Who is the Client

A US-based Fortune 500 departmental store chain with more than 1000 stores across the states, they are bringing stylish clothing for the entire family since decades now.

The Challenge

The client is running multiple loyalty programs that have a total of 65+ million users and account for more than 70% of the sales. One of the loyalty programs is rewards points based and has 15+ million users. However, this loyalty program allows only the registered users who possess loyalty cards to accumulate points.

All the loyalty programs utilize TIBCO’s loyalty platform. However, with the growing user base, the TIBCO’s system started showing performance issues. The client wanted to develop a loyalty system based on a new reward mechanism. It will award the customers ‘cash points’ instead of ‘reward points’ that get adjusted in the next purchase within 30 days of the transaction.

This will eliminate the need for the customer to visit multiple portals as it combines features of all the loyalty programs. The new loyalty system needs to provide ease of redemption by eliminating the need to register or possess a loyalty card.

The Solution

We collaborated with the client to document and prioritize their requirements. The key tasks were:

Design and Development

  • Event-Driven/REST Services – supported a high-volume, high availability Loyalty application.
  • Quality Engineering – built in-sprint automation framework using Spring Boot, Cucumber, and Allure technologies to quickly certify the applications/services.
  • Application Access and Reporting – provided a UI based on React JS, Angular JS, and Node JS to configure multiple data sources, security filters, and user privileges.
  • Analytical Dashboards/Reports – depicted critical Key Performance Indicators (KPIs).
  • Logic for File Consumption from Google Storage bucket and Pub-Sub – stored records in MongoDB and generated events to be streamed into Kafka as messages.
  • CI/CD Deployment – automated application deployment into the cloud, including Google Cloud Platform (GCP) and OpenShift.

Additionally, we re-architected batch applications to improve the performance while simultaneously consuming large files from Google Cloud buckets. These batch jobs were run to allot points for the offline transactions made by the call center agents.

Application Monitoring and Maintenance

  • Automated application monitoring and failure notifications. Configured and customized Splunk dashboards.
  • Configured Kafka to monitor the flow of all event messages.
  • Automated diagnoses and recovery of IBM MQ, Active MQ, and Kafka Middleware errors.

Business Impact

The new loyalty system performance exceeded all benchmarks. It already has 10+ million users and the remaining 55 million users are expected to be migrated by Dec 2021.

  • The solution handles up to 150% of peak-load transactions compared to the previous app.
  • We automated the in-sprint testing during the development phase that reduced the certification time by 60%.
  • We also automated injection of golden data for testing and used Jenkins to achieve 100% test automation.
  • The solution supports batch automation, uploads files into GCP buckets, turns on/off specific features, and publishes reports in a comprehensible format.

Technologies Used

Apache Kafka. A unified, high-throughput, low-latency platform for handling real-time data feeds
Active MQ. An open-source message broker, written in Java together with a full Java Message Service (JMS) client
OpenShift. An open-source container application platform based on the Kubernetes container orchestrator for enterprise app development
Jenkins. An open-source automation server that helps in building, testing, deploying, and facilitating continuous integration and continuous delivery
Prometheus and Grafana. Application monitoring tools
Google Cloud Platform (GCP). A suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products
Languages. NodeJS, ReactJS, Java, and J2EE

Related Capabilities

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