Amazon valid number detection, which numbers among tens of thousands of customer data can still be used

DoFor Amazon-related customer development, once the amount of data reaches tens of thousands, the biggest fear is not that there are few numbers, but that you simply don’t know which ones can still be used. Historical orders, after-sales records, official website price inquiries, event registrations, and old customer information are mixed together. There are duplicate numbers, format errors, and data that has no business records for a long time. If you directly hand over the whole batch to sales, it will become a verification process while contacting you later, and a lot of time will be wasted on sorting out data.

Amazon valid number detection is more suitable before selling. First, tens of thousands of original numbers were sorted in batches, unified format, removed duplications, and obvious exceptions processed, and then classified based on existing customer sources and target communication channels. The final list given to sales may not necessarily be the list with the largest number, but it should be the batch that is easier to continue processing.

There are tens of thousands of numbers, so don’t rush to check them one by one.

If you only have a few dozen pieces of data, manual inspection can still handle it.

butThere are 50,000, 100,000 or even more numbers, and it is basically meaningless to manually look at them one by one.

The first thing to do in this case should be batch sorting.

First put all the numbers into a unified data structure, at least separate the fields of country, mobile phone number, customer source, and data time.

If the data formats saved by different departments are different, they should be merged into a unified template first.

For example, the order system only has a mobile phone number and order date, the customer service table has a mobile phone number and after-sales records, and the sales table has product requirements and notes.

After merging this information, start the number detection.

Otherwise, even if the number status is found later, it will be difficult to correspond to the specific customer.

The first batch processes the number format first

large batchThe most common problem with Amazon numbers is that the format is not uniform.

Some numbers have the full international dialing code.

Some only save local numbers.

Some have spaces, brackets, and dashes in between.

There are also some customers whose country is written as the United States, but the number field does not match the country information.

These issues are best addressed in batches first.

You can unify the international number format according to the country field, filter out content that is obviously not a phone number, and then check whether the country code is reasonable.

In this round, there is no need to judge whether the customer is accurate or whether there is any purchase intention.

Only one problem needs to be solved: whether the number format can be processed in the next step.

There is no need to hand over data to sales first if the basic format is not correct.

The second batch will be deduplicated and checked together with the historical customer database.

a lot ofThere doesn't seem to be much duplication within the Amazon customer list, but when compared with the historical database, the amount of duplication may be high.

For example, this new import50,000 numbers.

What remains after deduplication within the current file46,000 items.

Comparing it with the customer base of the past two years, there may be more12,000 of them have appeared before.

The only newly added data is actually34,000.

If we don’t do historical deduplication, this will12,000 items are likely to re-enter the sales process, resulting in repeated contacts.

soIt is best to do the Amazon number deduplication twice.

Check the current batch again.

Check the historical database again.

Don’t simply delete duplicate numbers, you can merge customer information.

If a customer had an order before and inquires again this time, it is a return of old customers that deserves attention.

What should really be reduced is duplicate records, not deleting customer history altogether.

In the third batch, obviously abnormal data will be released first.

After formatting and deduplication, there will still be some data left that obviously needs to be confirmed.

For example, the number of digits in the number is abnormal;

Country and number information conflicts;

Contact information field is missing;

There is no source record for a long time;

The customer has made it clear that he/she does not wish to continue to be contacted;

The data content in the same record is obviously misplaced.

Such numbers can be put into the to-be-checked list first.

Don’t let the salesperson make his or her own judgment after getting it.

What sales should really do is communicate with customers, not find errors in data sheets every day.

The greatest significance of mass testing is to handle this basic work in advance.

For the fourth batch, let’s see what channels we plan to contact through in the future.

After the Amazon customer numbers themselves are sorted out, we still need to consider how to prepare for sales follow-up.

If the main passTo maintain overseas customers, WhatsApp needs to know which of the existing numbers have the basis of a WhatsApp account.

If certain markets are more commonly usedYou can also continue to filter based on business needs through Telegram, LINE or other channels.

Digital Planet can be placed in this ring.

The company already has a clear sourceAfter Amazon related customer numbers, you can use Digital Planet to deduplicate numbers, organize countries, and filter the status of target social platforms.

For example, a batch of cleaned customers can be further divided into:

USAAmazon historical customer + WhatsApp has been activated

FranceAmazon inquiry customers + WhatsApp has been activated

JapanAmazon after-sales customers + LINE related users

German historical order customers +Telegram related users

In this way, after sales get the list, they will at least know which platform is more suitable for these customers to continue communicating with.

Digital Planet solves the problem of organizing existing numbers and communication channels. It does not mean that after detecting a certain platform account, this person must beAmazon active buyer.

Valid numbers do not equal high-value customers

This isThe most important thing to distinguish during Amazon number detection.

If a number has a normal format, no duplication, and has the target social platform account base, it can only mean that this contact method is more suitable for continued processing.

It does not mean that the customer must have been recentlyAmazon shopping.

It also does not mean that customers have high spending power.

It’s also not possible to judge the specific product purchased based on the mobile phone number alone.

What really determines customer value is the company's own business records.

For example, there has been an order recently;

Recent re-inquiries;

Just inquired about product specifications;

Just handled the after-sales service;

Have purchased similar products before.

This information determines who sales should contact first.

Therefore, valid number detection addresses data availability, and customer grading addresses sales priority.

The fifth step is to merge the test results back into the customer information

After many people's numbers are screened, they will get a new list separately.

It only contains mobile phone numbers and test results.

This is actually not useful.

It makes more sense to merge the results back into the original customer database.

For example, the original piece of data is:

American customers

Historical orders in 2025

BuyA product

No interaction in the past six months

Add after detection:

The number format is normal

WhatsApp has been activated

Current follow-up status: pending maintenance

Another one:

French customers

Official website inquiry 7 days ago

focus onB product

WhatsApp has been activated

Current follow-up status: priority contact

Both numbers are data that can continue to be processed, but the sales order is completely different.

Only when the number status and business records are put together can the detection results be truly useful.

Tens of thousands of pieces of data can be directly divided into three batches

If you don’t want to make the process too complicated, you can just divide it into three batches in the end.

The first batch is given priority to enter sales.

The number format is normal, the source is clear, there is no duplication, and there are recent orders, inquiries or clear business interactions.

The second batch can be kept.

There is no obvious problem with the number itself, but the customer has been around for a long time, has unclear needs, or has no new actions recently.

This type of data can be placed in a long-term maintenance pool.

The third batch is not yet available for sale.

The format is obviously abnormal, the duplication is serious, the source is completely unclear, or the customer has clearly refused to continue contact.

Such an original data table with hundreds of thousands of entries will eventually turn into several lists with different purposes.

Sales no longer need to start from the first line and try each item one by one.

Amazon historical customers especially look at the last business time

Just because a number was valid five years ago doesn't mean it's still worthy of priority contact today.

soAfter Amazon number detection, it is best to add a layer of time sorting.

RecentlyIf you have orders, inquiries or after-sales within 30 days, you can put them in the front.

closeIf there are business records within 3 months, normal follow-up will begin.

If there are no new actions for half a year to a year, the priority can be lowered.

Long-term silent clients are kept separately.

If an old customer later inquires again, put him back on the list of high priorities.

In this way, the customer database is not a one-time list, but will be continuously updated as the business changes.

The number is valid, but don’t rush to market if the source is unclear.

After some data has been tested, the number format is normal and the target platform also has a corresponding account, but the source field is not recorded at all.

In this case, it is not recommended to directly enter promotion just because the technical status is normal.

Because the number could be processed, I only answered the question of whether I could be contacted.

It did not answer why it was contacted.

Really usefulAmazon customer list, it is best to explain that the data comes from historical orders, after-sales, official website consultation, user active forms, or other normal business relationships.

Only after the source is clear can the first sentence of sales be well-founded.

Otherwise a stringIt is still difficult for a "valid number" to become a valid customer.

Large-volume inspection is ultimately to reduce sales workload

Assume that initially there is100,000 Amazon related numbers.

What remains after the unified format95,000.

Remaining after deduplication of current batch and historical database78,000.

After handling the obvious exception, the remaining70,000.

Then according to the target communication channels and customer sources, in the end, only the ones who really give priority to sales may be30,000.

from100,000 has become 30,000. It seems that there is a lot less data.

But what’s really missing from sales is70,000 pieces of data that require repeated verification, manual judgment, or are not worthy of priority processing for the time being.

This is the most direct value of batch number detection.

Amazon valid number detection is best turned into a fixed process

Every time you import new customer data in the future, you can follow the same sequence.

First unify the number format.

Then perform deduplication with the current batch and historical database.

Handle obvious exceptions.

Organize target social platform status through Digital Planet.

Re-consolidate orders, inquiries, after-sales and customer sources.

Finally, prioritize sales according to recent business conditions.

In this way, the client database will become cleaner with more use, instead of having to be refreshed every few months.

Amazon valid number detection is not simply to determine whether there is a problem with a mobile phone number, but to step by step narrow down tens of thousands of raw customer data into a list that can actually continue to be processed. By sorting in batches first, then screening communication channels, and finally sorting order and inquiry records, sales can leave time to people who are more worthy of follow-up.

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