Understanding Big Data: The Importance of Data Breadth and User Profiling for Precise Marketing and Product Optimization

In recent years, media have heavily promoted big data. From the 4V model (Variety, Volume, Velocity, Veracity), the most critical aspect for applications is data breadth, meaning that big data sources must span multiple product lines.

1. Big Data Important Judgment Criterion: Data Breadth

Many online articles claim to be big‑data analyses but only examine data within a single product line, such as regional purchase preferences. When an analysis combines cross‑product behaviors—like ride‑hailing or video‑watching patterns—it becomes a genuine big‑data study.

One key application of big data is leveraging cross‑product data to fully understand users, pinpoint pain points, and enable precise marketing or personalized recommendations.

2. Role of Big Data Portraits: Comprehensive User Understanding and Deep Demand Mining

By aggregating data from various product lines, we can create a 360‑degree, no‑blind‑spot portrait of users, revealing their preferences.

Big‑data portrait data come from two sources: (1) log analysis, such as clustering search logs to infer content interests, and (2) model‑driven inference of demographic attributes (age, gender, etc.) from logs. These attributes—personal, social, and interest tags—carry weights that reflect confidence levels.

For example, using ID‑mapping across devices (PC, mobile, TV), we can link a single user across platforms. A sample portrait might describe user "A" as 78% likely female, 80% likely 35 years old, college‑educated, financial‑sector clerk, car owner, weekend traveler, low‑spending on food delivery, parent of a toddler, occasional stock and travel enthusiast, and recent fan of a Korean actor.

3. Using Personal and Interest Attributes for Product Optimization

With such a comprehensive portrait, we can align product‑line data and business needs to serve truly relevant users, recommending information and services that match their interests.

Continuing the example, knowing that user A shows strong interest in stocks and travel allows Baidu Stock and Baidu Travel teams to target her with relevant app acquisition campaigns.

Another case: analyzing game‑page user churn reveals that RPG‑loving users are leaving, suggesting the RPG resource pool is insufficient and needs improvement.

Additional data dimensions can further uncover user preferences, providing solid evidence for product decisions and experience enhancements. Future posts will share more big‑data portrait use cases.