How to earn 30% more by "serving dishes to different people"? The logic behind the huge profits of user profile income stratification

As e-commerce competition becomes increasingly fierce, how to accurately locate users and maximize profits has become the key. This article deeply analyzes the importance of user portraits and income stratification in e-commerce operations, and reveals the logic behind the profiteering of "serving different customers based on their needs".

“Why do some people think a 200 yuan bag is too expensive, while others place an order for a 20,000 yuan bag in seconds?”

The ultimate secret of e-commerce is not how cheap the goods are, but “ being able to tell who is rich at a glance ”!

1. Why do e-commerce companies “order dishes based on revenue”?

The logic of profiteering from tiered operations

1) Earn “experience fees” from the rich and “traffic fees” from the poor

High-income users: willing to pay 50% more for “saving time, exclusive products, and exclusive services” (data from a luxury e-commerce company);

Low-income users: rely on low-priced hot-selling products to boost sales and use scale to spread costs (the GMV of Pinduoduo's 9.9 free shipping accounts for more than 60%).

2) Avoid “accidental losses”

Negative example: A platform pushed Dyson hair dryers to young people in small towns, but the ROI was only 0.3, resulting in a loss of millions.

Positive case: JD PLUS members’ exclusive discounts increased the repurchase rate of high-income users by 70%.

3) Increase customer lifetime value (LTV)

High-income users: average annual consumption of 80,000, LTV is 10 times that of ordinary users;

Low-income users: attract traffic through the "9.9 trial" and increase profits through subsequent cross-selling.

2. Case Study: Income Stratification

How to let giants “count money while lying down”?

Case 1: The “address metaphysics” of express delivery companies

Parse the user's delivery address → match the housing price of the residential area (e.g. Beijing Wanliu Academy = 150,000/㎡);

For users with high-frequency and high-end addresses, the "insurance + scheduled delivery" service will be automatically upgraded;

Result: The average order value of high-end users increased by 120%, and after-sales costs decreased by 30%!

Case 2: Pinduoduo’s “salary slip harvesting technique”

Reversely estimate revenue using "delivery address province + shopping cart low-priced goods ratio":

Third- and fourth-tier cities + group-buying ratio > 80% → marked as "price-sensitive users";

Targeted push of "price cuts" and "10 billion subsidies" contributed more than 50% to GMV.

Case 3: The “rich lady circle” strategy of a maternal and infant e-commerce company

Filter users who have "bought imported milk powder + received it in a villa area in a first-tier city"; push high-end early childhood education classes priced at 4,999 yuan per class, with a conversion rate of up to 15%;

Profit: Marginal cost is almost 0 and gross profit margin exceeds 80%.

III. Practice: 5 big data sources to accurately understand user income

1. Address information: the most hardcore "wealth code"

Access the real estate platform API (such as Lianjia) to map the address to the average price of the community;

Example of rule:

House price＞100,000/㎡ → Ultra-high net worth users;

House price < 20,000/㎡ → Subsidize sensitive users.

2. Consumption behavior: “class signals” in shopping carts

Brand contempt chain: users who buy LV vs. users who buy Nanjiren;

Decision-making speed: More than 60% of high-income users place orders within 3 hours after adding items to cart.

3. Payment habits: Your payment method betrays you

Characteristics of the rich: full payment by credit card, frequent use of Apple Pay;

Characteristics of workers: the proportion of installment payment and pay-later payment is > 50%.

4. Devices and apps: The model of your phone is your “pay slip”

80% of Huawei Mate 60 RS users have a monthly income of more than 30,000 yuan;

60% of Redmi Note users have a monthly income of less than 8,000 yuan.

5. External data “traceless skinning”

China Unicom operator data: 199 yuan package users vs 29 yuan package users;

WeChat friend list: Friends who sell luxury goods → potential high net worth users.

IV. Revenue Label and Differentiated Operation Strategy

Example of rule:

If the average price of the community is > 100,000 and has purchased luxury goods:

Tagged as "Ultra High Net Worth"

elif 5 orders are placed per month and the average order value is less than RMB 50:

Marked as "price sensitive"

High-income users:

Pushing the “Personal Shopping Guide” service increased the conversion rate by 30%;

Pre-sale codes for scarce products will be distributed on membership day (hunger marketing).

Low-income users:

Pushing "limited time flash sales" at 8 pm has a conversion rate 3 times higher than during the day;

Use the "10 yuan return for orders over 3" hook to increase repeat purchases.

5. Notes

Privacy compliance: Never ask about salary directly! Use "housing price range in the neighborhood" instead of specific address.

“Static label” trap: Workers may change jobs to get a salary increase, and need to update data every month (for example, the delivery address changes from a village in the city to the CBD).

Regional differences: In Shenyang, a monthly income of 10,000 is considered rich, while in Shanghai a monthly income of 30,000 is considered passing. The difference must be calibrated by city!

Conclusion

In the world of e-commerce, the thickness of users’ wallets has never been a secret, but rather a calculable and manipulable data game.