How to Build Professional Network Automation Rule Engine with Python

#python #automation #tutorial

Processing hundreds of LinkedIn CSV exports manually while trying to identify quality leads is a time sink that breaks most recruitment workflows. A python rule engine can automate the scoring and filtering process, but building one from scratch takes hours that busy recruiters and sales teams don't have.

The Manual Way (And Why It Breaks)

Manually processing LinkedIn Sales Navigator exports means opening each CSV file, scanning through hundreds of profiles, and making subjective decisions about which contacts to prioritize. You spend time filtering out obvious mismatches like recruiters or irrelevant industries, then try to rank remaining prospects based on gut feelings about their titles, companies, and connection counts. This approach doesn't scale when you're dealing with multiple searches across different verticals, and human error creeps in when fatigue sets in during long screening sessions. The lack of consistent criteria means good leads get buried while you chase lower-priority prospects, killing your lead scoring automation efficiency and wasting valuable network automation tools time.

The Python Approach

This lightweight snippet handles basic CSV processing and simple keyword matching for initial lead filtering:

import pandas as pd
from pathlib import Path

def process_leads(input_file, output_file):
    # Load LinkedIn export CSV
    df = pd.read_csv(input_file)

    # Basic scoring based on title keywords
    title_keywords = ['director', 'vp', 'manager', 'cto', 'ceo']
    df['title_score'] = df['Job Title'].str.lower().apply(
        lambda x: sum(1 for kw in title_keywords if kw in str(x))
    )

    # Filter out recruiters and HR roles
    exclude_terms = ['recruiter', 'hr', 'human resources', 'talent']
    mask = ~df['Job Title'].str.lower().str.contains('|'.join(exclude_terms))
    filtered_df = df[mask]

    # Sort by connection count and title score
    result = filtered_df.sort_values(['Connection Count', 'title_score'], ascending=False)

    # Export cleaned results
    result.to_csv(output_file, index=False)
    print(f"Processed {len(result)} qualified leads")

This code provides basic python data filtering functionality for title-based scoring and simple exclusion rules. However, it lacks advanced features like company-specific filtering, location-based rules, and complex scoring logic that real recruitment scenarios require.

What the Full Tool Handles

• Load and parse LinkedIn Sales Navigator or Recruiter CSV exports with automatic field detection
• Define scoring rules based on title keywords, company, location, and connections

• Filter out profiles based on criteria like 'exclude recruiters' or 'only Fortune 500'
• Sort and rank the final list by a calculated priority score
• Export the processed results to a new, clean CSV file

The complete solution handles edge cases and complex business logic that a python rule engine needs for production use in recruitment automation workflows.

Running It

linkedin_rules --input leads.csv --config rules.json --output prioritized_leads.csv

The input flag specifies your LinkedIn export file, config defines your scoring rules in JSON format, and output creates the filtered results file sorted by priority score.