Krishna Akurathi

▪ Led $100M+ Sales impact using ML/AI initiatives to transition from campaign-centric to personalized customer experiences by executing customer journey mapping initiatives, leveraging Customer Sensitivity Optimization Suite ,Offer Orchestration Engine ,Offer Simulation Tool using Mixed Integer Programming (MIP) , AI-driven Offer Engine Chatbot using Azure OpenAI GPT-4 to optimize audience targeting and offer recommendations , resulting in 25% increases in conversion rates and significant gains in customer engagement, satisfaction, and loyalty. ▪ Directed customer segmentation using survey data, analyzing over 1.7B+ historical trips to uncover key trip drivers. Guided the development of a predictive model for future trip motivations for 75M+ customers using the Multi-Horizon Quantile Recurrent Neural Network (MQRNN) technique, projecting a sales increase of over $15M+ and margin boost of $5M+ ▪ Managed EC+ subscription program, developing Acquisition ML models for new customer enrollment and real-time retention model to identify churn, resulting in over $4M+ annual margin impact through proactive customer strategies ▪ Led the development of the Fraud Blocker & Identifier (FBI), an AI-driven anomaly detection system to identify unusual customer behaviors and flags fraudulent coupon usage equipping retail managers with a real-time alerts dashboard, significantly reducing fraud-related losses by over $4.5M+ annually ▪ Led the creation of self-service analytical tools using (Tableau, PowerBI, ThoughtSpot), a business intelligence platform. Developed Leadership Reports to visualize key financial metrics and a Vendor Funding Tool with Streamlit to track performance and identify growth opportunities, thereby enhancing data-driven decision-making across teams ▪ Managed cross-functional team of 20+ decision scientists, data scientists, engineers and external vendors, fostering collaboration to leverage data insights for strategic decision-making and operational improvements

▪ Partnered in building and deploying the best-in-class Personalization Engine that uses state of the art Machine learning to personalize our touch points (channel, offer, content, digital etc.) for our 75M+ active customer base to provide business and leadership a transparent suite to optimize the profitability ▪ Led ML and AI initiatives (algorithm and software Research & Development) for loyalty, CRM, and personalization, achieving $100+MM business impact ▪ Partnered with Data Engineers and IT to deliver an AI/ML backed Personalization Engine allowing an Omni-Channel optimization and Experimentation helping in fast refinement and scaling ▪ Directed a team of data scientists and analytical vendor/partner resources, guiding them through the analytical process, solution development, and integration with personalization plans, as well as merchandising strategies and decisions ▪ Led the design, development of new front store customer segmentation that is more behavior-based, actionable, and impactful to customer lifetime value and implemented to facilitate CRM initiatives ▪ Led the design, development, and testing of audience selection /offer recommendation models across cross channels (Email, In-store offers, and app offers); Collaborated with business stake holders, Merchants/Category teams, IT, Digital/Product teams, and execution to deliver top line/bottom-line growth • Direct Mail ($60MM sales gain) – Designed an optimal strategy for customers who need a direct marketing nudge to make the next purchase or purchase more • Email ($34MM sales gain)– Developed an offer optimizer model interlaced with an investment-based customer segmentation framework • Category OAR ($10MM Sales and $10MM Margin gain) – Architected and developed ▪ Experimentation framework to come up with a customer x category offer recommendation solution

• Lead the analytics of several customer-centric workstreams in identifying and executing on opportunities for growth • Use advanced analytical techniques to analyze customer and category trends, build personalized contact and communication strategies that deliver incremental sales and margin • Play an integral role in advancing data-driven customer insights and targeting methods across the organization • Responsible for the execution and delivery of analytical projects and insights to internal business partners and senior leadership

Contribute valuable insights that shape content and enhance understanding of management and leadership concepts from HBR and beyond. This Council offers a platform for active participation, knowledge sharing, and collective advancement in our understanding of the business world. Thrilled to be part of this collaborative journey.

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• Interact with the offshore team and ensure deliverables • Built a Predictive Model Framework that automates/schedules scoring process of various Predictive models.

• Work closely with SAS ETL team • Responsible for integrating SAS with other databases like Oracle, Netezza, SQL Server etc • Automate/Schedule scoring process of various Predictive models using Unix /SAS

• Responsible for requirement gathering, solution design, and generate BRD • Identified KPI's to track company performance on different subject areas • Worked closely with Data migration team to understand the quality of data and call out any data gaps • Analyzed Data requirements and perform analysis prior to building predictive models • Responsible for building Predictive models • Worked closely with Predictive Underwriting team - Applied Decision Tree technique using Sas Eminer to "Identify profile of customers who are healthy and thus avoid medicals prior issuing a Life policy"

• Developed statistical models to improve direct marketing using SAS Enterprise Miner to build response models for marketing campaigns, and models to identify customers likely to purchase certain types of products • Solution-oriented consultant in Retail & Insurance domain; Managed Advanced Analytics and Foundational projects for the Customer Relationship Management team • Designed an ‘Uplift / true lift / Netlift’ model for audience selection of an enterprise-wide direct mail campaign, and it generated a net sales lift of $18M (annualized) over the existing strategy. • Used advanced machine learning techniques to build channel-specific response models to help the business run marketing programs based on customer’s channel preference, and thereby increasing conversion by 4% and expense to sales ratio by 1.4 times • Built a Downward migration model to identify customers with early indicators of potential attrition or declining customer engagement. The model assisted in improving the retention rate by 2% and sales by 8% • Performed market basket analysis to create bundle offers and recommendations for the next logical product (NLP) that the customer is most likely to buy. This assisted campaign managers to maximize cross-sell and upsell opportunities

• Profiled the patients, score them by Financial & Clinical Opportunities and generate monthly reports • Developed patient segmentation to understand the Health stage of a patient at a given point in time. Trigger-based campaigns are designed based on the segments that help improve the conversion (mail-2-retail; brand-2-generic; formulary drug usage) rates by 2% • Built Retail to Mail Conversion model to identify members that are likely to convert their scripts to Mail channel. This model helped in improving conversion rates by 5%

• Analysed Hospitalization data and generate patient drug usage trend report • Developed the reusable coding with SAS Macro Facility • Involved in various automation projects achieved through Advanced Excel VBA techniques • Analyzed HealthCare data and generated patient profiling and segmentation based on adherence, persistence, medication, and channel usage

• Programmer Analyst Trainee at Cognizant • Trained on Excel VBA, Java and core JAVA • Awarded 'Cognizant Certified Professional (CCP)' for completion of requirements for Level-0 certification in Core Java

Masters in (Operation Research & Computer Application) is two year postgraduate consisting of four semesters with equal weight-age for subjects in the areas of Operation Research and Computer Applications.