion
   
   This dynamic workflow is a powerful example of how integrating n8n with Datagma and Pipedrive can save time, improve data quality, and deliver custom lead scoring logic—all in real time. Whether your sales team is scaling or you're just tired of manual research, this setup leverages modern API-based automation to give your CRM superpowers.
   
   Instead of typing and guessing, now your Pipedrive speaks fluently in data—and it all starts with one trigger.
   
   Start automating your workflows today by building on this or customizing it for your business. All you need are the right APIs, a bit of configuration know-how, and the desire to turn data into action.
   
   —
   
   If you found this useful, consider exploring more of what n8n automation can offer—your pipeline will thank you.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.