Case Study 1:
A business function wanted to build analytics capability to mine data and develop insights by implementing predictive modeling techniques. The business function faced many challenges in finding right talent, procuring tools and dependency on technology teams for getting access to data which impacted them from improving their business model.
- Helped augment the business team with skills spanning Visualization Experts, Data Science & Data Analysts
- Assisted the team to leverage open source tools based on their needs.
- Implemented data access solutions that eliminated the dependency on technology teams.
- Re-engineered visualization and reporting from legacy reports to Tableau Dashboards/Reports
- Developed Machine Learning Algorithms to predict turnaround time for road side assistance to customers.
- Implemented solutions in iterative manner to enable value realization within 6 weeks.
- Over 40% cost savings in business operations and incorporated best practices for agile analytics development.
A business function wanted to increase personalized emails with coupons to customers based on their shopping patterns and product promotions. The current outsourced service for customer notification was expensive and time consuming which constrained agility to contact customers.
- Implemented a data platform to integrate data from social, sales transactions, web traffic, patterns in promotions selection, recommendations, customer profile for personalized services
- Utilized Big data and open source analytic tools to build brand affinity algorithms, score customers based on behaviors and generate product recommendations
- Integrated offers across multiple channels to address all customer touchpoints
- Re-engineered marketing ROI by measuring results and improve response rates
- Supported rolling 6 months of on-line and in-store sales data to update models and reflect recent customer interactions
- A stable data platform to host 4M transactions daily and architecture to refresh algorithms in a daily manner.
- Eliminated the dependency of external marketing agency, reduced operating cost.
- Increased revenue and personalized offers by 20%
Case Study 2:
The federal regulation for money laundering law is tremendously complex. This bank was spending extensive manual effort even with sophisticated alert management systems to analyze risky transactions and still unable to identify them all in a timely manner.
- Helped implement a graphical view of all intertwined transactions using Neo4j and graphical visualizations that enabled the team to increase their efficiency by 10 times to resolve alerts.
- Developed rules based on historical transaction patters and developed visual representations that allowed case managers to understand the transaction flow quickly
- The centrality metrics showed the critical path of the interdependent transactions that helped to gain insights from the underlying data.
- Re-designed customer data from a Relational format to a Graph Network Model to receive alerts on fraud and money laundering
- Developed dashboards and graphical workflows on top of the graph networks to enable analysts to view alerts propagated by upstream systems
- Increased the efficiency of the team to resolve alerts at real time and reduce risk.