Fashion retail companies often struggle with making informed buying decisions for new, untested products due to reliance on gut instinct, which leads to substantial unsold stock and lost revenue.
Seestone tasked me with creating their branding and AI-driven platform to centralise and analyse diverse data sources to empower fashion buyers. The objective was to replace subjective decision-making with data-driven insights, enabling more accurate predictions and efficient buying processes. I designed an intuitive platform that integrates data from various sources, such as demographic trends, design elements, and sustainability impacts, to provide a holistic view of potential inventory decisions.
The Seestone platform provides a suite of tools that transform the traditional buying process:
Data-Driven Decision Making: Utilises AI to analyse and synthesise data, offering buyers clear insights into potential product performance.
Comprehensive Analytics: Measures key aspects of workplace design and their impact on business outcomes, supporting strategic buying decisions.
Predictive AI Modeling: Employs advanced AI to forecast the success of new products, helping buyers to choose wisely.
User-Friendly Interface: Consolidates all relevant information into a single platform, simplifying the decision-making process for buyers.
Significantly reduce overstock and understock situations, enhance buyer confidence with data-backed decisions, and improve overall financial performance for retailers.
While the Seestone platform is still under development, it is poised to revolutionise the fashion retail industry by providing a more systematic and data-centric approach to buying new products. Once deployed, the platform is expected to significantly reduce overstock and understock situations, enhance buyer confidence with data-backed decisions, and improve overall financial performance for retailers.
