1 . Google BERT based NLP/Open AI
2 . Snowflake deployment on top of Azure cloud
3 . Azure OpenAI (client secured environment)
4 . Python
5 . UI based on StreamLit and React.JS
The proposed solution addressed these challenges through a multi-faceted approach:
1 . Creating a Unified Data Model: We established a common data model that mapped product attributes across sales and inventory data, providing a centralized view of product information.
2 . NLP Engine with BERT: Utilizing Natural Language Processing (NLP) powered by Google’s BERT, we analyzed customer reviews and comments on Amazon’s online store, extracting valuable insights.
3 . Automated Product Attributes: We automated the extraction of product names and attributes based on Amazon searches, seamlessly mapping them to the client’s products and inventory.
4 . Identifying Trends and Competitive Insights: Our Solution identified key consumer fashion trends and highlighted gaps in the client’s offerings compared to competitors.
5 . Next Best Action Recommendations: Based on consumer demand analysis, we provided actionable recommendations to optimize product offerings
The implementation of our solution yielded impressive results:
1 . 80% Reduction in Manual Efforts: Automation significantly reduced the need for manual data tracking and analysis, allowing the client’s team to focus on more strategic tasks.
2 . Approx. $2.5 Million in Cost Savings: The streamlining of processes and improved demand-sensing capabilities translated into substantial cost savings, enhancing profitability.
3 . Reduced Demand Sensing Analysis Time: What used to take months for demand analysis now takes just days, enabling the client to respond swiftly to market trends and consumer preferences.