E-commerce consumer user analysis makes it easier to find customers who are really willing to place orders.

The background also showsWith 100,000 pieces of user data, two cross-border stores handed over completely different report cards.

One store continued to increase its advertising budget, and the number of clicks was getting higher and higher, but the transaction rate never improved significantly; another store did not increase advertising significantly, but it was able to continue to increase the repurchase rate, and old customers continued to bring new orders. Many people's first reaction is to think that the products are different and the operating capabilities are different. In fact, the biggest difference between the two is often whether they can truly understand the user data.

More and more operation teams are beginning to realize that e-commerce competition has shifted from traffic competition to data competition. In the past, the competition was about who could get more customers; now, the competition is about who can find the people who are really willing to consume faster. If there are a large number of low-value users mixed in the database, it will be difficult to achieve ideal conversion results no matter how high the advertising budget is.

It is against this background that e-commerce consumer user analysis has become an increasingly important task. It does not simply count the number of orders, but helps operators find target customers who are more worthy of investing resources through multiple dimensions such as user behavior, spending power, and activity level.

Why does more and more data not bring more orders?

Many stores have a common experience: a large amount of user data is added every day, and the number of customers in the background continues to grow, but the actual transactions do not increase simultaneously.

The reason is not complicated, because data is only a resource, not a result.

If a group of users have only browsed the products and never made a purchase; or have placed an order but have not consumed it again for a long time, then although the data exists, it may not be able to continue to create value.

There are also situations that are easier to overlook. For example, of two customers who have purchased the same product, one belongs to a long-term stable consumer group, and the other only completed the order because of a promotion. If subsequent marketing strategies are exactly the same, it is likely to result in a waste of marketing resources.

Really effective data analysis is not to put all customers together, but to find those who really have consumption potential.

What exactly does e-commerce consumer user analysis analyze?

When many people come into contact with consumer user analysis for the first time, they think it is a very complicated concept. In fact, it focuses on several very practical issues.

The first is whether the user has the spending power.

Different users have different budgets and consumption habits for purchasing goods. If different consumption levels can be identified in advance, marketing strategies can be formulated that are more in line with user needs.

Second, is whether the user remains active.

Some users browse dozens of products within a year and frequently participate in activities; some users never log in to the platform again after completing a purchase. These two types of users obviously cannot operate in the same way.

The third is the user’s interest direction.

Through historical browsing, purchase categories, and interactions, you can learn which products users pay more attention to, thereby improving recommendation accuracy.

Fourth, it is the user’s future consumption potential.

Rather than focusing only on customers who have already completed transactions, more and more operations teams are beginning to pay attention to potential consumers, hoping to discover in advance those people who are more likely to continue to purchase in the future.

Only when these analysis results are combined can a valuable user portrait be truly formed.

Why are more and more teams starting to create user tags?

In the past, many operations personnel liked to manage all customers in a unified manner.

Later, it was discovered that the same marketing text message was sent to everyone, and the effect was getting worse and worse; the same promotion was pushed to all customers, but fewer and fewer people actually participated.

The reason is that different users have completely different needs.

Therefore, more and more teams are starting to build user tags.

For example:

Classified according to spending power;

Classified by purchase frequency;

Classified according to activity level;

Classify according to interest tags;

Categorized by region and platform.

The biggest advantage of doing this is that subsequent marketing will be more precise.

For example, high-frequency purchasing users can receive priority new product recommendations; users who have not spent a long time can be awakened by discounts; and high-spending users can be provided with more personalized services.

The richer the user tags, the higher the efficiency of subsequent operations.

How does Digital Planet help complete consumer user analysis?

Many teams know how to do user analysis, but they don’t know how to quickly organize huge data.

Especially cross-border e-commerce and overseas marketing teams add a large amount of user information every day. If you rely on manual sorting, it will not only be inefficient, but also difficult to ensure that the data is continuously updated.

Digital Planet can help operators organize and filter user data more systematically.

For example, after importing a batch of user numbers, the data can be classified based on different dimensions to help the team quickly create user labels instead of facing a messy list.Excel tables are processed item by item.

For teams that need to conduct e-commerce consumer user analysis, Digital Planet can help complete multiple key links.

Number resources can be managed uniformly to make data from different sources more standardized; different customer groups can be established based on filtering conditions to facilitate subsequent precision marketing; the database can also be continuously optimized to reduce duplicate data and low-value data, allowing operators to focus more on customers with real potential.

For cross-border sellers, when preparing to promote new products, they can first use Digital Planet to organize existing user data, and then create different marketing lists based on consumption tags; for teams that operate private domain customers for a long time, they can also use Digital Planet to continuously maintain customer databases to make user portraits more and more complete.

Really valuable data is not just saved, but can be quickly found, quickly analyzed, and quickly applied.

In three common scenarios, what role can consumer user analysis play?

The first scene is new product promotion.

When a new product is launched, not all customers are suitable as the first batch of promotion targets. If you can first find users with higher spending power and higher purchasing frequency, the promotion effect will usually be more ideal.

The second scenario is repurchase by old customers.

Many stores focus all their efforts on acquiring new customers, but ignore old customers who have already purchased products. Through consumption analysis, you can find people who are more likely to buy again and increase the repurchase rate.

The third scenario is overseas market expansion.

Facing users from different countries and regions, it is no longer enough to just know where the customers are from. Spending power, purchasing preferences, and user activity can all be used as important references for market analysis.

These scenarios may seem different, but they are all inseparable from high-quality data analysis.

When doing consumer user analysis, many people make these mistakes

The first mistake is to only look at the order amount.

A high order amount does not mean a high long-term customer value. Some users make a large one-time purchase, but never make a repeat purchase; some users, although their single purchase is not high, can continue to purchase for many years.

The second mistake is to only focus on new customers.

Developing new customers is important, but maintaining old customers can often lead to more stable profits.

The third mistake is that the data is not updated for a long time.

User behavior will continue to change. Customers who were active last year may have stopped purchasing this year; users who were ordinary consumers in the past may also become key customers due to changes in demand. If the database is not maintained for a long time, no matter how accurate the analysis model is, it will gradually lose value.

The fourth mistake is to ignore the correlation between data.

Many teams store order data, user data and marketing data separately without forming unified management, resulting in a lot of valuable information being unable to play a role.

The focus of consumer user analysis is not analysis, but action

Many operations teams spend a lot of time producing various data reports, but rarely use the analysis results to optimize marketing strategies.

In fact, the ultimate goal of consumer user analysis is not to obtain beautiful data, but to help operators quickly find people worthy of investing resources, so that marketing budgets can exert greater value.

As cross-border e-commerce competition becomes increasingly fierce, whoever can establish a complete user analysis system earlier will be more likely to take advantage in subsequent operations.

If you want to complete e-commerce consumer user analysis more efficiently, you may wish to use Digital Planet. Digital Planet supports multiple functions such as number data sorting, user tag management, customer classification screening, active user identification, etc., which can help the operation team quickly build a clearer user portrait, so that originally scattered data can truly be transformed into marketing assets. Whether it is new product promotion, old customer operations, or overseas market expansion, Digital Planet can provide more stable and efficient support for subsequent data analysis and precision marketing.

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