General Information:
| Industry: | Country: |
|---|---|
| E-Commerce | USA |
| Platform: | Website: |
| Web, Mobile | - |
Key Challenges:
Increasing customer engagement and conversion rates: Business faced the challenge of attracting and converting visitors into paying customers because of unoptimized website content and sophisticated UX/UI model. Poor product recommendation system: developers of the platform used poor vanilla ML model for segmentation customer by groups. There was very poor customer support required automation.
Solution Highlights:
Improvement of user experience: Our ad-hoc solution helped to attract new customers as well as new categories of customers (younger ones). Customer are satisfied with the new tool which allows to add and cancel orders just in two clicks. Personalized product recommendations: Our integrated AI-powered recommendation engine improved the former one and analyzes customer data of purchase history to provide tailored recommendations based on individual preferences. Sentiment analysis for product reviews: We implemented our custom NLP model to analyze customer queries and reviews to be able to identify positive, negative, and neutral sentiments, providing valuable insights for product development and marketing strategies.
Results:
Increased customer engagement. Improved conversion rates. Reduced customer support costs. Enhanced supply chain efficiency.
Technologies used:
- AWS
- PyTorch
- HuggingFace
- LangChain
- Pinecone
- Python
- Django
- JS
- K8s
- Docker
- DVC
- Jupyter
- scikit-learn
- pandas
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