Automated WhatsApp Lead Nurturing with Personalized Messages via Postgres & Gallabox

Description

n8n Workflow: WhatsApp Lead Nurturing (MQL)

🔄 Purpose
This workflow fetches unqualified leads from Postgres at defined retry intervals, sends personalized WhatsApp template messages via Gallabox API, and logs message activity while updating lead status in the database.

🧩 Workflow Structure

Schedule Trigger
Type: n8n-nodes-base.scheduleTrigger
Runs the workflow automatically at set intervals (seconds-based).
Entry point of the workflow.

Execute a SQL query
Type: n8n-nodes-base.postgres
Fetches leads from mql_contacts that:
Have count = 0, 1, 2, or 3.
Respect time delays:
count=1 → after 3 minutes
count=2 → after 5 minutes
count=3 → after 8 minutes
Must have disposition = unqualified.

Loop Over Items4
Type: n8n-nodes-base.splitInBatches
Iterates over each lead individually.
Ensures one-by-one processing.

Code1
Type: n8n-nodes-base.code
Selects message content based on:
Lead’s model (nexus, magnus, reo, general).
Current count (0–3).
Ensures personalized, varied message text.

new_lead_4
Type: n8n-nodes-base.httpRequest
Sends WhatsApp template message through Gallabox API.
Dynamic fields:
Lead’s name and phone.
Message details selected from Code1.
Quick reply buttons: Show Brochure, Get Showroom Location, Not Interested.

Insert rows in a table4
Type: n8n-nodes-base.postgres
Inserts a record into mql_logs.
Captures:
Phone, name, disposition.
Message count, message_id, timestamps.
Gallabox API response (status, code).

Update rows in a table4
Type: n8n-nodes-base.postgres
Updates mql_contacts:
Increments count by 1.
Updates last_message_sent timestamp.
Matches using phone number.

Sticky Notes
Provide inline documentation:
SQL query purpose.
Message matrix explanation.
Gallabox API role.
Postgres update/logging details.

⚙️ Data Flow Summary

Trigger** → Runs every X seconds
SQL Query** → Fetches eligible leads (unqualified, retry intervals)
Loop** → Processes each lead sequentially
Code1** → Chooses personalized message based on model & count
HTTP Request** → Sends WhatsApp template via Gallabox
Insert Log** → Stores activity in mql_logs
Update Contact** → Updates count & last sent in mql_contacts
Cycle repeats** until all leads are processed

📊 Use Case
Automates WhatsApp drip campaigns for unqualified leads.
Respects retry intervals to avoid spamming.
Uses personalized message variations based on product model & retry count.
Provides full traceability with logs and lead updates.