Resolusi dan Deduplikasi Identitas Berbasis Pembelajaran Mesin

Resolusi dan Deduplikasi Identitas Berbasis Pembelajaran Mesin

Klien, sebuah perusahaan terkemuka yang beroperasi di sektor Teknologi, menghadapi tantangan operasional yang kritis karena saluran data dan inco yang terfragmentasi... Artha Solutions mengatasi hal ini dengan menerapkan kesamaan string fuzzy Terapan (misalnya, Jaro-Winkler, Levenshtein) di seluruh bidang nama, alamat, email, dan telepon. • Menggunakan pelatihan model klasifikasi ML... sehingga penerapan mencapai kesuksesan luar biasa, memberikan akurasi penyerapan data 100% dan menyederhanakan loop pelaporan. Ini menghasilkan pengurangan 40% ... & 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%.

IKHTISAR UTAMA
  • Industri Ritel & perdagangan elektronik
  • Daerah Fokus Data & Modernisasi AI
  • Klien Organisasi Perusahaan Besar
  • Wilayah Global
  • Teknologi
    Snowflake Salesforce ular piton Aliran udara

Hasil Utama & Dampak Bisnis

12%
Area Dampak Sebelum Sesudah Dampak Bisnis Profil Pelanggan Duplikat 8– dari CRM <1,5% ↑ Tampilan 360° yang akurat dari pelanggan Waktu Pembersihan CRM Manual 100+ jam/ bulan <30 jam/ bulan ↓ 70% menghemat waktu analis Tingkat Tumpang Tindih Kampanye ~18% <5% ↓ Pemborosan pembelanjaan iklan sebesar 70% Customer Lifetime Value (CLV) Akurasi Rendah Tinggi ↑ Presisi dalam segmentasi loyalitas Akurasi Personalisasi (email
60%
penawaran) ~ >90% ↑ RKT

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%.