From Data Mining to Ecosystem Reconstruction: How Chinese AI Models Are Rewriting Marketing Rules

Key Insights and Supporting Data

Technological Breakthroughs: Chinese AI Models Rival International Giants

Chinese large language models like the DeepSeek series (V3, R1) are now competitive with OpenAI’s offerings, excelling in inference speed, mathematics, and coding tasks. What’s remarkable is their cost efficiency—DeepSeek’s training cost of $5.58 million pales in comparison to GPT-4o’s $78 million investment. This cost advantage is creating new market opportunities.

Leading companies such as Marketingforce are leveraging DeepSeek to build comprehensive AI platforms that span the entire marketing value chain. Their AI-Agentforce platform integrates data analytics (ChatBI), customer service (TTalk), and content generation (Precision Content), helping businesses increase marketing efficiency by 35%.

Industry Applications: The Leap from “Precision” to “Prediction”

According to iResearch Consulting, China’s AI marketing sector exceeded 50 billion yuan in 2023, with AI tool adoption reaching 38%. Generative AI now contributes to over 30% of advertising script development and visual design processes.

A telling example comes from a major e-commerce platform that implemented multimodal AI to achieve 92% accuracy in product-user matching, while increasing virtual host adoption to 15%. Similarly, Xiaomi’s SU7 electric vehicle used hot data analysis to identify high-potential buyers, contributing to sales of over 130,000 units in just nine months.

Ecosystem Reconstruction: From Tools to Infrastructure

China Research Institute predicts AI marketing penetration will exceed 70% by 2025. The market is stratifying into four tiers, with technology suppliers (like Tencent Cloud and SenseTime) collaborating with vertical service providers (such as Weimob). Specialized AI tool vendors are experiencing annual growth rates of 200%.

Privacy computing technologies, particularly federated learning, have become crucial market differentiators with 210% annual growth. These technologies address data compliance challenges while supporting cross-border marketing needs.

Challenges and Trends: Balancing Efficiency and Ethics

MIT research demonstrates that GPT-4 can already generate 82% of standard marketing copy. However, 42% of users express discomfort with overly personalized recommendations, driving a shift toward “privacy-first personalization” strategies.

Experts caution that by 2025, models like GPT-5 may replace up to 18% of entry-level marketing positions. To stay competitive, companies need to increase R&D investment to 25-35% of revenue to build technological moats and develop proprietary AI capabilities.