An AI tool for optimal pricing in the second-hand fashion market
Our AI model optimizes the price for pre-owned fashion items by collecting and analyzing all relevant factors that affect the price.
Challenge
AI Transforms the Fashion Industry for Circular Commerce
How can AI-driven analysis and algorithms optimize pricing strategies for pre-owned fashion items and goods in the resale market, considering various factors such as market trends, consumer behavior, product condition, stocks, and demand fluctuations, to ensure competitive pricing and maximize profitability?
Team
Philipp Striegl, Christopher Lepissier, Mohammadjavad Zamani Pozveh, Amna Nadeem
Contact: philipp.striegl@outlook.com
About the prototype
Our fashion reselling prototype leverages AI for accurate pricing and crediting. Users upload an image and description of an item to sell. Computer vision and NLP models categorize attributes like style, brand, size, fabric, color, and condition. These deep learning algorithms are trained on large datasets of fashion images and descriptions, enabling faster and more accurate analysis than humans. The attributes are input to an AI pricing model which scans current resale market data to determine the optimal price. It accounts for brand trends, changing demands, competitor pricing, and other factors to ensure the fairest and most profitable price for both parties. In a nutshell, our AI-powered platform facilitates fashion resale by efficiently analyzing items and dynamically pricing them. This captures residual value from used fashion while reducing waste.
Outputs
Further information about the progress, milestones, and roadblocks.
More information about the SAP Recommerce
Short article about a circular economy in fashion for environmental sustainability and social equity.