Comparative analysis of Amazon’s empty account detection platform to quickly clean up low-quality customer number resources

More and more Amazon sellers are beginning to discover that with the same batch of customer data, some people can continue to make repeat purchases, while others can never be reached. Many times the problem is not with the product, but with the number itself.

Empty numbers, downtime numbers, duplicate numbers and invalid numbers will continue to consume marketing budgets and also affect the entire user operation system.

Therefore, empty number detection has become an important step before customer data enters the system.

Why Amazon user data is prone to empty numbers

The more user sources there are, the more complex the data quality becomes.

Common sources include:

lStore order data;

lAdvertising investment data;

lSocial platform diversion data;

lHistorical customer database;

lThird-party channel resources.

As time goes by, some numbers will appear:

lUser changes number;

llong-term downtime;

lInvalid registration information;

lrepeated collection;

lThe contact information is invalid.

If not processed in advance, this data will continue to reduce marketing efficiency.

The losses caused by empty numbers are far greater than imagined

Many teams only see send failures.

In fact, the hidden costs are higher.

Decreased advertising effectiveness

Wrong users entering the system will affect advertising model learning.

Reduced customer service efficiency

A large number of invalid customers occupy human resources.

SMS cost increases

Invalid sending will continue to consume the budget.

Distorted user portrait

Wrong data can impact subsequent user analysis.

Therefore, more and more companies are beginning to put empty number detection in front.

What capabilities should a good empty number detection platform have?

A truly valuable platform goes beyond just detecting the presence or absence of a number.

Also need support:

lbatch processing;

lMulti-country code identification;

lEmpty number filtering;

lDuplicate data cleaning;

lActive status analysis;

lData export capabilities.

Only by forming a complete data cleaning link can subsequent operations be more stable.

The differences between several types of platforms on the market

Single function detection tool

advantage:

llower cost;

lEasy to operate.

shortcoming:

lLimited functionality;

lUnable to create user label.

API interface platform

advantage:

lFast processing speed;

lSuitable for system docking.

shortcoming:

lThe technical requirements are high.

Comprehensive data platform

advantage:

lSupport multi-dimensional data analysis;

lUser portraits can be created.

shortcoming:

lMore suitable for long-term operation teams.

For teams that hope to continue to accumulate user assets, the value of a comprehensive platform will be even greater.

How to establish the data cleaning process

Mature teams usually use the following sequence:

first step:

Import historical customer data.

Step two:

Complete empty number detection and duplicate filtering.

Step three:

Identify user platform status.

Step 4:

Classify user tags.

Step 5:

Enter advertising, text messages or private domain operations.

This can prevent a large number of invalid users from entering the subsequent process.

Many teams will establish a one-stop cleaning system through Digital Planet

At present, many cross-border sellers will first use Digital Planet to complete data preprocessing.

In addition to basic empty number detection, it also supports:

lMobile phone number validity identification;

lWhatsApp activation detection;

lTelegram account identification;

lFacebook registration inquiry;

lUser activity index analysis;

lScreening of high-value users;

lDuplicate number filtering;

lBatch data cleaning.

After processing, you can create:

lReachable user pool;

lHighly active user pool;

lconsumer user pool;

lPrivate domain operation user pool.

In this way, whether you conduct SMS marketing, email marketing or community operations in the future, it will be more accurate.

Customer list utilization determines long-term profitability

Many sellers are acquiring new customers every month.

But in fact, what is really worth investing in is improving the utilization of existing customer data.

Because the cost of acquiring customers has been rising, and high-quality users are becoming more and more precious.

Instead of constantly increasing your budget, start by cleaning up your client list.

When there are fewer and fewer invalid data, user assets will become healthier, and the overall operational efficiency will gradually improve.

Many times, improving profits does not necessarily come from more orders, but from less waste of resources.

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