CRM Lead Data Cleaner – Email/Phone Validator + Dedup

Turn messy CSV and Excel lead data into clean, standardized, CRM-ready records.

CRM Lead Data Cleaner normalizes phone numbers, cleans and validates email formats, removes duplicate records, standardizes text fields, and assigns lead-quality scores.

Built for lead generation, CRM imports, outreach workflows, and data automation pipelines.

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What It Does

The Actor processes messy lead datasets and produces structured records that are easier to import into CRMs and downstream automation systems.

Key capabilities:

• Normalize phone numbers
• Recover phone numbers stored in Excel scientific notation
• Clean and validate email syntax
• Remove known placeholder or invalid email values
• Remove duplicate records
• Normalize column headers
• Clean text formatting
• Score email and phone data quality
• Assign HIGH, MEDIUM, or LOW lead-quality classifications
• Process CSV and Excel files
• Generate structured Apify Dataset output
• Generate a cleaned CSV file for downstream use

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Quick Start

Provide either:

1. A public CSV/XLSX file URL, or
2. An uploaded file key

Do not provide both in the same run.

Example

{
  "file_url": "https://example.com/leads.csv",
  "max_rows": 1000
}

The Actor downloads the dataset, cleans the records, removes duplicates, calculates quality scores, and writes the processed leads to the Apify Dataset.

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Input

file_url

Public URL of the CSV or Excel dataset to process.

Example:

https://example.com/leads.csv

The URL must be accessible without authentication.

file_key

Key of a file available through the Actor's key-value storage workflow.

Use this instead of file_url when processing an uploaded file.

max_rows

Maximum number of cleaned rows to process.

Example:

{
  "max_rows": 1000
}

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Supported File Formats

The Actor supports:

• CSV
• XLSX
• XLS

The file-loading pipeline selects the appropriate parser based on the supplied file type.

Excel files are processed using the Excel parser, while CSV files use the CSV parsing pipeline.

This prevents binary Excel workbook data from being incorrectly decoded as UTF-8 CSV text.

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Data Cleaning

Phone Numbers

Phone fields are detected automatically when the column name contains phone.

The Actor:

• Removes non-numeric formatting
• Normalizes phone values
• Handles numbers stored using scientific notation
• Rejects values with fewer than 7 digits
• Produces normalized phone output

Example input:

9.171234567E+09

Normalized output:

'+9171234567

The leading apostrophe helps preserve the phone number as text when opened in spreadsheet applications.

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Email Cleaning

Email fields are detected automatically when the column name contains email.

The Actor:

• Trims whitespace
• Converts email addresses to lowercase
• Checks basic email syntax
• Removes empty values
• Rejects known placeholder values such as dummy, test, noemail, and not_unlocked

Example:

JOHN@EXAMPLE.COM

becomes:

john@example.com

Important

Email validation is syntax-based.

The Actor does not verify mailbox existence, perform SMTP verification, or guarantee that an email address can receive messages.

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Duplicate Removal

Duplicate records are removed after normalization and cleaning.

This helps reduce repeated CRM entries and duplicate outreach records.

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Header Normalization

Column names are automatically standardized.

For example:

First Name
Phone Number
Email Address

become:

first_name
phone_number
email_address

Headers are:

• Converted to lowercase
• Trimmed
• Converted to underscore-separated field names

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Lead Quality Scoring

The Actor calculates three scoring fields.

email_score

1.0 = valid email syntax
0.0 = missing or invalid email

phone_score

1.0 = 10 or more digits
0.5 = 7–9 digits
0.0 = missing or invalid phone

overall_score

Calculated from:

(email_score + phone_score) / 2

The final record is classified as:

HIGH    overall_score >= 0.8
MEDIUM  overall_score >= 0.5
LOW     overall_score < 0.5

These scores provide a simple way to prioritize records based on the completeness and basic validity of their contact information.

They are data-quality indicators, not guarantees that a lead will respond or convert.

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Output

Each cleaned lead is written to the Apify Dataset.

Example:

{
  "first_name": "John",
  "last_name": "Smith",
  "phone_number": "'+9171234567",
  "email_address": "john@example.com",
  "address": null,
  "city": null,
  "facebookprofileurl": null,
  "email_score": 1,
  "phone_score": 1,
  "overall_score": 1,
  "quality": "HIGH"
}

Standard Output Fields

Field	Description
first_name	Lead first name
last_name	Lead last name
phone_number	Normalized phone number
email_address	Cleaned email address
address	Address when available
city	City when available
facebookprofileurl	Facebook profile URL when available
email_score	Email quality score
phone_score	Phone quality score
overall_score	Combined contact-data score
quality	HIGH, MEDIUM, or LOW

Missing optional values are returned as null.

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Cleaned CSV

In addition to Dataset output, the Actor generates a cleaned CSV representation of the processed dataframe in key-value storage under:

cleaned_dataset

This can be useful for spreadsheet workflows, exports, or downstream processing.

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Error Handling

The Actor uses controlled failure handling for common operational problems.

Examples include:

missing_input
invalid_input
file_not_found
download_failed
invalid_file

This makes failures easier to diagnose in automated workflows.

For example, providing neither file_url nor file_key produces a missing_input failure, while providing both produces invalid_input.

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Use Cases

CRM Data Preparation

Clean lead lists before importing them into CRM platforms.

Lead Generation Pipelines

Place the Actor between a scraper and your CRM:

Scraper
   ↓
CRM Lead Data Cleaner
   ↓
CRM / Outreach / Automation

Sales Outreach

Standardize contact information and remove duplicate records before outreach.

Spreadsheet Cleanup

Repair common formatting problems in CSV and Excel lead files.

Automation Workflows

Use the structured Dataset output in Apify integrations, APIs, or external automation workflows.

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Typical Workflow

Raw CSV / Excel
       ↓
File Detection
       ↓
CSV / Excel Parser
       ↓
Header Normalization
       ↓
Text Cleaning
       ↓
Email + Phone Processing
       ↓
Duplicate Removal
       ↓
Quality Scoring
       ↓
CRM-Ready Dataset

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Pricing

This Actor uses pay-per-result pricing.

1 cleaned dataset row = 1 result

You pay based on the number of results produced, subject to the current pricing displayed on the Actor's Apify Store page.

Use max_rows to control how many records are processed in a run.

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Limitations

CRM Lead Data Cleaner focuses on deterministic data cleaning and basic contact-data validation.

It does not currently:

• Verify whether an email mailbox actually exists
• Perform SMTP verification
• Verify whether a phone number is active
• Identify the phone carrier or line owner
• Enrich missing contact information from external sources
• Bypass authentication to access private files

Input URLs must be publicly accessible unless the file is supplied through the supported uploaded-file workflow.

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Privacy and Data Handling

Lead datasets can contain personal or business contact information.

Use this Actor only with data you are authorized to process and in accordance with applicable privacy, data-protection, outreach, and platform requirements.

The Actor's Sentry configuration disables default PII collection:

send_default_pii=False

Secrets and credentials should never be embedded directly in public dataset URLs or source files.

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Built for Automation

CRM Lead Data Cleaner is designed to work as a reusable data-processing stage rather than only as a standalone cleaning tool.

A typical production pipeline can look like:

Web Scraper
    ↓
Raw Lead Dataset
    ↓
CRM Lead Data Cleaner
    ↓
Validated / Scored Dataset
    ↓
CRM
    ↓
Outreach or Automation

This makes it suitable for lead-generation systems, sales operations, data pipelines, and automated CRM workflows.

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Reliability

The Actor includes:

• Explicit input validation
• Separate CSV and Excel parsing paths
• Controlled download handling
• Structured parsing failure handling
• Deterministic cleaning rules
• Duplicate removal
• Structured Dataset output
• Lead-quality scoring
• Production error observability

The goal is simple:

Turn messy lead data into predictable, automation-ready output.