Worked with more than two dozen brands on various marketing campaigns. Some of the clients included Swordfish, Syncari, Redactable, Salesrobot, Clientvenue.

Built chatbots and virtual assistants that can handle common customer inquiries, provide product information, and route customers to the appropriate resources. This improvement reduced response times by 25 minutes on average per user and freed up human customer service representatives to focus on more complex issues.

- Forecasted demand, supply and prices of 1000+ goods over next 4 quarters with 83% precision which helped the organization plan accordingly. - Built a recommender system for the Computers/Laptop Section which outperformed their current one. - Took a series of workshops for the Sales team and educated them on the applications of AI. Worked with them to implement a couple of projects like lead scoring, opinion mining.

• Enhanced response rates and revenue by 6% compared to previous campaigns by optimizing marketing emails. Not only did we personalize the campaign for each prospect, but we also predicted who was more likely to respond and sent it only to those individuals. This approach also resulted in savings from the handwritten mail notes campaign.• Automated numerous customer service processes, such as connecting high CLV customers with the most senior representatives in significantly shorter wait times. • Developed tools for real-time insights into user behavior, predictive outcomes of calls with probabilities, and automated response suggestions to assist representatives.• Segmented customers based on lifetime value using hierarchical and self-organizing map (SOM) techniques, providing key data that aided executives in strategic decision-making.• Achieved 94% accuracy in revenue forecasting utilizing advanced time series models.• Identified critical data governance issues and delivered strategic recommendations to the data warehouse team for enhancements.• Advised management to reevaluate the utility of non-predictive variables purchased from third parties, highlighting inefficiencies and potential cost savings.

‣Developed and deployed machine learning models to predict donor churn propensity, achieving 92% accuracy. Provided strategic recommendations to enhance donor retention efforts.‣Utilized churn modeling and survival analysis to identify 46,051 constituents at immediate risk of churn, enabling preemptive engagement strategies.‣Created visualizations to analyze characteristics of major gift donors, clarifying key traits associated with significant donations. This insight supports tailored cultivation strategies for potential major donors.‣Implemented a customer lifetime value metric into the data warehouse, facilitating deeper analysis of profitability and more effective resource allocation.‣Identified customers with significant residual value post-churn, representing a potential revenue opportunity of $0.72 million.

➢Developed two-stage predictive models to determine the likelihood of a prospect donating and the expected donation amount, achieving 93% accuracy.➢Employed Natural Language Processing algorithms to analyze customer sentiment and topics, which enhanced donation amount predictions with 82% precision and facilitated the creation of targeted marketing segments.➢Designed and implemented interactive dashboards using Power BI, significantly enhancing insights into donor demographics and donation patterns.

Customer Segmentation and Marketing Personalization: Led the implementation of clustering techniques to segment customers based on purchase behaviors. Utilized these insights to craft personalized marketing strategies, significantly enhancing customer engagement and retention. This approach resulted in a 3% increase in customer lifetime value and a 8.33% improvement in campaign response rates.Supply Chain Optimization Project: Played a key role in analyzing supply chain logistics to optimize delivery routes and warehouse operations. Contributed to projects that predicted stock shortages and automated inventory processes, improving efficiency and reducing operational costs. Achieved a 10% reduction in delivery times and a 12% decrease in inventory holding costs.Dynamic Pricing Strategy: Developed algorithms for real-time pricing adjustments based on market demand, competition, and inventory levels, boosting profitability and market adaptability. This initiative contributed to a $25M increase in overall profit margins within the first year of implementation.Fraud Detection System: Assisted in creating models to identify and prevent fraudulent transactions, enhancing the security of financial operations and safeguarding company assets. Product Recommendation Engine: Contributed to the development of advanced algorithms that recommend products to users based on their browsing and purchase history, elevating the shopping experience and increasing sales. This enhancement led to a 25% increase in repeat customer purchases and a 15% boost in average order value for a small segment of users we tested on.