Marketing & Business Analytics Professional

Carnegie Mellon MSBA graduate with proven expertise in marketing analytics, data science, and AI-driven solutions.

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Professional Highlights

  • 🔍

    Advanced Data Analytics

    Python, R, SQL, Machine Learning, LLMs, AI

  • 💼

    Industry Experience

    Deloitte (Capstone), Publicis Global Delivery

  • 🎓

    Education

    MS in Business Analytics, Carnegie Mellon University

    Bachelor of Commerce, Narsee Monjee College

  • 🏆

    Recognition

    Two-time "Achiever" awardee, recognized by CEO at Publicis

About Me

I'm a business and marketing analytics professional with a Master of Science in Business Analytics from Carnegie Mellon University's Tepper School of Business. I bring a unique blend of technical expertise in data science and machine learning, paired with hands-on experience in marketing analytics and process optimization.

At organizations like Publicis, I've led high-impact initiatives that enhanced ad spend efficiency, improved customer acquisition strategies, and automated workflows—unlocking measurable business value. I specialize in turning complex data into actionable insights that inform strategy and drive performance.

I'm passionate about using AI and advanced analytics to solve real-world business challenges. My approach combines analytical rigor, strategic thinking, and strong communication to deliver data-driven solutions that align with business goals and drive sustainable growth.

Education

Carnegie Mellon University – Tepper School of Business

Master of Science in Business Analytics (Merit Scholarship)

GPA: 4.00/4.00 | May 2025

Leadership: Student Leadership Council – Operations Officer and GSA Representative

Narsee Monjee College of Commerce and Economics

Bachelor of Commerce (Financial Accounting and Auditing)

GPA: 3.82/4.00 | May 2021

Leadership: Performing Arts Association – Marketing & Finance Head; Finance and Investment Cell – Elected Director

Work Experience

Deloitte Consulting US (Capstone Project)

Jan 2025 - Apr 2025

Simulated Consumer Testing with AI

  • LLM Integration & AI Technology Creation: Built an AI tool using Flask, LLMs, and Selenium to simulate 25+ customer personas and evaluate website content, UX, and key marketing elements such as message clarity, CTA effectiveness, and brand perception, saving over 12,000 hours of manual review across 1,000+ websites.
  • UX Optimization & Business Impact: Engineered automated testing and persona flows with Selenium, enabling a 25% increase in website usability scores and driving adoption of strategic recommendations by client teams.
  • Insight Validation: Benchmarked persona feedback across multiple LLMs, applying a 70% agreeableness threshold to reduce hallucinations and ensure high-confidence insights for optimizing conversion funnels, UX, and content performance.

Publicis Global Delivery

Jul 2023 - Jan 2024

Associate Manager

  • Revenue Growth: Created A/B testing frameworks, analyzed marketing data using SQL and Python, and optimized ad delivery improving return on ad spend (ROAS) by ~30%, generating $7.2M in incremental revenue for clients.
  • Data Analytics and Ad Optimization: Decreased trafficking errors by 13% month-on-month, saving an estimated 40+ hours/month of manual rework, through deep analysis of delivery KPIs using SQL and Tableau.
  • Process Automation: Proposed and delivered automation initiatives that cut manual workload by 25%, strategically positioning the team to scale services and manage $2M in added revenue from a key Media & Entertainment account.

Publicis Global Delivery

Feb 2022 - Jun 2023

Ad Operations Analyst

  • Process Improvement: Led global ad operations process evaluation to identify pain points and drive compliance improvements across 15+ countries, enabling 3,500+ error-free campaign deliveries (an unprecedented milestone).
  • Performance Analysis: Analyzed CM360 trafficking logs, QA failure rates, and time-to-live metrics to identify workflow inefficiencies, improving efficiency by 48% and reducing turnaround time by 42%.
  • Ad Fraud & Viewability Analysis: Reduced invalid traffic exposure by ~35% using IAS (Integral Ad Science) insights, improving ad spend efficiency and media delivery quality across campaigns.

Publicis Global Delivery

Jun 2021 - Jan 2022

Analyst

  • Customer Acquisition and Media Optimization: Designed and executed A/B tests to evaluate creative and media performance, increasing click-through rates (CTR) by 25% and reducing customer acquisition costs (CAC) by 18%.
  • Project Management: Effectively managed coordination and optimized task assignment to maintain ~95% team utilization.
  • Ad Trafficking: Streamlined ad trafficking processes, reducing errors for special request projects and saving ~120 hours/week.

Analytics Projects

📊 Northwind Trading Company - Advanced SQL Marketing Analytics

Comprehensive business intelligence transformation analyzing 600,000+ transactions to rescue a declining trading company. Uncovered the "Inverted Customer Pyramid" phenomenon where supposedly "at-risk" customers generated 32.5% of revenue, identified $77M inventory crisis, and developed data-driven strategies that projected 1,726% ROI with $127.7M revenue increase potential.

Advanced SQL 600K+ Transactions Customer Segmentation Strategic Analytics Business Intelligence Revenue Optimization

🚀 Business Impact Story: Transformed raw transaction data into actionable strategies that identified critical inventory risks, revealed hidden customer value patterns, and delivered a roadmap for 15% profit margin improvement across global markets.

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Predictive Modelling for Customer Acquisition

Developed predictive logistic regression and decision tree models in R to optimize a bank's telemarketing campaign, improving customer targeting and achieving a 90% cost reduction and 2.5x increase in net profit.

R Predictive Modeling Logistic Regression Decision Trees
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Cell2Cell Proactive Retention Campaign

Designed a churn prediction model in Python and implemented a proactive retention strategy using an Excel simulator to optimize offers for at-risk mobile customers, improving LTV by $4.39M and achieving 757% ROI.

Python Churn Prediction Customer Retention ROI Optimization
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Competitive Analysis using Topic Models

Utilized LDA and Euclidean distance in Python to cluster 1,100+ films by topic and identify optimal 2014 release date for The Maze Runner, reducing direct-release competition by 23%.

Python LDA Topic Modeling Competitive Analysis
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Ford Ka Market Segmentation

Applied k-means clustering in R on demographic and psychographic data to segment small-car buyers and identify high-MQL clusters for Ford Ka, boosting alignment with trend- and value-driven consumers.

R K-means Clustering Market Segmentation Consumer Analysis
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Skills & Expertise

Programming & ML Frameworks

Python R Advanced SQL Scikit-learn PyTorch TensorFlow XGBoost LLMs Prompt Engineering

Databases & Data Handling

SQL NoSQL (MongoDB) Pandas NumPy Data Cleaning ETL/ELT Data Storytelling

Tools & Automation

Tableau Power BI Advanced Excel Flask Selenium VBA Git Jira Airtable WordPress Adobe Analytics Google Analytics CM360 Integral Ad Science

Marketing Analytics & Strategy

A/B Testing Marketing Mix Modeling Multi-Touch Attribution Campaign Analysis Growth Marketing Churn Prediction Forecasting Customer Journey Mapping Optimization Budget Optimization

Contact Me

✉️

Email

aharlalk@tepper.cmu.edu

👔

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