CLIENT OVERVIEW

Pan-European IT Company with a Mobility Application

The client is a European technology pioneer recognized for developing the continent's first integrated mobility platform. This innovative platform combines several distinct transportation services into a single, unified application. The services integrated within the platform include:

Ride-hailing
Car rentals
Bike sharing
Intercity and intracity transport services

By consolidating these offerings, the client has successfully eliminated the need for users to rely on multiple, disparate service providers and streamlined local and cross-city travel for users throughout Europe.

PROJECT REQUIREMENTS

Enterprise-Scale Company & Contact Data Classification and Validation

The core objective of this project was to support the client's outreach program for European market expansion by delivering a foundation of clean, structured, and current contact data. We were given two precise, high-volume goals:

Company Classification (61,000 Records)

Classify companies using NAICS (North American Industry Classification System) categories, referencing Microsoft Industry Codes to ensure consistent, accurate, and relevant categorization.

Contact Detail Validation (59,000+ Records)

Verify and update the contact data of prospects (like full name, current job titles, department, official email, direct phone numbers, valid company affiliations, etc.), ensuring immediate outreach relevance.

Project Challenges

Ensuring Accurate Data Processing Under Strict QC Standards and Tight Timelines

This large-scale data management project demanded a delicate balance between high data integrity, stringent security, and complex legal compliance—all under intense time pressure.

High Volume & Tight Deadlines: The sheer scale of the data—exceeding 120,000 records and continuously growing—combined with an extremely tight timeframe, created an operational hurdle. Every record required meticulous classification and verification with zero tolerance for errors, meaning the team had to achieve high throughput without sacrificing precision.
Data Quality Issues: A significant portion of the dataset contained incomplete or irregular information — special characters in company or contact names, missing or partial phone numbers, invalid email formats, and inconsistent address formatting.
Outdated Contact Information: It was particularly challenging to verify professional details and update obsolete ones, such as job titles and organizational affiliations. To ensure accuracy, records necessitated updates only from trusted external sources, such as LinkedIn and official company websites.
Multi-Source Verification Necessity: Relying on a single data point proved inadequate for the required accuracy level. Maintaining true data integrity mandated a rigorous multi-source verification process, requiring systematic cross-referencing across several disparate databases (LinkedIn, ZoomInfo, business directories, etc.) before a record could be deemed reliable.
GDPR & Compliance Navigation: Processing a cross-national dataset brought the challenge of adhering to diverse regulatory landscapes. The project required strict navigation of data protection regulations, particularly the GDPR, ensuring absolute adherence to data protection and consent standards across all relevant jurisdictions.

OUR SOLUTION

Implementing a Multi-Step Framework for Data Cleansing, Classification, and Validation

To successfully address the client’s comprehensive data requirements, a structured process was implemented, governed by a detailed Standard Operating Procedure (SOP), with a dedicated team of data specialists and subject matter experts.

Data Cleansing and Web Research

The initial data cleansing phase focused on eliminating structural flaws and validating basic entity information:

Deduplication: All duplicate entries were rigorously identified and removed to prevent conflicts in downstream processes.
URL Validation: Manual Google searches were conducted to verify official company URLs, which were promptly updated in the database.
Source Cross-Verification: Industry details and physical addresses were cross-verified against the information found on official company websites and LinkedIn profiles.
Flagging Inactive Entities: As per client instruction, companies that had been dissolved were clearly flagged in a separate column for immediate exclusion.

Company Data Classification

Once the company entities were validated, we standardized the data and then classified the entries as instructed.

Each company was assigned the appropriate NAICS code. This classification relied on the client-provided Microsoft Industry Codes as the authoritative reference.
This process created a consistent classification system, ensuring that businesses were accurately mapped to their respective industry sectors, regardless of their geographic location.

Contact Data Verification and Enrichment

This critical step focused on ensuring the accuracy and relevance of the individual contact records:

Using advanced tools such as ZoomInfo, Apollo, and LinkedIn, along with targeted manual research, our team successfully verified and updated all contact records.
For employees who had departed, we recorded their last known job titles, along with detailed contextual notes about their roles and responsibilities, in a dedicated “Comment” field.
Throughout the data collection process, we maintained strict adherence to the terms of service and all relevant data privacy regulations for each data source.
Every contact record was enriched with valuable metadata—such as inferred company size, industry code, and potential decision-making capacity for each prospect—to maximize future data usability.

List Building for Effective Outreach

The clean, classified data was then leveraged to create actionable intelligence:

Based on the finalized NAICS classifications, our team created custom lists comprising qualified companies and their verified contacts.
These lists were segmented to facilitate targeted marketing, partnership exploration, and direct outreach.
By filtering the data based on key criteria such as industry, job role, employee count, and geography, the client was able to tailor campaigns more effectively to specific audiences and needs.

Edge Case Management and QA

The final stage involved meticulous review to handle anomalies and confirm final adherence to standards:

These complex edge cases (often related to special characters, incomplete fields, or inconsistent formatting) were manually investigated, corrected, validated, and standardized.
A mandatory, final quality assurance check confirmed that the entire resulting dataset perfectly met the client’s strict quality guidelines and project requirements.

Collaborative Human-AI Solution for Data Management

To handle the scale, complexity, and sensitivity of the client’s dataset, we implemented a human-in-the-loop framework that augments automation with expert human oversight.

Automation for Speed and Consistency

Automated workflows accelerated the processing of tens of thousands of records by enabling rapid data collection, scraping, and initial matching, while maintaining uniform formatting and reducing manual effort.

Human Expertise for Contextual Accuracy

All critical decisions—such as updates to company status, classification changes, or sensitive contact information—were reviewed by subject matter experts, minimizing errors, ensuring strong compliance, and delivering highly trustworthy datasets aligned with the client’s operational requirements.

Data Classification, Verification, & Enrichment for 120K+ Company and Prospect Records for a Mobility App