Meghan Kotak

About Meghan Kotak

I’m a Marketing Data Analyst with ~4 years of experience transforming customer and campaign data into actionable growth strategies for global digital products and subscription businesses.Currently working with Netflix, I focus on web and customer analytics, attribution modeling, and AI-driven personalization to improve engagement, retention, and marketing ROI across multi-channel ecosystems. Previously at Adobe, I partnered with product, marketing, and customer success teams to analyze feature adoption, forecast churn and renewal behavior, and optimize lifecycle campaigns using predictive analytics and behavioral segmentation.My strengths lie at the intersection of marketing strategy, analytics, and technology — helping teams move from dashboards to decisions.Key areas I specialize in:• Web & product analytics (GA4, Adobe Analytics, Mixpanel)• Campaign measurement, attribution & A/B testing• Customer segmentation, lifecycle & cohort analysis• Predictive modeling for churn, retention & CLV• Marketing dashboards & storytelling (Power BI, Tableau, SQL, Python)• CRM and omnichannel campaign optimizationI enjoy solving complex marketing problems, building scalable analytics frameworks, and enabling teams to make faster, data-backed decisions that directly impact user growth and revenue.I’m currently pursuing a Master’s in Business Analytics & AI at The University of Texas at Dallas and am open to connecting with professionals in marketing analytics, product analytics, and data-driven growth.

Experience

1. Marketing Data Analyst

   Netflix

   Jun 2025 — Present · US

   Optimized Google Analytics 4, Adobe Analytics, and Mixpanel to track user behavior across web, mobile, and smart TV platforms, boosting personalized content engagement and retention metrics by 20% through actionable insights and analysis.• Orchestrated A/B testing for emails, push notifications, and homepage thumbnails using Matomo and internal tools, enhancing engagement by 18% while strategically improving content placement for targeted audience segments and driving measurable campaign performance gains.• Deployed AI-driven recommendation models to segment micro-audiences, enabling precise targeting in email campaigns via Maropost, generating a 25% lift in conversion while advancing personalization strategies for diverse subscriber groups across marketing channels.• Developed dynamic dashboards in Power BI and Tableau, consolidating data from Supermetrics, CDPs, and streaming analytics to track multi-channel campaign performance, reveal trends, and support consistent, data-driven marketing decisions across cross-functional teams.• Engineered robust attribution models to measure omnichannel campaigns, integrating paid media, influencer, and social strategies, enhancing ROI tracking accuracy by 30% while guiding strategic marketing decisions and informing future campaign optimization.• Monitored audience sentiment from Sprout Social and internal channels, identifying viral content trends, gauging viewer reactions, and optimizing AI-powered thumbnail testing to improve content discovery, time-on-platform, and personalized user experience engagement.

Education

• Dwarkadas J. Sanghvi College of Engineering

  Bachelor of Engineering - BE, Chemical Engineering

  2017 — 2021

• The University of Texas at Dallas

  Master of Science - MS, Business Analytics & Artificial Intelligence