Enterprise retail data is scattered across dozens of retail systems and summary reports often require multiple days of waiting time, which affects the time of enterprise retail decision-making.
Online third-party e-commerce platform, self-employed mall, offline direct operation, distribution stores of the full-channel retail and O2O orders and orders delivery inventory is not timely and affecting real-time sales performance during “681” or “Single 11” mega sales period,
A large amount of data generated by the Internet cannot be carried and applied, such as user information, camp promotion, rights and benefits are from WeChat, Weibo, Tiktok, RED or other channels.
Physical store operation data is not timely cannot effectively solve the staff sales problems and timelier decision-making store operation.
Based on the capability of DataForce, it realize the cleaning, storage and data visualization of retail data such as global order, membership, inventory, commodity, payment, channel, etc., and establish a unified commodity labeling system and UniID membership system to support the personalized business and omnichannel capability of enterprise retail.
Based on the capability of DataForce, it supports the API capabilities of thousands of standard data and business services while supporting enterprise personalized data and business API customization, fast service retail enterprise new applications, and its own IT developers to quickly realize your own development capabilities.
Based on the big data framework of DataForce, it supports the processing and flow processing power of large amounts of data, enables data analysis and external data service capabilities such as the ability to support tens of millions of orders during the “Single 11” period and inventory query.
Based on the capability of DataForce, new applications are generated through the convergence of existing retail data and other data, such as business-bound passenger conversion rate analysis, fitting room fitting conversion rate analysis, store display sales conversion analysis, and so on.
Break data silos to realize real-time global retail data and online business
Form data collaboration and convergence capabilities to quickly service new application data requirements
Big data processing capabilities to support scale-out and address performance bottlenecks
Solve enterprise responsiveness dilemmas and respond quickly to new needs
Smart docking of the original system without affecting the continuity of the original business
Share service capability to reduce recurring development costs