Stellar Segmenter

Inspired by the intricate pricing strategies in e-commerce and the profound human drives in Asimov and Silverberg's 'Nightfall,' this project aims to build a niche customer analytics tool. Think of 'Interstellar's' desperate need to find new worlds and optimize resources; our project applies a similar principle to customer data. The core concept is to scrape anonymized e-commerce pricing data (e.g., historical price changes, discounts, competitor pricing) for specific product categories. This data will then be analyzed to understand how pricing fluctuations influence purchasing decisions. Simultaneously, we'll draw abstract parallels from the characters' motivations and societal pressures in 'Nightfall' – fear, survival, the drive for knowledge – to inform the types of customer segments we're looking for. For example, a 'risk-averse buyer' might react differently to a steep discount than a 'value-seeking early adopter.' The project will employ relatively simple machine learning techniques (like clustering algorithms) to identify distinct customer segments based on their price sensitivity, purchase frequency, and engagement with promotional offers. The 'Interstellar' influence comes into play in how we might frame the output: identifying the 'most habitable' customer segments for targeted marketing campaigns, predicting their future needs with limited data, and understanding resource allocation for marketing spend to maximize return, much like planning a space mission. Implementation is low-cost, relying on publicly available scraping tools and open-source ML libraries. The niche lies in the blend of price psychology, literary inspiration, and predictive analytics, offering high earning potential by providing businesses with a more nuanced and effective way to understand and engage their customer base, ultimately leading to optimized marketing ROI.