Building Data&AI Integrated Platform for the Retail Industry to drive Digital and Intelligent Transformation
KeenData Lakehouse is an AI-Native data intelligence platform that integrates retail enterprise-wide data to drive precise marketing and inventory optimization through intelligent decision-making.
Challenges
Fragmented Online and Offline Data and Difficulty in Building Unified Customer Profiles
Online and offline consumption data, including orders, membership behavior, and in-store traffic, are disconnected, making it difficult to form a complete, unified customer profile and limiting precision operations.
Inaccurate Inventory Forecasting and Lack of Multimodal Data Fusion
Inventory forecasting models fail to effectively integrate multimodal data such as sales data, market sentiment, and policy signals, resulting in limited forecasting accuracy and reduced inventory turnover efficiency.
Slow Dynamic Pricing Response and Lack of Intelligent Market Adaptation
Reliance on manual pricing models prevents rapid response to market changes. The absence of intelligent pricing mechanisms reduces pricing agility and compresses profit margins.
Solution Architecture
Empowering the future of digital intelligence, our architecture is your optimal solution.
Achievements
Our path to a win-win future is built together with you.
Unified User Profiling
Leverage Data & AI platforms to integrate fragmented online and offline data. Advanced AI algorithms construct a unified, omnichannel user profile that uncovers consumer preferences, behavioral patterns, and purchasing cycles, enabling highly precise, data-driven user operations.
Marketing Resource Optimization
AI-powered analytics continuously evaluate the real-time effectiveness of marketing campaigns. Machine learning models dynamically optimize strategies and intelligently allocate marketing resources to minimize waste and maximize return on investment (ROI).
Intelligent Inventory Forecasting
Multimodal data fusion integrates sales data, market sentiment, and policy signals to train AI-driven demand forecasting models. By connecting enterprise-wide retail data, it enables intelligent inventory decisions, improves stock turnover, and enhances overall operational efficiency.