An American multinational retail corporation wanted to develop an advanced solution for better end-user personalization to enhance digital journeys. The current challenges involved inventory management, supply chain intricacies, customer experience enrichment, workforce management, technology integration, and innovation. The goal was to improve operational efficiency, elevate the customer experience, and promote business growth, aiming to solidify their leadership position in the retail sector.
Techwish developed a robust customer personalization module focusing on:
- Infrastructure consultation
- Data extraction and intent prediction
- Content aggregation for improved personalization
Efficient Information Processing workflows were created to process the data stored in key areas and advanced algorithms were used to predict customer intent and refine personalization. The content aggregator transformed predictive model output into relevant content, incorporating filters and the ability to include advertising content.
The project team followed the Scaled Agile Framework® (SAFe) and involved two Release Train Engineering (RTE) teams. One RTE team focuses on internal functionalities, while the other oversees coordination with external teams such as the Search Team, W+ Team, Order Team, and Data Team. The Program Increment (PI) duration is monthly, with a sprint cycle of two weeks. Each RTE team consists of 17 members, including 5 Quality Assurance (QA) professionals, 5 Software Development Engineers (SDE), 2 RTE members, 3 DevOps specialists, 1 Business Analyst (BA), and 1 Project Manager (PM).
The project team used a technology stack that includes a Kubernetes Cluster with 20 nodes and AWS services such as EKS, S3, Athena, and Lambda. Java, Spring, Maven, and Docker are used for development, while Cassandra and Memcached handle data storage. Jenkins is used for continuous integration and continuous deployment (CI/CD) processes.
Techwish’s Advanced Customer Personalization module stands as a testament to our engineering prowess. Harnessing data-driven methods, it anticipates customer preferences, personalizing their digital experiences in real time. With predictive models generating insights and a content aggregator disseminating them, the solution provides real-time personalized recommendations. This transformative approach elevates the customer experience, maximizing efficiency, satisfaction, and sales.