The differentiation in development levels across industries is striking: some sectors like technology and new energy continue to post rising profits, while catering, retail, and real estate struggle under the weight of consumption downgrades and cost pressures, highlighting the pain of economic restructuring. Take the apparel industry as an example: in 2024, the Chinese apparel industry presents a stark ""pyramid effect.""
According to data from the China National Garment Association and the National Bureau of Statistics, the total profit of China's above-scale apparel enterprises (annual main business revenue of 20 million RMB or above) in 2024 reached 62.381 billion RMB. This profit covers 13,820 above-scale enterprises, representing the industry's main body.
Among them, 64 listed companies achieved a net profit of 31.51 billion RMB, with the top ten enterprises accounting for 96% of listed companies' total profits. ANTA alone contributed 15.596 billion RMB in net profit — nearly half. On the other side, vast numbers of small and medium enterprises struggle to survive in a price war, with the menswear category even seeing a ""double hit"" of a 10% sales volume decline and 9% drop in average transaction value.
Even more striking is the imbalance between platform economy and the real economy: Alibaba's 2024 fiscal year attributable net profit of 157.479 billion RMB is equivalent to 2.5 times the total net profit of the entire apparel industry. This distorted distribution mechanism forces enterprises to rethink: online traffic costs erode profits while platforms hold the data dividend. Where does the traditional model go from here?
The Missing Data Sovereignty in the E-Commerce Era
Among China's major platforms, Alibaba's share of apparel is relatively high compared to other platforms. When Jack Ma founded Alibaba, the mission was ""making it easy to do business anywhere"" — but the echoes of that promise are fading as the apparel sector, supposedly an easy business to run, now faces enormous challenges: insufficient demand, cutthroat price competition, and stark polarization.
In recent years, Burgeon has expanded into industries beyond apparel, such as food, 3C, FMCG, home furnishings, ceramics and sanitary ware, and hardware and building materials. Admittedly, under current economic conditions, everyone is struggling — or at least not doing as well as before. But apparel enterprises face even greater difficulties. Food, clothing, shelter, and transportation: apparel should be a massive industry. Why is it so struggling? Is the apparel industry somehow very traditional and backward? From the perspective of digitalization level, the apparel industry outpaces other traditional industries decisively. If the industry's scale, importance, and advancement are all high, yet the entire sector can't make money, while a platform company serving the industry earns 2.5 times the industry's total profit — what is the reason? How did it come to this?
We believe the answer is data sovereignty.
When a customer buys clothes at a Tmall store, does the merchant know who this customer is — where they came from, where they went? Which products did they browse? How did they make their decision? What product attributes attracted them? The merchant made a sale and possibly earned money, but the entire process is a black box — while the platform holds all of this process data. In other words, the platform has data sovereignty, and the merchant does not. Analyzing the merchant-platform relationship from a data sovereignty perspective: it appears the platform serves the merchant, offering numerous paid services. But since the final data lies with the platform, accumulated value accrues to the platform. The real relationship is that merchants serve the platform — and on top of that, pay for the privilege.
The cost of lacking data sovereignty will grow ever heavier. Merchants pay to provide data to the platform; the platform packages various services that merchants need based on that data. For more traffic and better repurchase rates, merchants spend their earned money on platform services again. This closed loop of data and services forms a vortex that ultimately drains almost all merchant profits.
Some enterprises have a low proportion of online sales, with most revenue from offline. Looking at offline business from a data perspective: the same questions arise — who is the customer, where did they come from, where did they go? What products attracted them? Why didn't they buy in the end?
Merchants still lack this data, holding only transaction data. Of course, transaction data is important and critical, but a transaction is an end result — it has already happened, and the merchant cannot change anything about it! Compared to online, offline is generally lacking in data, especially rich process data. Process data covers every aspect of business operations. Only by digitizing more operational processes can businesses truly analyze, summarize, and improve — leading to better efficiency and ultimately higher profits.
The AI Revolution: Can It Break the Platform Spell This Time?
The platform-merchant relationship has largely taken shape, and there's little we can do to change it. Fortunately, times are changing faster than ever, and we've entered a much bigger trend: AI.
AI does hold greater promise. ""ALL IN AI,"" ""Every industry deserves to be reinvented in the AI era"" — we hear these familiar refrains in the market. Years ago, when e-commerce and the internet rose, we also heard: ""ALL IN e-commerce,"" ""Every industry deserves to be reinvented in the internet era."" It seems the ending may be destined to repeat — Alibaba, ByteDance, and Tencent remain the giants. The same ingredients, the same plot. Can we expect a different outcome this time?
Burgeon's AI practices in recent years offer hope: using AI to assist in collecting process data, making operations as simple as ordering delivery. Take our five latest applications as examples:
1. AI Smart Badge: Real Cases Forge Real Sales Mastery
Traditional top sales experience sharing often stays at the ""practice run"" level, lacking real case support. The AI Smart Badge collects real, successful sales conversations, integrating diverse information such as customer demographics, product attributes, and sales consultant strategies, supplemented by AI deep analysis — enabling businesses to efficiently extract verifiable and scalable gold-standard scripts, empowering the sales team and improving selling efficiency.
2. Store-Level Promotions: Goodbye to the ""50% Off Across the Board"" Wild West
In August 2024, a certain sports brand saw GMV plummet 23% on Alibaba yet surge 71% on Douyin — rooted in failing to differentiate platform users' price sensitivity. Different stores' locations, channels, and platforms lead to differentiated operational performance. Based on AI's comprehensive analysis of each store's historical performance and surrounding business data, we derive store-specific marketing strategies — no longer a centrally issued standardized campaign plan.
3. Smart Order Routing: Inventory ""in Motion"" Is Living Inventory
Smart fulfillment's dynamic warehouse selection strategy uses real-time data analysis and algorithmic decision-making to automatically match the optimal shipping warehouse the moment an order is generated, achieving the best balance of cost, speed, and resources — ""do more business with less inventory."" The strategy relies on three core technology modules:
- Rule Engine: Defines priority logic;
- Real-Time Data Pool: Monitors inventory, workload, and logistics quotes;
- Path Optimizer: Implements multi-objective decision algorithms.
In practice, a certain apparel enterprise diverted orders from the overloaded Guangzhou warehouse to Shenzhen via dynamic routing, maintaining 48-hour logistics SLAs while saving approximately 1.5 million RMB in annual freight costs.
4. Smart Allocation & Replenishment: Making AI the ""Chief Forecaster""
Traditional allocation & replenishment heavily relies on merchandise teams' work experience. The Smart Allocation & Replenishment system uses deep learning on historical sales, weather, competitor activities, and other information, generating optimal inventory allocation plans based on available stock — achieving a dynamic balance between improved product efficiency and reduced logistics costs. After applying this system, a certain women's apparel brand not only compressed allocation & replenishment workload by 80%, but also improved sell-through rates, full-size availability, and top-seller fulfillment rates.
5. Smart Wardrobe: From ""Selling"" to ""Lifetime Service""
After a customer completes a clothing purchase, the brand provides personalized outfit recommendations based on purchase records combined with real-time dynamic data such as seasonal changes, local weather, and upcoming holidays/events,推送at preset frequencies like daily or weekly. This not only stimulates repeat wearing of purchased items, effectively extending product lifecycle, but also creates natural repurchase opportunities by recommending new items or accessories that complement purchased pieces, boosting repurchase rates. Maintaining regular, valuable brand interactions with customers, providing style inspiration and lifestyle advice, also significantly enhances brand loyalty and member retention.
Outlook: AI Brings Offline Retail ""Bottoming Out""
Looking back at the e-commerce era over the past decade, in terms of operational efficiency and business models, e-commerce is far superior to offline retail. There are many reasons, but a crucial one is that e-commerce has much richer process data than offline — offline retail's shortcoming. But in the decade ahead, AI can help offline retail acquire and accumulate sufficient data — data that can even rival e-commerce. Combined with offline retail's stronger service characteristics, we can expect that the AI era will be the era of offline retail's bottoming out and rebound!
This article is featured in Burgeon New Vision, edited-in-chief by Sun Yihui, Chairman of Burgeon Technology, and produced by Burgeon Research Institute.
2026/06/21 周日 22:17 
