Remove Duplicates and Enrich Lead Data

Sales teams rely on accurate and up-to-date CRM data to close deals and nurture leads. However, manually cleaning data and identifying duplicates can be time-consuming. What if there was a way to automate these tasks, so your team could focus on what really matters—selling?

With Protocols, you can automate CRM data cleaning, remove duplicate leads, and enrich your data for more targeted outreach. This guide will show you how to clean your CRM data, remove duplicates, and gain daily insights with minimal effort.

MongoDB can be used to securely store and manage your enriched lead data, enabling easy access to real-time insights.

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The Problem: Dirty Data Slows Sales

Sales teams often struggle with:
❌ Duplicates and outdated lead information
❌ Manual data entry and cleaning
❌ Missed opportunities due to incomplete or incorrect data

Time Wasted Without Automation

Sales teams often spend 5 hours per week manually cleaning CRM data and removing duplicates. At $30/hour, that’s $600 per month spent on repetitive data maintenance tasks that could be automated.

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The Solution: Automating CRM Data Cleaning and Lead Enrichment with Protocols

With Protocols, you can:
✅ Automatically remove duplicate leads from your CRM
✅ Cleanse data to ensure consistency and accuracy
✅ Enrich lead data (e.g., filling missing fields with public data)
✅ Track daily insights into your CRM data’s quality and trends

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Step 1: Load CRM Data for Cleaning

First, let’s load the CRM data from an Excel or CSV file. This could contain leads with various details such as name, email, phone number, and company name.

Example CRM Data (crm_leads.csv)

Lead ID

Name

Email

Phone

Company

1

John Doe

johndoe@email.com

555-1234

Acme Corp.

2

Jane Smith

janesmith@email.com

555-5678

Beta Inc.

3

John Doe

johndoe@email.com

555-1234

Acme Corp.

Let’s load this data into Python.

import pandas as pd

# Load CRM data
crm_data = pd.read_csv("crm_leads.csv")

# Display first few rows
print(crm_data.head())

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Step 2: Remove Duplicate Leads

To remove duplicate leads based on specific fields (e.g., email and phone number), we can use Python’s drop_duplicates() function.

# Remove duplicate leads based on 'Email' and 'Phone'
cleaned_data = crm_data.drop_duplicates(subset=["Email", "Phone"])

# Display cleaned data
print(cleaned_data)

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Step 3: Enrich Lead Data

We can enrich the data by filling in missing details, like company size or industry, using external data sources or a predefined set of rules.

# Enrich data by filling missing company size (example)
cleaned_data["Company Size"].fillna("Unknown", inplace=True)

# Display enriched data
print(cleaned_data)

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Step 4: Track Data Insights

With each data cleaning session, we can track key metrics, such as the number of duplicates removed and the percentage of leads enriched. This gives you real-time insights into how your CRM data is improving.

# Track insights
total_leads = len(crm_data)
duplicates_removed = len(crm_data) - len(cleaned_data)
enriched_leads = cleaned_data["Company Size"].isna().sum()

# Display daily insights
print(f"Total Leads: {total_leads}")
print(f"Duplicates Removed: {duplicates_removed}")
print(f"Enriched Leads: {enriched_leads}")

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Step 5: Store Cleaned Data in MongoDB

To manage your leads and track changes over time, you can store the cleaned data in MongoDB. This allows your sales team to access updated lead information quickly.

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')
db = client['sales_data']
collection = db['leads']

# Insert cleaned data into MongoDB
collection.insert_many(cleaned_data.to_dict("records"))

print("Cleaned lead data saved to MongoDB.")

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Step 6: Generate Insights and Reports

After cleaning and enriching your CRM data, you can generate reports showing the number of leads processed, duplicates removed, and the effectiveness of enrichment strategies.

# Calculate insights
cleaned_leads_count = len(cleaned_data)
duplicate_removal_percentage = (duplicates_removed / total_leads) * 100
enrichment_percentage = (enriched_leads / total_leads) * 100

# Display insights
print("CRM Data Cleaning Insights: ")
print(f"Total Cleaned Leads: {cleaned_leads_count}")
print(f"Duplicate Removal: {duplicate_removal_percentage:.2f}%")
print(f"Enrichment Rate: {enrichment_percentage:.2f}%")

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The Result: Streamlined CRM Data and Daily Insights

By automating CRM data cleaning and enrichment, sales teams can:
✅ Save 5 hours per week, worth $600/month
✅ Remove duplicates and maintain a cleaner, more accurate database
✅ Track daily insights into data quality, allowing for continuous improvement

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I Can Automate This, So You Can Focus on Closing Deals

With Protocols, CRM data cleaning and enrichment become effortless. I can build a custom automation solution tailored to your CRM, ensuring accurate, up-to-date leads that improve your sales efforts. Let me handle the data, so your sales team can focus on closing more deals.

Contact me today to get started!