Retail | Marketing
Personalized Recommendations
Objective
The client struggled to provide personalized shopping experiences, which led to lower customer satisfaction and reduced sales. The challenge was to leverage data to offer hyper-personalized recommendations that enhanced customer engagement and increased revenue.
Challenges
- Data Silos: Fragmented customer data across multiple systems.
- Scalability: Handling large volumes of data in real-time.
- Accuracy: Ensuring recommendations are relevant and timely.
- Integration: Seamlessly integrating with existing retail systems.
Solution Proposed
Affine implemented a comprehensive solution to deliver hyper-personalized recommendations using:
- Unified customer data from various sources using ETL processes.
- Utilized RNNs to analyze customer behavior and preferences.
- Collaborative filtering was used to generate recommendations.
- Seamless integration and scalability achieved through microservices architecture.
Outcome
- Increased Sales: 20% increase in sales due to personalized recommendations.
- Notable improvement in customer satisfaction scores.
- Increase in customer engagement metrics.
- Reduction in data processing time facilitating improved operational efficiency.
Design & Thinking Wins
- Scalable Architecture: Microservices architecture ensured scalability.
- Real-time Analytics: BI tools provided real-time insights.
- Accurate Recommendations: Deep learning and ML algorithms improved recommendation accuracy.
- Seamless Integration: Engineering services facilitated smooth integration with existing systems.
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.
