This project focuses on building a data-driven system to automate and optimize customer engagement and product discovery. The goal is to leverage machine learning, data analytics, and automation to drive revenue growth and improve operational efficiency for June Adaptive's online platform.

1. AI-Optimized Customer Engagement & Analytics

• Develop a customer segmentation model using Python in a Jupyter Notebook to analyze purchase history and user behavior, moving beyond basic blasts to predictive targeting.
• Engineer automated email workflows (e.g., abandoned cart, post-purchase nurture sequences) by integrating with the email software's API, potentially using Python scripts.
• Implement a structured A/B testing framework for subject lines and content. Use statistical analysis to measure significance and build a data-backed model for optimal email performance.
• Build an interactive performance dashboard in Tableau/Power BI to visualize key email metrics (open rate, CTR, conversion rate), tying them directly to revenue impact.
• Apply prompt engineering to leverage conversational AI/LLMs for generating and A/B testing personalized email copy and subject line variations at scale.

2. Data-Driven Product Catalog Management & SEO

• Develop a web scraping tool (using Python with BeautifulSoup/Scrapy) to gather competitive product data and pricing intelligence for market positioning.
• Create a product data validation script (Python) to ensure uploaded product data (titles, descriptions, inventory) is accurate and consistent before import, reducing errors.
• Automate the generation of SEO-optimized product descriptions by using prompt engineering with a conversational AI/LLM, based on a set of brand and keyword guidelines.
• Implement and analyze product schema markup to enhance search visibility and enable rich results on search engines.
• Build a product performance report to analyze sales data, identifying top-performing products and opportunities for inventory and merchandising strategy shifts.

Deliverables:

• A Customer Propensity Model: A Jupyter Notebook containing code and analysis for segmenting users based on their likelihood to purchase or churn.
• An Automated Email Pipeline: Documentation and code for at least two triggered email sequences (e.g., welcome series, abandoned cart).
• An Interactive Analytics Dashboard: A Tableau/Power BI report detailing campaign performance, subscriber growth analytics, and product-level sales data.
• An AI-Generated Content Framework: A documented process and set of prompts used to generate and optimize product descriptions and meta tags at scale.
• A Technically Optimized Product Catalog: A batch of uploaded products enhanced with structured data and content informed by data analysis.