Understanding Big Data Price Discrimination and Data‑Driven Marketing

1. What Is Big‑Data Price Discrimination?

Big‑data price discrimination is essentially a data‑marketing tactic: Businesses exploit information asymmetry between transaction parties, using each user’s identity and historical behavior—or current needs—to adjust product pricing.

For example, on an e‑commerce platform, a user with the latest iPhone and high past spending is deemed a high‑spending customer, so the prices shown to them may be inflated. In video apps, different users may see different recommendation feeds—some get motivational videos, others get comedy—based on their preferences and history.

2. The Digital Age Fuels Data Marketing Development

Today’s market is constantly refreshed by big data and AI, recording countless personal traces and creating a massive data “blue ocean.” Under the influences of the pandemic and new‑infrastructure initiatives, many industries are shifting from industrial and product economies to a digital economy.

Leading companies now face intense homogeneous competition: platforms such as Douyin, Kuaishou, Meituan, Ele.me, Taobao, and JD.com all target overlapping user groups, intensifying rivalry.

Ten years ago, many firms were indifferent to data marketing, focusing on product technology and traditional operations; now, experience‑based tactics are widely known and no longer provide differentiation, prompting a shift toward more advanced, intelligent marketing methods.

In the era of booming big data, data marketing becomes the optimal auxiliary tool for operations, with deep mining of massive user history data becoming a key competitive factor.

3. How Is Data Marketing Implemented?

To understand big‑data marketing, first identify the marketing scenarios.

1. Acquisition Scenario: Users obtain product information from online feeds, social platforms, TV, outdoor ads, etc. Personalized recommendation feeds are typical data‑marketing tactics used by apps like Baidu, Toutiao, Douyin, and Xiaohongshu.

2. Consideration Scenario: Before purchase, users gather information from multiple channels (reviews, social platforms) and may encounter tactics such as incentivized positive reviews.

3. Purchase Scenario: Users buy online or offline, often encountering promotions that drive the final transaction.

4. Retention Scenario: Post‑purchase, refined marketing (membership programs, offline events, SMS) aims to increase user retention.

5. Referral Scenario: Retained users further spread the product, creating a secondary diffusion loop.

In each scenario, user interactions generate data that forms the historical behavior dataset supporting data marketing. Data marketing fundamentally mines this behavior data to create personalized strategies applied across the scenarios.

Analyzing the funnel across the five scenarios reveals where user drop‑off is highest, enabling targeted optimization and continuous iteration.

In summary:

Data marketing permeates daily life, but over‑reliance on historical data can be a pitfall; user needs evolve over time and context. Effective data marketing must be flexible, incorporating multi‑factor and scenario‑based approaches. The next article will detail how to build a data‑marketing system.

A playful product‑focused “data enthusiast” invites you to discuss further.