When Algorithms Learn to Read People: Big Data’s Commercial Value in E‑Commerce (Part 1)

In the wave of digital transformation, big‑data technology acts like a strong wind that injects innovation and change across industries. The author asks how big data gradually creates commercial value in the e‑commerce and retail sectors.

Personalized recommendation has evolved from static hot‑item lists to context‑aware suggestions. For example, a single search for running shoes on Taobao now triggers not only various shoe brands but also sports apparel, fitness equipment, and even protein powder.

The underlying logic consists of three parts: who you are, what you have viewed, and what similar users bought. Collaborative‑filtering algorithms analyze the behavior of users with similar patterns, constructing a massive user‑behavior network where every click, dwell time, and add‑to‑cart action adds a new connection point.

A hidden variable in the time dimension further refines recommendations. JD.com’s system, for instance, tracks purchase‑time habits; users who regularly buy snacks on Friday evenings receive new snack promotions on Thursday, which has lifted conversion rates by 30% .

User profiling aims to see the real consumer through data. Effective profiles combine scenario and motivation. A 25‑30‑year‑old woman in a first‑tier city with monthly spending over ¥5,000 may buy for herself, for a boyfriend, or for a corporate team‑building event, and each scenario triggers a distinct recommendation logic.

The author later upgraded the profiling model to a behavior‑sequence + intent detection approach. Instead of only asking "who you are," the system also asks "what you intend to do now." Signals such as search terms, page dwell, add‑to‑cart then delete actions, and phrasing in customer‑service chats are stitched together to form a living user representation.

A seven‑word request illustrates this capability: a user searches at 11 pm for a "gift for the leader, affordable, presentable." The system does not push the user’s usual items but suggests a tea gift box with premium packaging, which the user purchases, demonstrating that profiling seeks to understand unspoken needs.

Other case studies include NetEase Yanxuan, which discovered that users who read product details and reviews are price‑insensitive but quality‑driven, leading to the launch of a high‑price "artisan" series that sold well. Xiaohongshu analyses note content, like types, and followed influencers; a user who shares fitness notes, likes food posts, and follows fashion influencers is labeled a "refined lifestyle seeker" and receives targeted high‑quality sports gear, organic food, and luxury brand recommendations.

Recommendation and profiling solve the "see the user" step, but the author notes that further challenges remain: determining optimal pricing, deciding inventory levels, reacting to sudden anomalies within 30 seconds, and deciding whether final decisions belong to humans or algorithms. These topics will be explored in the next part.