Description
Projects: Customer Lifetime Value (CLV), personalized marketing campaign, Marketing Mix Modeling (MMM) project, Fraud Detection, Real-Time Policy Automation in Salesforce
- Designed A/B testing strategies using statistical methods (Mann-Whitney U test) in Azure Databricks using Python, validating campaign effectiveness and delivering data-driven insights that informed marketing strategy.
- Collaborated with Loyalty, CRM, Marketing and Merchandising teams to align customer retention strategies, improving brand loyalty and enhancing targeted communications for new, reactivated & existing customers.
- Developed and automated ad-hoc reporting using Tableau, SQL and Alteryx, providing insights on cross-sell, up-sell, and ad tech performance, which enabled the marketing team to refine strategies and improve conversion rates by 10%.
- Collaborated with data science and marketing teams to design and implement a personalized marketing campaign model based on Customer Lifetime Value (CLV) to identify high value customers, aimed at optimizing customer engagement and improving retention.
- Leveraged predictive analytics to forecast future customer behavior, segmenting audiences into high, medium, and low-value groups, and optimized email content for each segment to increase conversion rates by 18%.
- Led audience selection for targeted marketing campaigns, optimizing customer segments based on historical behavioral insights, resulting in a 15% increase in campaign engagement and 10% uplift in incremental sales.
- Collaborated with Senior Data Scientists on a Marketing Mix Modeling (MMM) project, analyzing the effectiveness of various marketing channels and recommending optimized budget allocation to maximize ROI.
- Spearheaded brand cannibalization analysis, identifying shifts in customer preference between competing brands, retention, and churn rates which informed pricing & promotional strategies, driving 5% reduction in customer attrition.
- Created and transformed a coupon engine report from Tableau to Power BI, providing real-time, interactive data on coupon redemption, KPIs like ATV, UPT, AUR to enhance targeted promotions.
- Performed extensive data preprocessing and cleaning in Azure Databricks using PySpark, transforming raw sales and transactional data into structured, high-quality datasets for use in sales forecasting models.
- Led demographic (ethnicity, gender, age, Income) and geographic segmentation analysis to uncover key insights into customer behavior, enabling highly targeted marketing campaigns and contributing to 15% growth in regional sales.
- Streamlined complex workflows like SMS segmentation based on categories and channel using, Alteryx, reducing report generation time by 30% and supporting cross-functional teams with faster access to key insights.
- Managed and optimized personalized email campaigns using Salesforce Marketing Cloud, segmenting audiences based on behavioral data and customer preferences to drive engagement and conversion.