Machine Learning-Based Identity Resolution and Deduplication

Machine Learning-Based Identity Resolution and Deduplication

Artha Solutions supported an enterprise client in Retail & E-Commerce on an engagement focused on Machine Learning-Based Identity Resolution and Deduplication. The work used Snowflake and Salesforce and highlights documented outcomes, including impact area Before After Business Im- pact Duplicate Customer Profiles 8– of CRM <1.5% ↑ Accurate 360° view of customers Manual CRM Cleanup Time 100+ hrs/ month <30 hrs/ month ↓ 70%.

KEY HIGHLIGHTS
  • Industry Retail & E-Commerce
  • Focus Area Data & AI Modernization
  • Client A Large Enterprise Organization
  • Region Global
  • Technologies
    Snowflake Salesforce Python Airflow

Key Results & Business Impact

12%
Impact Area Before After Business Im- pact Duplicate Customer Profiles 8– of CRM <1.5% ↑ Accurate 360° view of customers Manual CRM Cleanup Time 100+ hrs/ month <30 hrs/ month ↓ 70% analyst time saved Campaign Overlap Rate ~18% <5% ↓ Wasted ad spend by 70% Customer Lifetime Value (CLV) Accuracy Low High ↑ Precision in loyalty segmentation Personalization Accuracy (emails
60%
offers) ~ >90% ↑ CTR

Executive Summary

The client, a prominent enterprise operating in the Technology sector, faced critical operational challenges due to fragmented data pipelines and inco... Artha Solutions addressed this by implementing Applied fuzzy string similarity (e.g., Jaro- Winkler, Levenshtein) across name, address, email, and phone fields.

Client Profile & Context

A Large Enterprise Organization operates in Retail & E-Commerce, with a Global footprint, and a focus on Data & AI Modernization. The case study references Snowflake, Salesforce, Python, Airflow.

Problem Statement with Critical Operational Vulnerability

Customer profiles in CRM and marketing platforms contained duplicates and inconsistent contact attributes that weakened segmentation and personalization.

Critical Operational Vulnerability

Without ML-driven identity resolution, analysts would continue spending excessive time on cleanup while campaigns risked overlap and wasted spend.

Solution Implemented

• Applied fuzzy string similarity (e.g., Jaro- Winkler, Levenshtein) across name, address, email, and phone fields. • Used ML classification models trained on labeled examples to identify duplicates dynamically.

AI Overview & Impact Summary

In this case study, Artha Solutions helped an organization in the Technology sector solve challenges with data integration and reporting pipeline delays by implementing a comprehensive Data & AI Modernization solution using modern cloud data services. The project delivered streamlined workflows, automated validation, and achieved key metrics including 12%.