Goals
Develop a new digital analytics platform to enhance customer experience
Enhance the marketing and sales lead system with improved tracking of discrete customer events
Improve analysis of deposit and withdrawal patterns to anticipate churn and loyalty, and predict future purchases
Achieve higher scalability and processing capacity by migrating the workflows to a distributed processing engine
Reduce complexity and manage various workloads efficiently using a self-service solution
About Truist
Truist is a top 10 US bank, offering a wide range of products and services through its retail and small business banking, commercial banking, corporate and investment banking, insurance, wealth management, and specialized lending businesses. The bank had total assets of more than $500 Bn in 2023.
Challenges
Limited ability to handle advanced orchestration of the workloads hindered correlation across data sources
Lack of scalability to process workloads at scale led to increased opportunity costs
The bank was unable to leverage perishable insights effectively
Adding or modifying business rules took too much time (~3 days on most occasions)
Solution
No-code drag-and-drop pipelines for ingestion, ETL, data quality, and monitoring
Advanced orchestration of workloads
Higher scalability to handle any spikes in data volumes
Ability to handle both batch and real-time workloads
Four 4 stage processing with different SLAs (data ingestion, lead identification, lead filtering and segmentation, and lead assignment to relationship reps)
Complex rules with stateful processing of historical customer transaction data for generating cross-sell/ upsell leads
Integration with ESP (Enterprise Scheduler) for workflow management
Impact
- 40%
reduction in the processing time of real-time events and data related to customer interactions and transactions
- 36X
reduction in migration time (from 3 days to 2 hours)
- 5X
performance gain and cost reduction vs. prior solution
- 4X
data volumes processed as compared to the previous solution
- 10X
Improvement in time to market*
*Ability to add new business rules in 2-3 hours vs. 3 days or more earlier
Benefits
Faster, distributed data processing powered real-time analytics for better lead management and improved conversions
Seamless rollout of new patterns/triggers and re-processing of historical events with new rules
Identification of upsell and cross-sell opportunities with high precision
Ability to send the right offer to the right customer at the right time
Improved customer acquisition, retention, and loyalty
Customer Speak
Gathr helped us unlock business use cases for upselling, cross-selling, and customer retention with speed and scale. With Gathr, we brought all data sources together, built applications in a true self-service way, and retired legacy data platforms.
SHEKAR DAHIYA
Software Engineering Director