Traya is India’s first-ever hair fall solutions company that addresses hair loss from within, combining the goodness of three sciences. With ingredients from the most authentic sources, Traya personalizes treatments to ensure effective results.
Challenges
Solutions
Traya faced significant challenges in managing and analyzing its customer engagement and transactional data efficiently. With data spread across MongoDB and PostgreSQL, the company lacked a centralized analytics solution to gain real-time insights into agent performance, customer interactions, and call analytics. The absence of real-time data validation and a unified dashboard made it difficult to track critical business metrics, impacting decision-making and overall operational efficiency. Additionally, the existing infrastructure was not scalable enough to handle increasing data loads effectively.
Brio Technologies provided technical guidance and support, assisting Traya in implementing a robust Data Analytics solution on Google Cloud Platform (GCP). The solution focused on integrating data sources with Google BigQuery, enabling a centralized, scalable, and efficient data warehouse. Google Dataflow was leveraged for real-time data ingestion and processing, ensuring seamless data replication from MongoDB and PostgreSQL. Additionally, Looker Studio dashboards were developed to provide deep insights into agent performance, customer engagement, and call analytics. To further enhance data management and orchestration, Cloud Composer and Data Catalog were implemented, allowing automated workflows and metadata management for structured and organized data handling.
Business Impact
With the implementation of GCP-based analytics, Traya significantly improved its operational efficiency and decision-making capabilities. The real-time dashboards enabled the team to monitor agent performance, call trends, and customer engagement metrics with ease. The serverless architecture of BigQuery and Dataflow provided a cost-effective and scalable solution, ensuring seamless handling of growing data volumes. Automated data pipelines reduced manual efforts, enhanced data integrity, and facilitated faster data processing.
