Retail | E-commerce
Product Search & Recommendation
Objective
The client’s customers were having difficulty finding the right products quickly and efficiently. This led to a poor shopping experience, reduced customer satisfaction, and, ultimately, reduced sales. The client needed an intelligent system to enhance product search and provide personalized recommendations.
Challenges
- Handling large volumes of product data.
- Understanding customer preferences and behavior.
- Providing real-time, accurate search results.
- Generating personalized recommendations.
Solution Proposed
- Utilized Azure Open AI for natural language processing to understand customer queries and improve search accuracy.
- Implemented customer classification/clustering to analyze customer behavior, preferences, and content-based filtering for personalized recommendations.
- Integrated with existing retail systems for seamless data flow and real-time updates.
Outcome
- Increased customer satisfaction by 25%, through more accurate and efficient product search.
- Boosted sales through personalized recommendations.
- Enhanced customer engagement with increased repeat visits.
Design & Thinking Wins
- Seamless integration with existing retail systems.
- A scalable solution capable of handling large volumes of data.
- Real-time processing and updates.
- Enhanced user experience with intuitive search and recommendation features.
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.
