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ML Product Strategy Planning.pdf

# Loyalty Program as a Platform Service Building a product to personalize and optimize loyalty benefits for customers and retailers * This demo was developed following the completion of lessons on AI and building AI solutions from completing the "ElementsofAI" courses: https://www.elementsofai.com/ * Sequel to that is the completion of the Machine Learning Product Management Guide on Udemy: https://www.udemy.com/course/machine-learning-for-product-managers/ ## Summary The product is an AI-driven loyalty program optimization platform that automates the selection of weekly promotional items and personalizes loyalty benefits for both customers and retailers, maximizing engagement, sales, and satisfaction. ## Background Retailers face challenges in efficiently selecting the most effective products for weekly loyalty promotions, leading to suboptimal sales and customer engagement. This issue is frequent, given the dynamic nature of retail inventory, changing customer preferences, seasonality changes, and loyalty program schedules. My motivation is to enhance retail productivity and profitability while improving customer satisfaction. ## How is it used? * Context: The platform is used by retail managers and marketing teams to select weekly promotional items and tailor loyalty rewards. * Users: Retailers and their marketing teams are the primary users, while the end beneficiaries are customers participating in the loyalty programs. Retailers gain improved sales and operational efficiency, while the customers receive more relevant and enticing loyalty benefits. ## Data sources and AI methods * Data Sources: Historical sales data, inventory levels, customer purchase history and demographics, market trends, and competitor pricing. * AI Techniques: Predictive analytics, recommendation systems, multidimensional regression, and clustering for personalized and optimized promotional strategies. ## Challenges The project does not solve broader issues related to supply chain disruptions, external market forces, or changes in consumer behavior not related to promotions. It also doesn't directly address in-store experiences or non-promotional aspects of customer service and satisfaction. ## What next? * The idea could grow to include predictive inventory management, optimizing product pricing and sales projection, and integrating real-time market trend analysis. * Read the 'Product Strategy Plan' attached to this repository. ## Acknowledgments * Retail Data Analytics dataset on Kaggle - Contains historical sales data for 45 stores across different departments (https://www.iguazio.com/blog/13-best-free-retail-datasets-for-machine-learning/) * E-Commerce Sales Data - Comprehensive dataset with sales data across channels and financial information (https://www.kaggle.com/datasets/manjeetsingh/retaildataset ) * Retail Data Databases & Datasets from Datarade - Curated selection of top retail data sources, including market research reports and e-commerce trends (https://datarade.ai/data-categories/retail-data)

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