The China Stone Corporate Intelligent Decision-Making Algorithm Research and Consulting Center (hereinafter referred to as the "Algorithm Center") is composed of a team of experts from diverse fields including economics, management, theoretical physics, mathematics, and computer science.

The Algorithm Center is dedicated to helping enterprises unlock the value of their digital assets. By leveraging big data and artificial intelligence algorithms, we empower businesses to make scientific decisions, reduce labor costs and human decision-making biases, ultimately achieving the goals of cost reduction, efficiency improvement, and rapid profit growth.

Leading Expert Professor Liu Xiaoou:

•Professor at Renmin University of China

•Ph.D. in Economics from the University of Connecticut, USA

•Visiting Researcher at Stanford University's Statistics Department and Financial and Risk Algorithm Research Center (FARM)

•Founder of the Computational Economics Professional Group at China Computer Federation

Research Focus Areas: Big data analytics, corporate AI intelligent decision-making

Industry Collaboration Experience:

Has provided consulting services and algorithm cooperation to numerous enterprises including Alibaba, Meituan, Douyin, Huawei, and OPPO in areas such as:

•Smart supply chain construction

•Foundational design of personalized recommendation algorithms

•Dynamic pricing algorithm design

•Dynamic advertising strategies

•Customer sentiment analysis

•Marketing planning
   
 Key Service Areas of the Algorithm Center:
 
I. Brand
 
1. Customer Experience Enhancement:
 
Utilizing internet big data to capture brand-related comments, photos, and video content, we employ natural language processing (NLP) and computer vision technologies to analyze customer sentiment dimensions associated with the brand, mapping out emotional curves. Based on these insights, our algorithms optimize "emotionally resonant" brand language to effectively connect with consumers and evoke engagement.
 
2.Advertising Strategy Optimization:

While current RPA-based automated ad placements successfully mimic human execution rules and processes—reducing manual effort in platform ad bidding—they lack dynamic strategy optimization. Our machine learning algorithms analyze competitors' bidding strategies to enable real-time dynamic ad bidding optimization, significantly improving ad performance.
 
II. Customer Resource Management & Private Domain Operations
 
Using unsupervised learning algorithms, we create precise customer profiles and segment them into tiers. Supervised learning algorithms then predict customer preferences, lifetime value, and influence. This enables targeted ad and push message delivery, enhancing brand trust and promotional efficiency.
 
III. Channel Efficiency Improvement
 
1.Inventory simulation via accurate demand forecasting, coupled with dynamic pricing strategies for retail based on stock levels.

2.Product portfolio optimization driven by precise demand predictions.

3.Hyper-personalized "one-person-one-policy" product recommendations.

4.Smart optimization of in-store shelf displays.

5.Intelligent warehouse space layout planning.

6.AI-driven warehouse shelf management.

7.Slow-moving product analysis.

8.Store performance early-warning systems using customer text reviews and visual data.