Srikanta Kara

Digital Native Onboarding: Engineered an advanced ML solution using XGBOOST atop K-Means clusters to pinpoint promising startups for Microsoft's strategic investments, establishing a robust $2 million annual revenue pipeline.

Value-Based Delivery Impact Analysis: Innovatively pioneered a test and control mechanism with robust synthetic control to precisely measure the revenue impact of value-based delivery, enabling tailored customer engagements that drive sustainable growth.

Renewal Reconciliation Copilot: Developed a cutting-edge copilot using LangChain for renewal escalation, creating a seamless, automated system to identify renewal transitions, mitigating missed opportunities and preventing potential revenue loss.

Account Orchestration Strategy: Designed a comprehensive performance scorecard to rank customers and utilized regression analysis to identify key performance drivers. Leveraged a time-series lag regression model to optimize efforts and to ensure timely proactive collaboration and customer engagement to enhance account performance.

Off-manifest Prediction and Up-stream Improvement: Introducing Random Forest to predict off-manifest discrepancies in carrier invoices has revolutionized our pricing accuracy and carrier charge provisioning. This initiative saved USD 3.5 million annually through precise pricing strategies.

Language Neutralization Process: Introducing Random Forest, Decision Tree, and Logistic Regression to detect language-agnostic order investigations within language queues reduced language-related FTEs and hiring costs by 60%, streamlining operations for non-language experts.

·Chargeback Auto Tagging: Implementing XGBOOST and Multilayer Perceptron to automate fraudulent chargeback detection and tagging reduced investigation workload by 50%, resulting in USD 500K annual savings through automated actions.

Proactive Escalation Detection: Deploying Random Forest to proactively identify customer appeals likely to escalate improved customer service by streamlining detailed investigations. This initiative achieved a 35% reduction in escalation volumes, enhancing overall customer experience.

Global Advance Segmentation: Using K-means to identify customer segments with similar characteristics for targeted marketing campaigns in priority markets. Increased product penetration through cross-selling and upselling strategies.

Insurance Scoring and Campaign Management: Developed logistic regression models to create insurance scorecards for life and general insurance products. Achieved an average campaign success rate of 3% and conducted performance measurement.

Innovative Trigger Commercialization Campaign Strategy: Improved campaign success rates by 12% through transaction data mining and event identification. Identified cross-sale and upsell opportunities aligning customer needs with banking products.

Credit Card Reactivation Model: Implemented decision trees and scorecards to predict customer attrition in credit card usage. Successfully retained 3.7% of customers through targeted retention campaigns.

Customer Next Best Action: Achieved a 23% improvement in targeting by aligning retail banking products with customer needs through precise customer life stage identification and event occurrence mapping. Used K-means clustering for segmentation, transaction text mining for life stage event detection, and sequence analysis for triggered product identification.

Collection Optimization and Dunning Strategy: Reduced calling costs by 17% and increased inflows by 8% through strategic dunning and delinquency management. Applied logistic regression for payment propensity and delinquency bucket identification and used RFM analysis to optimize payment patterns and contact strategies.

Campaign and Promotional Effectiveness:Identified revenue-generating campaigns and maximized ROI through advanced multiple regression-based market mix modeling. Conducted rigorous test and control mechanisms and ROI analyses to assess promotional strategy effectiveness.

Volume Forecasting and Automation: Developed monthly, weekly, and daily forecasting models using ARIMA, ARIMAX, Timeseries Regression, and exponential smoothing in SAS ETS and SAS Enterprise Guide. Created a SAS application to automate forecasting pipelines to generate forecast reports which enhanced operational efficiency and accuracy.

Customer Profiling and Segmentation:Implemented a K-Means cluster analysis model to effectively segment and profile the client base. Utilized SAS STAT and SAS Enterprise Guide to provide insightful customer insights and enhance targeted strategies.

Developed exponential smoothing models for forecasting monthly and quarterly retail sales, implemented trend analysis across sales channels in the USA and Mexico, projected sales across various markets and retail channels in the USA, segmented the US retail market and customer base using cluster analysis, and managed universe estimation, sample design, stratification, and allocation using SAS and SQL, optimizing sample sizes and preparing sample KPIs and dashboards for effective monitoring.

Collaborated with the US DC team to produce and maintain MIS reports for the business unit, ensuring accurate reporting of investment activities to support operational decision-making based on evolving business needs.