Marketing Analytics & Predictive Modeling Specialist

Joseph Joshi Aerrolla

************@*****.*** 617-***-**** USA LinkedIn GitHub

Summary

Marketing Analytics professional with 5+ years of experience driving data-informed strategies to optimize campaigns, enhance customer engagement, and improve ROI. Skilled in Python, SQL, R, Excel, Tableau, Power BI, and other analytics tools for predictive modeling, cohort analysis, and A/B testing. Experienced in omnichannel marketing analytics, CRM integration, and reporting automation, delivering actionable insights across e-commerce, retail, and financial services to support business growth and decision-making. Technical Skills

• Data Analysis & Visualization: Python, R, SQL, Excel (Pivot Tables, Advanced Modeling, Macros), Tableau, Power BI, Power Query, DAX, Looker, Google Data Studio, Confluence, SharePoint

• Marketing Analytics & Optimization: A/B Testing, Multivariate Testing, Bayesian Testing, Cohort Analysis, Regression Analysis, Trend Analysis, Multi-Touch Attribution, ROI & CLV Modeling, Customer Lifetime Value Modeling, HubSpot, Google Analytics, GTM

• Customer & Market Insights: Customer Segmentation, SKU Performance Analysis, Loyalty Program Analytics, Purchase Behavior Analysis, Basket Analysis, Regional Sales Insights, Cross-Sell & Upsell Strategies, Decision Tree Segmentation, RFM Analysis, Churn Modeling

• Data Integration & Management: CRM Data Management, Transactional & Campaign Data Integration, ETL Automation (Python, Alteryx), Data Standardization, Process Optimization, Reporting Automation, Collaboration with Data Engineering

• Predictive & AI-Powered Analytics: Predictive Modeling, AI-Powered Customer Behavior Analysis, High-Value Customer Identification, Personalization Strategies, Marketing Forecasting, Engagement Analytics, Funnel Analysis, KPI Monitoring, Scenario Simulations Professional Experience

Marketing Data Analyst, Nestle Holdings 06/2025 – Present Remote, USA

• Conducted consumer behavior analysis across Nestlé’s e-commerce platforms and retail loyalty programs using Python and SQL, performing segmentation, basket analysis, and purchase frequency trends, providing actionable insights that increased repeat purchase rates by 14%.

• Collaborated with the Data Science and Product Innovation teams to develop predictive models for high-value customer identification, demand forecasting, and churn reduction, documenting reproducible workflows in JIRA to support cross-functional marketing campaigns.

• Designed advanced Bayesian A/B and multivariate tests for email, social media, and in-store promotions, incorporating uplift modeling, dynamic targeting, and consumer segment weighting, optimizing campaign ROI and boosting engagement by 20%.

• Developed interactive, AI-enhanced Power BI dashboards with predictive modules for sales forecasting, campaign performance, and category penetration analysis, enabling real-time monitoring of marketing KPIs and increasing campaign targeting efficiency by 17%.

• Performed omnichannel marketing analytics using HubSpot, Google Analytics, and Python-based CLV, ROI, and attribution modeling, integrating multi-touchpoint analysis and regional spend efficiency to increase marketing ROI by 25%.

• United with Brand, Product, and AI teams to implement data-driven insights workflows, leading scenario simulations, trend forecasting, and shelf- space optimization studies using Python, SQL, and Power BI, fully documenting findings in Confluence and JIRA to guide tactical decision-making. Marketing Analyst, Tata Consultancy Services (Client: USAA) 05/2019 – 12/2023 Hyderabad, India

• Designed a customer behavior analytics initiative using transaction, interaction, and loyalty datasets, uncovering patterns that improved marketing spend efficiency by 8% and enabled more precise member targeting strategies across campaigns.

• Evaluated cross-channel campaign performance across digital banking, email, and social media using segmentation and cohort analysis, increasing engagement rates by 31% through improved personalization and optimized communication strategies for diverse member segments.

• Built interactive KPI dashboards using Looker, Google Data Studio, and Excel, providing real-time visibility into acquisition, retention, and campaign ROI, reducing reporting turnaround time by 17% and improving stakeholder responsiveness.

• Developed automated ETL pipelines using Python and Alteryx to integrate, clean, and transform large datasets, reducing manual data processing time by 24% and ensuring accurate, regulatory-compliant datasets for predictive analytics.

• Applied advanced analytics techniques including decision trees, churn modeling, RFM segmentation, and forecasting, identifying high-value members and reducing attrition rates by 22% through targeted retention strategies.

• Designed and executed A/B and multivariate testing for marketing campaigns and pricing strategies, optimizing conversion funnels and improving campaign conversion rates by 42% through data-driven experimentation and analysis.

• Collaborated with cross-functional teams including product, risk, compliance, and digital banking units to align analytics with business goals, improving insight delivery speed by 19% and supporting strategic growth and member experience initiatives. Education

Northeastern University — Boston, MA, USA

Master of Science (M.S.), Information Systems 01/2024 – 12/2025 Gokaraju Rangaraju Institute of Engineering and Technology — Hyderabad, Telangana, India Bachelor of Technology (B.Tech), Computer Science and Engineering 08/2015 – 04/2019 Projects

Predictive Customer Churn Dashboard

• Developed a Python- and Power BI-based predictive dashboard identifying high-risk customers, integrating cohort analysis, RFM segmentation, and AI-powered forecasting, enabling targeted retention campaigns and reducing churn while improving subscription engagement and overall marketing ROI by 20%.

Omnichannel Campaign Attribution Model

• Built a multi-touch attribution model using SQL, Python, and Google Analytics to evaluate cross-channel marketing performance, integrating ROI, LTV, and spend efficiency metrics, providing insights for budget allocation and optimizing campaign effectiveness across digital and retail channels.

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