Retail | E-commerce
Product Catalog Management
Objective
The client faced challenges managing large and complex product catalogs, ensuring accurate and consistent product information, and providing customers with relevant search and discovery experiences.
Challenges
- Inaccurate and Inconsistent Data: Product information may be incomplete, inaccurate, or inconsistent across different channels.
- Manual Tagging: Manual tagging of products can be time-consuming and error-prone.
- Limited Search and Discovery: Customers may struggle to find the products they are looking for due to poor search functionality and inadequate product tagging.
Solution Proposed
- Azure Open AI-based Natural Language Processing (NLP) techniques, like text classification and keyword extraction.
- Data Warehouse: Built a data warehouse to store and manage product data.
- ETL Processes: Developed ETL (Extract, Transform, Load) pipelines to extract data from sources and ensure data quality and consistency throughout the product catalog.
- Dashboarding: Created interactive dashboards.
- API Integration: Developed APIs to integrate the product catalog management system.
Outcome
- Improved product discovery leading to 20% increase in customer satisfaction with product search results.
- Increased sales due to improved product visibility and discoverability.
- Reduced operational costs, reducing time spent on manual tagging and categorization.
- Improved data quality and consistency in product catalogs.
Design & Thinking Wins
- Automation: Automated tagging and categorization processes reduce manual effort and improve efficiency.
- Accuracy: Machine learning models can improve the accuracy of product tagging and categorization.
- Scalability: The solution can handle large and complex product catalogs.
- Data-Driven Insights: BI dashboards provide valuable insights into product performance and customer behavior.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
